CIDD: Collaborative Intelligence for Structure-Based Drug Design Empowered by LLMs
Authors: Bowen Gao
Yanwen Huang
Yiqiao Liu
Wenxuan Xie
Bowei He
Haichuan Tan
Wei-Ying Ma
Ya-Qin Zhang
Yanyan Lan
Venue: NeurIPS 2025 poster ()
Keywords: Large Language Model
Drug Design
Primary area: machine_learning_for_sciences

Abstract: Structure-guided molecular generation is pivotal in early-stage drug discovery, enabling the design of compounds tailored to specific protein targets. However, despite recent advances in 3D generative modeling, particularly in improving docking scores, these methods often produce rare and intrinsically irrational molecular structures that deviate from drug-like chemical space. To quantify this issue, we propose a novel metric, the Molecule Reasonable Ratio (MRR), which measures structural rationality and reveals a critical gap between existing models and real-world approved drugs. To address this, we introduce the Collaborative Intelligence Drug Design (CIDD) framework, the first approach to unify the 3D interaction modeling capabilities of generative models with the general knowledge and reasoning power of large language models (LLMs). By leveraging LLM-based Chain-of-Thought reasoning, CIDD generates molecules that not only bind effectively to protein pockets but also exhibit strong structural drug-likeness, rationality, and synthetic accessibility. On the CrossDocked2020 benchmark, CIDD consistently improves drug-likeness metrics, including QED, SA, and MRR, across different base generative models, while maintaining competitive binding affinity. Notably, it raises the combined success rate (balancing drug-likeness and binding) from 15.72% to 34.59%, more than doubling previous results. These findings demonstrate the value of integrating knowledge reasoning with geometric generation to advance AI-driven drug design.

CIDD: Collaborative Intelligence for Structure-Based Drug Design Empowered by LLMs
Bowen Gao Yanwen Huang"*, Yiqiao Liu', Wenxuan Xie*, Bowei He², Haichuan Tan', Wei- Vei-Ying Ma', Ya-Qin Zhang'. Yanyan Lan 6677
'Inttitute Al Industry Research (AIR), Tsinghua University, Beijing. China
Department of Computer Science and Technology, Tsinghua University, Beijing, China DDepartment of Pharmaceutical Science, Peking University, Beijing, China Sccooo of Future Technology. South China University of Technology. Guangzhou, China DDpaatmeet of Computer Science, City University of Hong Kong, Hong Kong SAR, China Beijing Fronticr Research Center for Biological Structure, Tsinghua University, Boijing, China "Beijing Academy of Artificial Intelligence (BAAI), Beijing, China
Abstract
Structure-guided molecular generation pivotal in early-stage drug discovery, enabling the design of compounds tailored specific protein targets. However, despite recent advances in 3D generative modeling, particularly in improving dock- ing scores, these methods often produce uncommon and intrinsically unreasonable molecular structures that deviate from drug-like chemical space. To quantify this issue, we propose novel metric, the Molecule Reasonable Ratio (MRR), which measures structural rationality and reveals a critical gap between existing models and real-world approved drugs. To address this, we introduce the Collaborative Intelligence Drug Design (CIDD) framework, the first approach to unify the 3D interaction modeling capabilities of generative models with the general knowledge and reasoning power of large language models (LLMs). By leveraging LLM- based Chain-of-Thought reasoning, CIDD generates molecules that are not only compatible with protein pockets but also exhibit favorable drug- drug-likence likeness, struc- tural rationality, and synthetic accessibility. On the CrossDocked2020 benchmark, CIDD consistently improves drug- drug-likeness metrics, including QED, SA, and MRR, across different base generative models, while maintaining competitive binding affinity. Notably, raises the combined success rate (balancing drug- likeness and binding) from 15.72% to 34.59%, more than doubling previous results. These findings demonstrate the value of integrating knowledge reasoning with geometric generation to advance Al-driven drug design
Introduction
Structure-basse drug design (SBDD) enables the direct generation of compounds tailored to protein binding sites, making a powerful tool in drug discovery. Recent 3D generative models-including autoregressive approaches like AR 24] and Pocker2Mol [25] and ceffusion-based methods such as TargetDiff [1]. DecompDiff [3], and MoICRAFT 26 have advanced rational molecule design. However, expert evaluation often reveals chemically unreasonable features, such as overly fused polycyclic systems and partially unsaturated rings (Figure T)). While these distortions may improve
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39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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docking scores via geometric complementarity. they often compromise intramolecular stability and result poor pharmacokinetic properties.
MRS Comperison
FDA
97.9%
92%
Melecue Reaporsble Ratoo
72%
67.4%
66.7%
52%
47%
37.3%
Stucture uncomeentionll systams
unmosarabie
Correcting
35%
Reckebo0dd
Brgeche
Deconptie
MUCCAOFT
(a)
Figure 1: (a) Common errors in 3D-SBDD outputs. Minor structural changes can cause large deviations in 3D conformation, highlighting the challenge of correcting chemically uncommon structures without disrupting valid 3D shapes. (b) MRR comparison. While FDA drugs reach 85.9% MRR, existing 3D models lag behind. LLM achieves 97.5 97.5%, and CIDD closes the gap with 81.7%.
While current models effectively generate molecules that fit target pockets, they often overlook the physicochemical and pharmacological features common to clinically approved drugs. These limitations stem from fundamental mismatch: the narrow focus on binding-site compatibility often which depend solely on the chemical structure itself and often conflict with geometric fit. To quantify the gap, propose the Molecular Reasonability Ratio (MRR), which measures the proportion of generated molecules that are structurally valid and chemically reasonable relative to drug-like standards. As shown in Figure [b. based on our experiments, existing 3D generative models perform poorly under this metric (e.g.. TirgetDiff: 37.8%, MoICRAFT: 58.5%), falling well below the 85.9% observed in apA-approved drugs. Some recent methods, such as TAGMol, build on TirfetDiff by incorporating gradient-based guidance jointly optimize for drug-like likeness properties like QED [3) While this leads to improvements in QED scores, it does not result in a general enhancement of drug potential, evidenced by TAGMol's 's similarly low MRR. In contrast, LLMs achieve high MRRs on SBDD tasks (e.g., GPT-4: 97.5% thanks to their general chemical and pharmaceutical knowledge, but they struggle to generate molecules with high binding affinity due to limited spatial reasoning and the inherent trade-off between potency and drug-likee likeness.
comes at the expense of structural reasonability and drug-likenese
To bridge this gap, we propose CIDD-a Collaborative Intelligence for Drug Design framework that combines the interaction modeling capabilities of 3D generative models with the domain knowledge and instruction following strengths of LLMs. Rather than directly producing final drug candidates, 3D models generate interaction- focused molecular proposals that capture key binding features. These serve as structured input for an LLM-driven design process that translates spatial intent into chemically viable drug-like molecules. This division of roles allows CIDD to generate compounds that are both interaction-comee competent and structurally plausible. CIDD decomposes the generation task into series of specialized modules, each powered by the LLM. The Interaction Analysis Module identifies fragment-level interactions; the Design Module proposes chemically informed modifications; the Reflection Module provides analysis; and the Selection Module ranks candidates by interaction quality and chemical coherence. CIDD follows structured Chain-of-Thought (CoT) prompting strategy guided by domain knowledge guided prompts, enabling interpretable, stepwise molecular design. This modular, CoT-guided architecture mirrors real-world medicinal chemistry workflows and enables the generation of pharmaceutically relevant, structurally sound compounds.
Evaluated on the CrossDocked2020 dataset [9, CIDD significantly outperforms -fthh-artt baselines, boosting the overall success ratio from 15.72% to 34.59%. It consistently improves key drug-likenee metrics-inchut
Quantitative Estimation Drug-likens (QED) B. synthetic
accessibility (SA), MRR, and the proportion of molecules meeting QikProp 300 thresholds across diverse baseline models. Our key contributions are:
(1) Identifying a core limitation in current SBDD models: We introduce the MRR to assess intrinsic structural rationality, revealing that pocket-based 3D generative models often produce

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intrinsically irrational and rare molecules that deviate from drug-like like chemical space despite favorable docking scores.
(2) A unified framework for rational molecule generation: We propose CIDD, the first framework to combine the spatial modeling of 3D generative models with the chemical reasoning capabilities of LLMs. By leveraging their complementary strengths, CIDD uses Mhain-of-TThughhe guidance to overcome the common trade-off between binding affinity and drug-likeness.
(3) Validated performance and broader implications: CIDD achieves state-of-the-art results on CrossDocked2020, significantly improving drug-likente likeness metrics (QED, SA, MRR) while preserving binding affinity. This demonstrates its effectiveness and highlights the advantage of combining LLMs with geometric modeling for AI-driven drug design.
2 Preliminaries
2.1
Structure-Based Drug Design
The goal of SBDD is to generate molecule that binds to a given protein pocket P. Recent work has explored a variety of deep generative approaches. LiGAN 27 uses a conditional variational autoencoder (CVAE) to generate 3D point clouds of ligands conditioned on pocket geometry. Autore- gressive models such as AR (24] and Pocket2Mol [25] sequentially construct molecules in 3D space. Diffusion-bert -baeio methods like TirgetDiff 11). IPDiff [15), and DecompDiff [13 iteratively refine 3D structures or molecular graphs, while TAGMol 7 further applies gradient- based optimization during generation. Other approaches include the fragment-based DrugGPS [37] and MolCRAFT [26), Bayesian flow model that learns the distribution of drug-like molecules conditioned on pocket struc- tures. All these methods generate intermediate 3D point clouds graphs, which subsequently mapped into chemically valid molecules as the final output.
2.2 Large Language Models
LLMs are advanced neural networks trained on vast corpora of textual data, enabling them to understand, generate, and reason with human-like language. Notable examples include GPT-4 (2) LLaMA (33), ChatGLM (T]] and DeepSeek (21]. which have demonstrated impressive capabilities in tasks such as natural language understanding, code generation, mathematical problem solving, and logical reasoning. Their versatility and ability to capture complex patterns have made them increasingly relevant in domains beyond traditional natural language processing, including drug discovery 4 Recent work has explored the use of LLMs to generate or modify molecules with desired properties. For example, ChatDrug [22] employs conversational LLMs for generating and editing molecules with desired properties. However, the application of LLMs to pocket-based molecular design remains largely unexplored. This is primarily due to the challenge of capturing essential three-dimensional structural information, which is critical for modeling protein-ligand interactions. Unlike textual or sequence-basee data, protein binding pockets and small molecules interact through spatially complex, non-linear, and chemically rich environments that cannot be directly encoded in natural language.
3 Methods
3.1 Evaluating the Gap Between Generated Molecules and Real Drugs
Drug-likeness is an inherently multidimensional attribute that dictates the probability p(drug) that molecule will reach its biological target-an an assessment that precedes, and is distinct from, estimating binding affinity. Widely used machine-learnin
surrogates, most notably QED (3] and Lipinski's Rule of Five 200, are inadequate for two principal reasons: (i) Legacy bias: These metries were calibrated on historical chemical space and fail to recognize the unconventional scaffolds produced by modern generative models. Consequently, structurally implausible or pharmacologically irrelevant molecules can still achieve high "drug-like" scores. (ii) Oversimplification: QED relies on only seven physicochemical descriptors, and its average value for DrugBank 17 molecules below 0.5, illustrating its limited discriminative power
3

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To close this gap, we introduce broader evaluation framework comprising two complementary tiers. Structural reasonability screens for fundamental medicinal-chemiste principles such as aromaticity, ring-system stability, and hybridization consistency, while physicochemical and pharmacokinetie suitability evaluates absorption, distribution, metabolism, excretion, and toxicity (ADME/T) prop- erties using established descriptors. Together, these tiers ensure that generated molecules are both chemically plausible and therapeutically relevant, providing a more reliable benchmark for modern Al-driven molecular design.
Molecular Reasonability Ratio. To address the lack of metrics capable of identifying the structural gap between generated molecules and rationally designed drugs, we introduce MRR as targeted solution. MRR is a rule-based, empirically motivated metric designed to flag structures that are uncommon medicinal chemistry practice. Its design is informed by expert analyses of SBDD outputs, which consistently reveal a predominant failure mode: the improper use of ring conjuga- tion- part-ccuallllrly aromaticity that often deviates from patterns observed in clinically validated drugs.
MRR operates by analyzing the hybridization states of atoms within molecule. It sequentially excludes fully aromatic or fully saturated rings and focuses on the remaining atoms within each ring system. After removing peripheral substituents, if the hybridization states of the residual ring atoms are neither all sp² nor all sp?, the structure is classified as chemically unreasonable. For example, structures such as cyclohexene and cyclohexa-1.3-diene are flagged, as they likely result from the model's 's failure to correctly generate either fully aromatic or fully saturated systems. Although not intended as an exhaustive classifier, MRR provides an interpretable, screen that complements basic valence checks and ADME filters, thereby reducing the incidence of structurally implausible candidates generated by models. The full algorithm for MRR shown in Appendix D
QikProp Multiple Property Requirements. To further evaluate the physicochemical and pharma-
cokinetic properties of the generated molecules, employ QikProp [30]. tool recognized for its robust performance in predicting molecular drug-likeness properties [16]. The assessed properties
include aqueous solubility, lipophilicity, polar surface area (PSA), the number of metabolizable sites, and oral absorption. Detailed requirements for each property are provided in Appendix E
A molecule is considered to have passed the evaluation if it satisfies all N predefined property requirements: P1, , Pv. If any of the properties fall outside the acceptable range, the molecule classified failing.
QikProp = {
PPAPPA- A Pv are satisfied,
otherwise.
3.2 Bridging the Gap with CIDD framework
We propose the Collaborative Intelligence Drug Design (CIDD) framework (Figure a modular system for target-specific molecule generation that combines 3D interaction modeling with LLM- enhanced molecular design. It consists of two components: the Structure-Based Interaction Generator (SBIG) and the LLM-Enhanced Drug Designer (LEDD).
SBIG uses 3D generative models produce interaction-orienter molecular pernccuure--iitermeddie that fit the protein pocket but might be chemically incomplete.
LEDD then refines these proposals into like molecules using LLMs. The whole process is formalized as:
Fg = (Targerrrer).
x LEDD(E0. Target)
Here, zy encodes the intended spatial interactions, and LEDD completes it into a chemically valid, syn- thetically accessible compound z. A key limitation of prior approaches is the absence of ground- -trth mappings from interaction scaffolds to drug-like molecules, making supervised training infeasible. Traditional generative models models-based on direct optimization or limited heuristics heuristics-struggle to bridge this gap. In contrast, LEDD leverages the implicit drug-relevant knowledge embedded in large- scale pretrained LLMs. With strong instruction-follont following, reasoning, and coordination abilities.

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CIDD Framework
Structure-Baan Baserd Interaction Generator (SBIG)
LLM-Enhanced Drug Designer (LEDD)
Interaction Analysis
CIDD output
Generative Model
molecules
Selection
input
Designed molecules
Dasign
Reflection
Generation
Fragmentation
Design the malecule with reesoning
Step
Interaction- Crifical Fragment
identificatio
ICI Arnotation
Design
Step
Unfavorable Atypical Substructures
QED SA MRR
Analyze Interaction based on fragmentation and NCI
Refect previcus design, evaluste design success.
Step
Strategic Deeign Planning
Generate Malecule based Design
Analysis
Reflection
Step
Figure 2: Overview of the CIDD framework. Top: end-to-end pipeline integrating SBIG and LEDD. Bottom: detailed view of LEDD's LLMM-based design process, which designs and generates molecules through interaction analysis, reflection, and Chain-of- Shaii-of-Thoughh-driieee reasoning.
LLM enables design decisions that generate molecules without incurring the typical trade-offs between potency and drug-likeness in drug design.
The CIDD framework is inspired by the real-world workflow of medicinal chemists, who begin by identifying key interaction patterns between a ligand and its target, and then construct molecules that retain these interactions while satisfying broader drug development constraints. CIDD for- malizes this reasoning process: SBIG captures spatial interaction intent, while LEDD powered by LLMs assumes the role of virtual chemist to transform raw scaffolds into viable drug-like like molecules. By leveraging the LLM's instruction-followee and general knowledge, CIDD effectively substitutes human expertise in the molecule design process.
3.2.1 Pipeline Overview
CIDD decomposes the complex task of drug-oriented molecule generation into structured pipeline of modular reasoning steps. It begins with SBIG, which generates interaction-focusel intermediate structures conditioned on the target pocket. These intermediates Analysis Module, which extracts fragment-level binding profiles using docking, fragmentation, and rule-based NCI detection. The Design Module then interprets these profiles generate chemically plausible molecules that maintain critical interaction motifs. Each design is re-evaluated, and its updated interaction profile processed by the Reflection Module, which compares successive design states and produces feedback for new designs. After generating N new candidates, the Selection Module ranks all candidates based on their interaction quality and chemical viability, selecting the final molecule for downstream use. This modular pipeline mirrors expert workflows and enables interpretable, multi-stage control over both structural interaction and chemical relevance.
passed to the Interaction
3.2.2 Interaction Analysis Module
This module evaluates the molecular interaction between candidate molecule z; and a target protein pocket P. The process begins by re-docking z; into the binding site of P to generate protein-ligand complex conformation. The molecule z; is then decomposed into chemically meaningful fragments via the BRICS algorithm
6 enabling fragment-Ieve attribution of interaction features.
Next, non-covalent interactions (NCIs), such as hydrogen bonding, т-ー stacking, salt bridges, and hydrophobic contacts, are identified from the docked complex based on geometric and chemical features. The resulting interaction map-capturrng both atomic contacts and fragment-level organiza- tion-is then analyzed by an ineraacionnsspccialieed large language model, I.LM,, Conditioned on spatial interaction cues and chemical context, LLM, synthesizes this information into structured
5

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interaction profile 1,, which semantically links molecular fragments to their respective roles in target engagement. Formally, the process can be represented as:
LL,, P)
The resulting profile 4 provides fragment-level interpretation of binding contributions and serves as the basis for chemically informed design in subsequent modules.
3.2.3 Design Module
Given a raw molecule Tn, its interaction report To, and prior feedback from the reflection module R = (R Rg,..., ......., ). the Design Module leverages an LLM with a Design role (LLMp) to produce refinement plan Dj and an updated molecule x;.
LLMp(2o, Lo R) (Di,
Here, Dj is a structured and interpretable set of design decisions grounded medicinal chemistry, and 2; is the generated compound for further evaluation.
To emulate real-world drug design workflows, we implement a Chain-of-T -fhohhh prompting strategy that guides the LLM through Homain-informee reasoning pipeline with four sequential stages. (1) Interaction- Ciaractio-CCriticall Fragment Identification: the model analyzes zo and highlights fragments forming key non-covalent interactions with the target, based on Lo. (2) Detection of Unfavorable or Atypical Substructures: chemically undesirable Tragments-sute as synthetically inaccessible motif's strained rings, poor physicochemical regions-arr flagged for replacement. (3) Strategic Design Planning: the model proposes modifications that improve chemical viability while preserving interaction and topology, often substituting problematic regions with pharmaceutically preferred alternatives. (4) Candidate Molecule Generation: a new structure æ; is generated based on the design plan, ensuring both target interaction and drug-like like properties.
By encoding expert priors into the prompt structure, the Design Module reframes molecule generation as chemically grounded reasoning task. The LLM is able to coordinate multiple design objectives within a unified framework. This enables the CIDD to effectively mitigate the trade-off's between affinity and drug-likenn lingelikeness that commonly arise in drug design. It bridges raw interaction scaffolds to viable candidate molecules in a transparent and interpretable manner.
3.2.4 Reflection and Selection Modules
The Reflection Module evaluates whether each candidate design x; achieves design goals by compar- ing it to the initial raw molecule Ty and their respective interaction profiles:
L.M To, Dj, Ni, It) -> R.
The Selection Module evaluate all the designed molecules { , END and their interaction reports to identify the most promising candidate:
LLMs (1(21, 4...... (N))) + Beett
Here, Phest balances interaction quality and chemical viability. Full prompt examples for each module are in Appendix C
4 Experiments
4.1 Experiment Settings
We follow prior 3D-SBDD settings and use the CrossDocked2020 dataset (9. adopting the same train/test split as TargetDiff [12], resulting in 100 protein pockets for test.
Dataset.
Metrics. We evaluate models using standard metrics: Vina docking score 34 for binding affinity, QED 3 for likenls,, SA score [ for synthetic accessibility, and molecular diversity (computed
as - ECFP4 similarity (29] for structural variety. As we do not directly generate 3D conformations,

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Vina Score/Min is excluded. Beyond conventional metrics, we emphasize the evaluation of drug potential via two additional metrics: MRR (Molecular Rule-based Reasonabilityy, which captures domain-inforner structural plausibility, and QikProp pass ratio, which assesses a wide range of physicochemical and pharmacokinetie properties. Together with QED and SA, these offer a more holistic assessment of molecular viability. We also report the success ratio, defined as the percentage of molecules that satisfy all the following criteria: Vina < 8.188 QED > 0.25, and SA > 0.59, following [23]. In addition, we introduce molecular reasonability an extra constraint for defining success. We also evaluate the proportion of molecules that pass all QikProp filters.
Baseline Models. We compare CIDD against diverse set of 3D-SBDD baselines spanning mul- tiple generative paradigms: the VAE-based LiGAN [27]; autoregressive models AR [24 and Pocket2Mol [25;; and diffusion-
approaches including TargetDiff [22, IPDiff [15] De-
compDiff 13], and TAGMol 77. IPDiff uses ineracttio-guidern sampling, while DecompDiff leverages interaction-aware priors; TAGMol adds gradient- based optimization for drug-likeness. We also evaluate DrugGPS (3,, a fragment-base bageee method, and MolCRAFT 26]. a Bayesian flow
network shown to be state-of-the-an in recent benchmarks 199 [0].
CIDD Settings. We use MoICRAFT in the SBIG step. All modules in the LEDD step are powered by GPT-4o. The Design Module generates 5 candidates per round, and the Selection Module selects the final molecule. For each protein pocket, we generate 10 molecules. All SBIG models are trained on CrossDocked2020 with their released weights.
4.2 General Results
As shown in Table T CIDD demonstrates strong overall performance across key drug-likeness metrics- including QED, MRR, SA, and QikProp pass ratio- as well as favorable binding affinity. In comparison, IPDiff, which leverages trained binding affinity predictor to guide both training and sampling, achieves some improvement in docking scores over TargetDiff. However, it performs worse in terms of MRR, highlighting the potential limitations of focusing solely on binding affinity. Similarly, the gradient-guider method TAGMol aims to enhance multiple properties through predictor guidance during sampling. While TAGMol shows notable improvements in QED, it brings minimal gains and still performs badly in metries such as MRR and SA, performing comparably to the unguided diffusion model TargetDiff. This suggests that optimization-drive approaches like TAGMol may overfit individual scoring functions rather than learning to generate molecules with generally improved drug-like profiles. In contrast, CIDD delivers consistent improvements across all major drug-likeness dimensions, indicating more robust and comprehensive molecular design capability. These results stem from CIDD's ability to integrate the complementary strengths of distinct modeling
Table 1: Test Results on CrossDocked20200. We benchmark several evaluation metrics, including Vina docking score, QED, SA, MRR, success ratio, and QikProp pass ratio. We also report the average molecular weight. Performance ranking per column is color-coded as follows:
best
aecond-bes
Category
Method
Vina
QED
SA
MRR
Success 1
QikProp
MW
VAE-based
LiGAN
-6.640
0.394
0.601
59.08%
2.79%
17.37%
286.44
AR- AR-based
AR
-6.737
0,507
0.635
56.67%
3.28%
18.66%
247.50
AR-based
Pocke(2Mol
-7.246
0.573
0.758
67.88%
14.60%
29.58%
234.30
Diffusion-based
TargetDiff
-7.452
0.474
0.579
37.81% %
3.04%
27.63%
346.24
Diff + Inter-Guide
IPDiff
-7.745
0.511
0.627
29.83%
5.31%
25.11%
328.34
Diff Inter-Prior
DecompDiff
-8.260
0.444
0.609
62.60%
15.72%
29.04%
424.09
Diff + Multi-
TAGMol
-7.563
0.563
0.583
37.31%
3.23%
32.31%
325,50
Fragment-based
DrugGPS
-7.396
0.463
0.622
54.80%
7.17%
25.60%
329.88
BFN-based
MoICRAFT
-7.783
0.503
0.685
58.47%
13.72%
22.37%
325.63
3DSBDD LLM
CIDD
-8.496
0.576
0.735
81.74%
34.59 %
35.22% %
336.70
paradigms-combiiingg the strong binding affinity modeling capability of 3D generative models with the drug-likeness reasoning and instruction-folloviin power of large language models. Guided by expert-designed prompts and leveraging the LLM's embedded chemical knowledge, CIDD refines molecular structures toward realistic and pharmacologically meaningful candidates. As result, it achieves significantly higher success ratio of 34.59 %, compared to 15.72% % for the best
7

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25.4%
19,4%
39.25
3.4%
7.6%
ganooeii Eor 251
86 05.24%
24.7%
20.0%
13.8%
92.6%
102.8%
13.7%
29.3% 57.4%
044
(a) QED
(b) SA Score
(c) Reasonable Ratio
(d) QikProp Pass Ratio
Figure 3: Performance comparison across different metrics indicative of drug potential between
models using only SBIG outputs and those incorporating LEDD outputs, evaluated across various 3D-SBDD models as the SBIG module.
baseline seeeiiin--dmmosstrttaiinn its unique capacity to generate molecules that are not only strong binders, but also truly drug-like and synthetically feasible. Interestingly, CIDD achieves comparable QED and SA scores, while significantly outperforming Pocket2Mol in MRR, despite generating molecules that approximately 50% larger average in terms of molecular weight (336.70 70 234.30). Moreover, achieves a substantially better docking score. This result provides strong evidence that CIDD is capable of generating larger molecules that are still drug-like, suggesting that its strong performance on drug- drug-likeness metrics is not simply a result of hacking these metrics by producing smaller, simpler molecules.
4.3 Improvements with Different Models on Multiple Metrics
CIDD is a flexible framework designed to interface smoothly with a broad spectrum of 3D SBDD models, significantly enhancing the quality of generated molecules. As illustrated in Figure 3 CIDD brings substantial and consistent improvements across key drug-likeness metrics-includiee SA Score, Reasonable Ratio, and QikProp Pass Ratio-achieving gains of 31.4%, 20.0%, 85.2%, and 102.8%, respectively. These improvements are observed across different base models, demonstrating CIDD's strong generalization ability and its capacity enhance diverse, diverse aspects of drug- likeness simultaneously. In contrast to optimization- based methods that often overfit individual metrics, CIDD drives broad and meaningful improvements that reflect a true advancement in the quality of generated drug candidates.
QED,
4.4 Ablation and Analysis
Table 2: Ablation studies LLM variants and pure LLM-based SBDD.
(a) Different LLM Backends in CIDD
(b) LLM-Only vs.
CIDD Comparison
LLM
Vina.
MRRT
Similarity?
Vinal
MRR1
Success Ratio
-7.78
58.47%
LLM-SBDD
-6,244
97.45%
5.95%
GPT-4o-mini
-8.29
80.02%
0.220
CIDD-LLM
-7.230
90.97%
17.59%
GPT-40
-8.50
81.37%
0.296
CIDD
-8.496
81.74%
35.22%
DeepSeek-v3
-8.49
76.00%
0.379
DeepSeck-rl
-8.57
79.17%
0.182
4.4.1 Impact of Different LLMs
We evaluate GPT-4o, GPT-4o-mini, DeepSeek-v3 [5) and DeepSeek-rl 14 using MolCRAFT as the SBIG module (Table 2). All models improve drug- likeness metrics (MRR. QikProp) and docking scores. DeepSeek-v3s achieves property gains with minimal edits, while GPT-4o-mint struggles with similarity, and DeepSeek-rl makes broader, less controllable changes. GPT-4o and DeepSeek-v3 best support CIDD's goal of generating similar yet improved molecules. Smaller models like I.LAMA-7B fail to follow design instructions. CIDD remains alng-and-play, benefiting from future L.M advances.

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Supporting molecule
CIDD strategy
CIDD result
Intoraction analysis
1.Btability and Bimpliatty. Replace complex frammenta io. conjagated diene, highly fluotnated (hwins]
Unreasonable Vina--77706
Reasonable Vina--1..355 QED: 0.860
QED: 0.202
SA: 0.62
and orug like atematives. 2.Ke 2.Key Interactions: Retain crttical interaction points with residues like "GLNS16. TRF184, VAL254, VALS15) and lL E319t)
common,
SA: 0.83
B: Gin316
A:G113
3. 3.Synthetic Feasibility...
Drugi Likeness: B.Limited Modifications....
Target: IDHP HUMAN 452.
(b)
40%
cC)
Polency 50 aif TaB
36.4%
Essiated by Vina
35%
33.0%
CIDD
Ball, Effclive. Controllanle
30.5%
31.2%
Outputs
Succees Ratto
50%
Traditional
Gap' Misalignment
Hi-tt-llad
Widely Accessible
Mateculess
(Strucarea Profemse
Chemisis)
Molecule
Drug-Skenest
10
15
Reasonabiliity
Physiochenical
Number of Previous Experiences
Biological
Figure 4: (a) A generation case and corresponding interpretable design strategy produced by CIDD, resulting in a structurally similar yet better compound. (b) CIDD demonstrates the ability to evolve by leveraging previous design experiences context, improving generation success rates. (c) CIDD integrates the strengths of 3D-SBDD models and LLMs to enable practical drug design with both high potency and sruug-ikkenes:
4.4.2 Using Pure LLM for SBDD
LLMs alone struggle with 3D protein pocket interpretation in structure-based drug design (SBDD). To test this, we prompted an LLM with PDB-format pocket data and also evaluated CIDD-LLM, which uses LLM-SBDD within the CIDD framework. As shown in Table LLM-SBDD generates chemically reasonable molecules but performs poorly on binding affinty, leading to a lower success rate than standard CIDD. This highlights the need for combining 3D models' interaction modeling with LLMs' reasoning.
4.5 Advantages and Impact the CIDD Framework
Interpretable Molecule Design. Figure 4h illustrates the CIDD generation process. The LLM- powered modules analyze and refine the raw supporting molecule (green), producing a high-quality final structure (blue). Problematic fragments-such as an unreasonable diene or an uncommon fluorinated chain thain-are are automatically identified and replaced (e.g., with a benzene ring), while side chains are adjusted to preserve key hydrogen bonds with GIn316 on both Chain A and B. These edits improve docking scores and enhance drug-relevant properties. CIDD performs this refinement through localized fragment substitutions, maintaining the core structure while improving overall drug potential. Notably, the process is inherently interpretable: each design step is traceable, with explicit rationales highlighting structural strengths and weaknesses. This transforms conventional opaque SBDD into transparent, expert-assisting workflow. CIDD also enables the automated creation of molecule pairs that differ drug-likeness with minimal structural edits. These pairs effectively capture how small chemical changes influence pharmaceutical viability, offering high- quality, distributionally aligned data for fine-tunin fine-tuning. Compared to random sampling, they provide more meaningful supervision and help mitigate data scarcity in 3D-SBDD (see Appendix H..
Evolvement Ability. One key advantage of LLMs their ability leverage prior experience, provided as context, to generate insightful outputs. To illustrate this, we conducted ooof-of--concep experiment using relatively lightweight LLM: 4o-mnni The model was given varying numbers of previous design reports and results (0, 5, 10. and 15) contextual input and tasked with generating insights to support the design module of CIDD. more prior reports were included. These results demonstrate CIDD's capacity for continual evolution by incorporating accumulated experience-ae without requiring model retraining. This mirrors the way human experts enhance their performance through repeated exposure and practice.
shown in Figure 4p, the success rate improved as

---

Generating Small Molecules with Both High Potency and Drug-Likeness, Drug potential hinges on two key factors: potency and likensik.nees. While most 3D-SBDD models emphasize target fit, they often produce chemically unreasonable structures. As shown in Figure our CIDD framework bridges this gap by combining geometric modeling with LLMM-driven reasoning. The LLM not only corrects unfavorable fragments but also plans coherent molecular edits that balance multiple objectives. By coordinating spatial and chemical constraints within a unified generation process, CIDD effectively overcomes the traditional trade-off between interaction strength and drug-likeness.
5
Conclusion
We presented CIDD, a collaborative framework that unifies 3D interaction modeling and LLM--drven reasoning for structure-based drug design. CIDD addresses key limitation of current generative models: the tendency to generate interaction compptiboenccmpttbelllee but chemically unreasonable molecules. Through a modular, interpretable generation process, CIDD achieves state-of-the-art results on the CrossDocked2020 serchmarrk--bsttnttaal QikProp) while maintaining high binding affinity. By bridging 3D geometric modeling with language- guided design, our approach sets a foundation for future directions in rational, interpretable, and generalizable drug generation. We envision such a collaborative paradigm enabling broader tasks such as target discovery and hit-to-lead optimization in carly-stage drug discovery.
improving drug-likeness metrics (QED, SA, MRR,
Acknowledgements
This work is supported by Beijing Academy of Artificial Intelligence and Beijing Frontier Research Center for Biological Structure Fundings.
References
[1] Asad Aali, Adney Cardoza, and Melissa Capo. Splitwiser: Efficient Im inference with con- strained resources. arXiv preprint arXivv2505.03773, 2025.
[2] Josh Achiam, Steven Adler, Sandhini Agarwal. Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al. Gpt-4 technical report. arXiv preprint arXiv:2303.0877/ 2023.
[3] Richard Bickerton, Gaia Paolini, Jérémy Besnard, Sorel Muresan, and Andrew Hopkins. Quantifying the chemical beauty of drugs. Nature chemistry, 4(2):90-98, 2012.
[4] Chiranjib Chakraborty, Manojit Bhattacharya, and Sang-Soo Lee. Artificial intelligence enabled chatgpt and large language models drug target discovery, drug discovery, and development. Molecular Therapy-Nucleic Acids, 33:866-868. 2023.
[5] Dee,Scek-AI, Aixin Liu, Bei Feng, Bing Xue, Bingxuan Wang, Bochao Wu, Chengda Lu, Chenggang Zhao, Chengqi Deng, Chenyu Zhang, Chong Ruan, Damai Dai, Daya Guo, Dejian Yang, Deli Chen, Dongjie Ji, Erhang Li, Fangyun Lin, Fucong Dai, Fuli Luo, Guangbo Hao, Guanting Chen, Guowei Li, H. Zhang, Han Bao, Hanwei Xu, Haocheng Wang, Haowei Zhang, Honghui Ding, Huajian Xin, Huazuo Gao, Hui Li, Hui J. L. Cai, Jian Liang, Jianzhong Guo, Jiaqi Jiashi Li, Jiawei Wang, Jin Chen, Jingchang Chen, Jingyang Yuan, Junjie Qiu, Junlong Li, Junxiao Song, Kai Dong, Kai Hu, Kaige Gao, Kang Guan, Kexin Huang, Kuai Yu, Lean Wang, Lecong Zhang, Lei Xu, Leyi Xia, Liang Zhao, Litong Wang, Liyue Zhang. Meng Li, Miaojun Wang, Mingchuan Zhang, Minghua Zhang. Minghui Tang. Mingming Li, Ning Tian, Panpan Huang, Peiyi Wang, Peng Zhang, Qiancheng Wang. Qihao Zhu, Qinyu Chen, Qiushi Du, J. Chen, R. L. Jin, Ruiqi Gc, Ruisong Zhang, Ruizhe Pan, Runji Wang, Runxin Xu, Ruoyu Zhang, Ruyi Chen, S. S. Li, Shanghao Lu, Shangyan Zhou, Shanhuang Chen, Shaoqing Wu, Shengfeng Ye, Shengfeng Ye, Shirong Ma, Shiyu Wang. Shuang Zhou, Shuiping Yu, Shunfeng Zhou, Shuting Pan, T. Wang, Tao Yun, Tian Pei, Tianyu Sun, W. L. Xiao, Wangding Zeng, Wanjia Zhao, Wei An, Wen Liu, Wenfeng Liang, Wenjun Gao, Wenqin Yu, Wentao Zhang, X. Q. Li, Xiangyue Jin, Xianzu Wang, Xiao Bi, Xiaodong Liu, Xiaohan Wang, Xiaojin Shen, Xiaokang Chen, Xiaokang Zhang, Xiaosha Chen, Xiaotao Nie, Xiaowen Sun, Xiaoxiang Wang, Xin Cheng, Xin Liu, Xin Xie, Xingchao Liu, Xingkai Yu, Xinnan Song, Xinxia Shan, Xinyi
10

---

Zhou, Xinyu Yang, Xinyuan Li, Xuecheng Su, Xubeng Lin, K. Li, Y. Q. Wang, Y. X. Wei, Y. X. Zhu, Yang Zhang, Yanhong Xu, Yanhong Xu, Yamping Huang. Yao Li, Yao Zhao, Yaofeng Sun, Yaohui Li, Yaohui Wang. Yi Yu, Yi Zheng, Yichao Zhang, Yifan Shi, Yiliang Xiong, Ying He, Ying Tang. Yishi Piao, Yisong Wang. Yixuan Tan, Yiyang Ma, Yiyuan Liu, Yongqiang Guo, Yu Wu, Yuan Ou, Yuchen Zhu, Yuduan Wang, Yue Gong, Yuheng Zou, Yujia He, Yukun Zha, Yunfan Xiong, Yunxian Ma, Yuting Yan, Yuxiang Luo, Yuxiang You, Yuxuan Liu, Yuyang Zhou, Z. F. Wu, Z. Z. Ren, Zehui Ren, Zhangli Sha, Zhe Fu, Zhean Xu, Zhen Huang, Zhen Zhang, Zhenda Xie, Zhengyan Zhang, Zhewen Hao, Zhibin Gou, Zhicheng Ma, Zhigang Yan, Zhihong Shao, Zhipeng Xu, Zhiyu Wu, Zhongyu Zhang, Zhuoshu Li, Zihui Gu, Zijia Zhu, Zijun Liu, Zilin Li, Ziwei Xie, Ziyang Song, Ziyi Gao, and Zizheng Pan. Deepseek-v3 technical report, 2024.
[6] Jorg Degen, Christof Wegscheid-GGrlachh Andrea Zaliani, and Matthias Rarey. On the art of compiling and using' drug-like' chemical fragment spaces. ChemMedChem, 3(10):1503, 2008
[7] Vineeth Dorna, D Subhalingam, Keshav Kolluru, Shreshth Tuli, Mrityunjay Singh, Saurabh Singal, NM Krishnan, and Sayan Ranu. Tagmol: Target-aware gradient-guidee molecule generation. arXiv preprint arXiv:2406.016690 2024.
[8] Peter Ertl and Ansgar Schuffenhauer. Estimation of synthetic accessibility score of drug-like molecules based molecular complexity and fragment contributions. Journal of cheminfor- matics, 1:1-11, 2009.
[9] Paul G Francoeur, Tomohide Masuda, Jocelyn Sunseri, Andrew Jia, Richard B lovanisci, Ian Snyder, and David R Koes. Three-dimensional convolutional neural networks and a cross- docked data set for structure-based drug design. Journal of chemical information and modeling, 6009)4420--4 2020.
[10] Bowen Gao, Haichuan Tan, Yanwen Huang, Minsi Ren, Xiao Huang, Wei- Ying Ma, Ya- Qin Zhang, and Yanyan Lan. Reframing structure crusture-based drug design model evaluation via metries correlated to practical needs. In The Thirteenth International Conference on Learning Representations.
[11] Team GLM, Aohan Zeng, Bin Xu, Bowen Wang. Chenhui Zhang, Da Yin, Dan Zhang, Diego Rojas, Guanyu Feng, Hanlin Zhao, al. Chatglm: A family of large language models from glm-130b to glm-4 all tools. arXiv preprint arXiv:2406.122 12793, 2024.
[12] Jiaqi Guan, Wesley Wei Qian, Xingang Peng, Yufeng Su, Jian Peng, and Jianzhu Ma. 3d equivariant diffusion for target-aware molecule generation and affinity prediction. arXiv preprint arXiv:2303.0334. 2023.
[13] Jiaqi Guan, Xiangxin Zhou, Yuwei Yang. Yu Bao, Jian Peng, Jianzhu Ma, Qiang Liu, Liang Wang, and Quanquan Gu. Decompdiff: diffusion models with decomposed priors for structure- based drug design. arXiv preprint arXiv:2403.07902, 2024.
[14] Daya Guo, Dejian Yang, Haowei Zhang, Junxiao Song, Ruoyu Zhang, Runxin Xu, Qihao Zhu, Shirong Ma, Peiyi Wang, Xiao Bi, et al. Deepseek-rl: Incentivizing reasoning capability in Ilms via reinforcement learning. arXiv preprint arXiv:2501.12945 2025.
[15] Zhilin Huang, Ling Yang, Xiangxin Zhou, Zhilong Zhang, Wentao Zhang, Xiawu Zheng, Jie Chen, Yu Wang, Bin Cui, and Wenming Yang. Protein-ligand interaction prior for binding- aware 3d molecule diffusion models. In The Twelfth International Conference Learning Representations, 2024.
[16] Leukothea loakimidis, Loizos Thoukydidis, Amin Mirza, Saira Naeem, and Johannes Reynisson. Benchmarking the reliability of qikprop. correlation between experimental and predicted values. OSAR& & Combinatorial Science, 27(4):445-455 2008
[17] Craig Knox, Mike Wilson, Christen M Klinger, Mark Franklin, Eponine Oler, Alex Wilson, Allison Pon, Jordan Cox, Na Eun Chin, Seth A Strawbridge, et Drugbank 6.0: the drugbank knowledgebase for 2024. Nucleic acids research, 522D))DD125-D1275, 2024.
11

---

[18] Woosuk Kwon, Zhuohan Li, Siyuan Zhuang, Ying Sheng, Lianmin Zheng, Cody Hao Yu, Joseph Gonzalez, Hao Zhang, and Ion Stoica. Efficient memory management for large language model serving with pagedattention. In Proceedings of the 29th symposium operating systems principles, pages 611 611-66 1-66,, 2023.
[19] Haitao Lin, Guojiang Zhao, Odin Zhang, Yufei Huang, Lirong Wu, Zicheng Liu, Siyuan Li, Cheng Tan, Zhifeng Gao, and Stan Z Li. Cbgbench: fill in the blank of protein-molecut complex binding graph. arXiv preprint arXiv:2406. 10840, 2024.
[20] Christopher A Lipinski, Franco Lombardo, Beryl W Dominy, and Paul J Feeney. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings, Advanced drug delivery reviews, 64:4-17, 2012.
[21] Aixin Liu, Bei Feng, Bin Wang, Bingxuan Wang, Bo Liu, Chenggang Zhao, Chenggi Dengr, Chong Ruan, Damai Dai, Daya Guo, et al. Deepseek-v2 v2: A strong. economical, and efficient mixture-of-experts language model. arXiv preprint 240.:24000004444. 2024.
[22] Shengchao Liu, Jiongxiao Wang, Yijin Yang, Chengpeng Wang. Ling Liu, Hongyu Guo, and Chaowei Xiao. Conversational drug editing using retrieval and domain feedback. In The twelfth international conference learning represeniations, 2024.
[23] Siyu Long, Yi Zhou, Xinyu Dai, and Hao Zhou. Zero-shot 3d drug design by sketching and generating. Advances in Neural Information Processing Systems, 35:23894 23990,, 2022.
[24] Shitong Luo, Jiaqi Guan, Jianzhu Ma, and Jian Peng. A 3d generative model for structure-based drug design. Advances in Neural Information Processing Systems, 34:6229-622 6239, 2021.
[25] Xingang Peng. Shitong Luo, Jiaqi Guan, Qi Xie, Jian Peng, and Jianzhu Ma. Pocket2mol: Efficient molecular sampling based on 3d protein pockets. In International Conference Machine Learning, pages 17644-17655. PMLR, 2022.
[26] Yanru Qu, Keyue Qiu, Yuxuan Song, Jingjing Gong, Jiawei Han, Mingyue Zheng, Hao Zhou, and Wei- Ying Ma. Molcraft: Structure- srusturr-bbased drug design in continuous parameter space. arXiv preprint arXiv:2404.122144 121411 2024.
[27] Matthew Ragoza, Tomohide Masuda, and David Ryan Koes. Generating 3d molecules condi- tional on receptor binding sites with deep generative models. Chemical science, 13(9):2701- 2713, 2022.
[28] Raimond BG Ravelli, Benoît Gigant, Patrick Curmi, Isabelle Jourdain, Sylvie Lachkar, André Sobel, and Marcel Knossow. Insight into tubulin regulation from a complex with colchicine and stathmin-like domain. Nature, (9869999):1882000 2004.
[29] David Rogers and Mathew Hahn. Extended-connectivig fingerprints. Journal of chemical information and modeling, 50(5):742-754, 2010.
[30] Schrödinger, LLC. QikProp. Schrödinger, LLI., New York, NY, 2025. Schrödinger Release 2021-2.
[31] Yuxuan Song, Zheng Zhang, Cheng Luo, Pengyang Gao, Fan Xia, Hao Luo, Zheng Li, Yuehang Yang, Hongli Yu, Xingwei Qu, et al. Seed diffusion: A large-scale diffusion language model with high-speed inference. arXiv preprint arXiv:2508.0211 2025.
[32] Bart L Staker, Michael D Feese, Mark Cushman, Yves Pommier, David Zembower, Lance Stewart, and Alex B Burgin. Structures of three classes of anticancer agents bound to the human topoisomerase i- dna covalent complex. Journal of medicinal chemistry, 48(7):2336-2345, 2005.
[33] Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timo- thée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, et al. Llama: Open and efficient foundation language models. arXiv preprint 232. 2023.
[34] Oleg Trott and Arthur J Olson. Autodock vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. Journal of computational chemistry, 31(2):455-46 2010.
12

---

[35] Xiaolun Wang, Shelley Allen, James F Blake, Vickie Bowcut, David M Briere, Andrew Calinisan, Joshua Dahlke, Jay B Fell, John Fischer, Robin J Gunn, et al. Identification of mrtx 1 133, noncovalent, potent, and selective krasg I 2d inhibitor. Journal of medicinal chemistry, 63(4):3133-3133, 2021.
[36] Jiale Xu, Rui Zhang, Cong Guo, Weiming Hu, Zihan Liu, Feiyang Wu, Yu Feng, Shixuan Sun, Changxu Shao, Yuhong Guo, et al. vtensor: Flexible virtual tensor management for efficient Ilm serving. arXiv preprint arXiv:2407.1539, 2024.
[37] Zaixi Zhang and Qi Liu. Learning subpocket prototypes for generalizable structure-based drug design. In International Conference Machine Learning, pages 41382-41399 PMLR, 2023.
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A Discussion on the Pharmaceutical Terminology
In this work, we employ comprehensive set of evaluation metrics- Vina, QED, SA, our pro- posed MRR, the Success Ratio, and QikProp--o analyze different facets of molecular generation performance. Following established practices, we use Vina scores assess the 3D complementar- ity between generated molecules and their target binding sites. Meanwhile, we evaluate broader molecular properties using standard metries (QED and SA), the domain-infonee informed MRR, and physico- chemical and pharmacokinetic descriptors from QikProp, which are widely used in computer-aided drug design (CADD).
To clarify the key concepts underpinning our evaluation framework, we distinguish among drug potential, drug-likeness, molecular reasonability, and chemical validity.
Previous studies have often focused too narrowly on geometric complementarity, overlooking other essential requirements drug-like molecule must fulfill. We introduce the term drug potential to describe the overall suitability of molecule as a drug candidate, encompassing not only binding affinity but also synthetic accessibility, chemical stability, pharmacokinetics (absorption, distribution, metabolism, and excretion), and safety. These properties are intrinsically determined by molecular structure and collectively influence whether molecule can reach its intended biological target and survive the drug development process.
The term drug-likeness is widely used in medicinal chemistry to reflect the multidimensional suitabil- ity of a molecule as a drug candidate. Drug discovery and development (DDD), however, are deeply implicit preferences for molecular scaffolds and nuanced,
influenced by human expertise-inclueee often tacit, domain knowledge that difficult to formalize or quantify. As a result, even experienced medicinal chemists struggle to define approximate the true probability function p p(drug). which represents the likelihood that a molecule will become a viable therapeutic candidate. However, the machine learning community often oversimplifies drug-like likeness to metrics such as QED or Lipinski's Rule of Five, which capture only a narrow range of basic physicochemical properties. This simplifi- cation overlooks critical factors such as oral bioavailability, metabolic stability, and toxicity risks (e.g., hERG liability).
At more fundamental level, molecule must be chemically valid, meaning it adheres to basic chemical rules such as proper valence and atom types. However, we observe that many model- generated molecules-whit technically valid-contan would be flagged by human medicinal chemists. These structures are neither common nor practically accessible and thus fall outside the bounds of what is typically accepted in pharmaceutical research. Despite the central importance of this distinction, prior work has not proposed an effective metric to differentiate between chemically plausible structures and those that formally valid but unrealistic. To fill this gap, we propose MRR, a rule-based metric that reflects medicinal chemistry heuristics. identifies implausible features such unstable ring systems and uncommon conjugation patterns, offering an interpretable and practical means of identifying unrealistic model outputs.
rare unstable structural substructures that
By explicitly defining these concepts and introducing MRR, we aim to guide molecular generation efforts toward pharmaceutically meaningful directions, bridging the gap between computational outputs and real-world drug development feasibility.
B Limitations
One limitation of CIDD its dependence on pretrained LLMs, which may occasionally introduce hallucinations in underexplored chemical regions.
C Detailed Prompts and Responses for LEDD
In this section, we present the detailed workflow of the CIDD framework, including the prompts and example responses for each module.
Figure illustrates the complete drug design pipeline. The Interaction Module first identifies key fragments within the supporting molecule that interact with the protein pocket. This information is then utilized by the Design Module, which devises strategies to replace uncommon or unfavorable fragments while preserving crucial interactions. Once new molecule is designed, the Evaluation
14

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Phase within the Design Module assesses its viability. Finally, the Reflection Module analyzes the design process and outcomes, highlighting both strengths and areas for improvement.
Figure
presents the prompt and example response for the Interaction Analysis Module.
Figures
and
display the prompt and example response for the Design Module.
Figures
10 and
illustrate the prompt and example responses for the Reflection Module.
Figures
12 and 13
show the prompt and example response for the Selection Module.
Pa/A
Iokikie and meediig
www.arr antory
AD0120 on kast JCh1o Arand 7YO6 T.l CA
Arees Smngl Properte
Eoee doring PIOR MEsICTI CUAStAL and plaaeeess pohensi "Maeece thaa
Figure 5: Workflow of CIDD framework
15

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Interaction analysis
The interaction analysie reeutts are as follows: (PLIP(Docking(x. P))
The smiles string of the molecule is: [xo]
Query
[Important] Using the information from the interaction analysis and the fragment labots, based on the coordinates matching, you need identify the key fragment the molecule mhtt conduct the interaction with the profein pocket. [Imporaant]
fragment
resicue protein pocket. example:
The fragment 'ethanol can interact with the residuve 'ASP123' the protein pocket. [Imporiant] it should be fragment level, instead of atom level. You need use the information from the fragment labels dientily belongs fragment.
Output [6]]
LLM,
#5e "Key Fragments of Importance"
Based on the analysis, the follwing fragments are most crucial for interactions with the protein pocket:
"Aromatic Hydrophoblc Core:"
[CJ01=C(0[+CCNC=-0 (Alome: 5-11)
Criticl for hycraphobic interactions with ILE113, LEU142, and LEU147. fragment anchors the molecule the hydrophobic pocket.
2. "Carbonyl (-C=O):*
'12, Vital for hydrogan bands, particularly with GLY63 and GLU64, This fragment contribute to the specifictt of the Interaction.
3. "Oxetane Ring:
|C]|CCCCj01 (Atoms: 15-18)
Key hydrogen bonoing TYR246, providing spectiiity
4. "Aromatic r-Syatem:*
NCCC=NN (Atoms:
Engaged restacking
TYR80, stabilizing the ligand the binding pocket.
"Halogenated Group:**
CCCCCCC))CCCCC-CCCC (Atoms:
Responsible for halggen bonding with PHE61, contrbbting unique and specttic Interactions.
Figure 6: Interaction analysis module
16

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Design Part 1
[Instruction]
This ls the original molecule: [xg]]
This the interaction analysis: [6]
Basad the interaction analysis, design modilcatiins to the original molecule. Your job is scatfold hopping: change scaffold
retsining key pharmacphoors.
[Design Objectivee]
The modified molecule should
stable and easier synthesize than original molecule.
fragments should common,
2. The moditted molecule should retain key properties of the original molecule, such sa shape, a22,, and functionailty 3. The moaileed molecule should contain the key interaction types with the same residuess the orignnal malecule. original molecule may with more common and stable counterparts, ensuring that the modiicaatios retain key propertess the orgiinal malecuia, such shape, size, and functionality For instance, you might replace syclohexadlene with benzene. drug
unconmon
fragments. Identiyy
fiagments replace
mooifeed molecule
than original molecule.
[Deeign Requiremente]
Do not modily the molecule much. only give moditiction steps, not generate smiles by yourselt use agent to generate the new molecule based on your design.
(Experience Summary)
We have done some medilcaations before. You can learn from the previous designs and reflections to make betler modification.
Hore is the summary of the provious designs and reflections: [R]
give modilication suggestions requirements.
Output [Dy]:
#P8 "Step Core Scaffold Replacement"
LLMp
"Unstable Uncommon
The original scafiold contains an oxetane rig ([C))C)))) ) which, while benericial for notabiliy and hydragon bondling can be chemically unstable under metabolic conditions and is synthotically challanging.
"Proposed Modilication:"
Replace the oxotane ring with "tetrahydrofuan (THF) ring"" THF more stable, synthetically accssible, and retains the ability hydrogen bonds oxygen importantly maintains similar shape, and poianity
#00 "Step 2: Optimize the Hatogenated Region"
"Uncommon Unstable Fragment:"
instabilit toxicity (especially with chionine) in some cases. The postion and type of halogenation also determine interaction strengih.
halagenated
fragment (CC--CCC))-CCNC))--11 ) contribte
"Proposed Modilication."
Replace the chlorine stom ('CCI) in the balogenated ring with a triliuormeetly group ('CF3', which metabolically more stable and provides similar hydrophabic character and electronic properties to maintain halogen-bonding remain unallered.
interactions. The fuoorne
#20 "Step 3: Revise the Aromatic Core"
"Uncommon Unstabie Fragment:: ring JCJC1-C/CH+CCCCI-C1 provides hycraphobicily and anchors the molecule, replacing with more common aromatic fragment (e.g. phenyl) can reduce metabolic labiliies without compromising interactions.
polyoyclic conjugated system prone to oxidative
"Proposed "odilication:"
Replace |C]C1=C[C)- with "'a phenyl ring (benzene)". Phenyl rings retain hydrophobiciiyy and size while reclusing the litelhood of oxidatio and increasing synthetic accessiblity.
Figure 7: Design Module
17

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Design Part 2
Output [Dy]:
#ee "Step 4: Replace the 1.2. T-iraazine Ring" "Uncommon Unstable Fragment:"
LLMo
[N]1=NN[C)=CY very common drug-lke scallolds and may pose synthetic chalenges.
rig) involved
"Proposed Modiiiction:"
Replace the 1,2,4-tiazine ring with "pynidine ring (CSH5N)" Pyridine maintains aromaticity hydrogen bond acceptor potential (Via the nitrogen alom) for r-stacking but is more drug-Wike and easier to syntheeize.
"Step Simplily Linker Chains"
"Uncommon Unstable Fragment:
The kinker fragment connecting the oxetane ring and other parts of the molecuie may Introduce unnecessary entropic
upon binding.
"Proposed Modilication:"
Sharten the lnnkr chain removing simplifying alkyl Use lower methylene groups where applicable reduce flexbility while maintaining shape size for proper binding.
### "Summary of Modifications"
Replace with "THH ring" while refaining hydragen-oonding capsbillly
"Oxetane Fing AintaableUnnoomneee
stabilty synthetic accessiblity
"Halogenated Aromatic Ring (Metabolic Liabilly)." "M Replace chlorie ('Or) with "billuorometly group ('OF3)" to improve metaboli stabillyy while maintaining hydrophoble interactione and halogen bonding.
(Oxedative Risk):" Replace [C]CC==QCCW(C]jjieC reduce matabolic labilit while rotaining hydrophobicity.
"Polycyclic
"phenyl ring (benzene)"
Trazine Rlnng (Uncommon Scalfoid) Replace with "pynldine ring" to retain r-stacking and hydrogen- acceptor
improving drug-ikkness. "Flexible Linkers (Entropic Pensily)." Simpliyy and rigitll linker regions by shortening akkyl chains, reducing Rexibilly.
This is the original malecule: [Xo]
moditication designed design expert: [Dy]
Change the molecule structure based modification designed the design expert. Importantly, modified sense in context medicinal chomistry.
molecule should
Query
Now plesse only return the smiles string the generated malecule. The smiles string should be enclosed pair SS8. example: 558o20553
Output [x,]:
LLMp
Figure Design Module
18

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Reflection Part 1
[Instruction]
This the original molecule structure: [Xo]
interaction analysis this molecule and protein pocket: [f]]
Query
This the moditication design generated by you: you:[Dy] [Dy]
designed based on your modification [x,]
This is the intoraction analysts ganerated by intoraction export agent: [4]
Previousiyy, we asked you to modil molecule to get new molecuie that binds better to the protein pocket. We protein pocket and put together with the binding before the
analyzed the binding the malecule modilication for comparison.
Based on these anaayses, you need to reflect on the previous modilizaiion and provide leecback on the design. can find which fragments inside the structure of these two molecules useful for binding the protein pocket.
Besides,
mememement
Please use critical thinking analyze, pointing out both the good and the bad points. Your raflection
Output [Ry]:
Aee "Strengthss of the Modifications" "Improved Stabilly and Synthelic "ccessbiity."" Replacing "ooottaee rin" with "tetrahydrofuran (THF) ring" a commendable adjustment. THF rings are hycragen-bonding bonding Interactions and similar storic buik. more stable under metabolic conditions, while retaining key hydragen
LLMR
This was sensible charge that aligne well with drug-ikanees priiciples.
atom" The CF3 group improves metabolic stabilty and reduces potential toxicity from reactive halagens, while maintaining the original hydrophabicity and shape.
Removing
"Villuoromettyl (CF3) group" was thoughtlul modifcation.
2. "Retention Key Interaction Modes:"
The redesigned molecule retains criiical interactions such as hydragen bonding with HIS117 and ARG152, hydrophabic interactions with HIS80, LEU147, and PHE188, n-stacking with TYR90. These interactions crucial binding afiintyy ensure molecule continues engage protein pocket effectively.
The "pyridine rin"" (replacing the lass common 1,2,4 4-riaziii rig) algns better with drug ilknness rules while mainitiniigg hydrogen bonding and hydrophabic capsbillles, This subelitution strangthens the molecule's charmacokinelic prospects.
3. "Refinement of Scafloids and Simpilication."" Simplifynng the polycyclic aromatic prone elements. This ensures the molecule is more stable and syntheticall tractable without losing significant hydrophobic interactione.
"benzone ring"* reasonable modification reduce oxdiation
Modifications" 1. "Reduction in Docking Score."
"Weaknesses
The docking score decreased from "*-11 (original molecule)" to **9.215 (modttied molacule)"", indloating drop binding alfinity. While modified were inadveriently weakened during redesign. The TEF reduction suggests a loss of key complementary contacts between the ligand and protein pocket.
relains key interactione, some binding contrbutione
2. "Ovar- Simpiifcation the Aromatic Cora." Replacing the conjugsted aromellc system ("CJC1=00GGHOJCC-00 7 with a simple "benzzne ring" reduced the hydrophobic surface and reelectron syslem available for interactions, Consequently hydrophobic interactions with residues TYR60, key minteractions within the pocket.
and LEU142 diminished,, and molecule lost some abilly to stabaize
"Unutiized Potential While the influrrmeetyll (CF3) group improved metabole stability, did not directy contribute birding
Titfuuormethhl Group:" **
interactions within the protein pocket. This functional group occupies space but does not engage with any specilic resicues, effectively adding molecular weight without enhancing binding, better centter-paace funchionally active replacement could have ylolded groater interaction strongth.
Figure 9: Reflection Module
19

---

Reflection Part 2
Output [Rq]:
"Redducdd Hydrogen
LLMp
The original malecule formed "mulliale airong hydrogen bonds", particularly with residues such as GLY63, GLUB4, and TYR246 via ite oxetane ring and carbony group. These hydrogen bonds were either weakened or lost in redeesign, hydrogen bonds (forming with HIS117 and ARG152) failing fully compensate for this depistion.
5. "Flexbilly in THF Ring."
ring. likely greater fiexbillty sp3- hybridized cartons. This flexbilit may lead to entropic penaliess upon binding, making the malecule less efficient in achioving preorganized. bioactive contormaioon.
"Over-Rellance on Hydrophobic Interactions""
Similar to the original molecule, the modilied structure still relies heavly on "hydraphoble interactione" for binding HIS60, TYR50, LEU147). Hydrophabic interactions less specilic than polar lonic interactions, which cntical seloctivity affintty By not introducing additional polar groups, redesign missed an apportunity improve overall binding spectiiity.
"Comparison Binding Fragments"
8868 "Original Molecule:"
1. "Oxetano Ring (Lost the Redesign)::**
pocket, contributing significantly specttic strong Agand binding. its removal weakened overall binding sifinity.
hydrogen
2. "Conjugated Hydrophobic Care (10]C1-CCG-(CMCC-C1 ):
Provides extensive hycrophobic and r-stacking interactions with residues such as ILE113, LEU142, and TYR90.
Replacing
aromatic structure with benzeno ring reduced the hydrophoblc mellscroo contribtion to
binaing.
3. "HHllggentted Aromalic Fing (PCt=CCIC(=CCTCC00011 ):"
Engages with CF3 abolished some halogen bonds, weakeming protein-liand complementarity.
halpgen bonding
adding spectiicty binding
Replacing
"Modlted Moleculs."
1. "THF Ring (ntroctuced)."
Feetains the ability to form hydrogen bonds (e.g. with ARG152). However, its Introduction has caused greater flembblity and entropic penallies that may offset its stabilizing contribtions.
"Trilluoromcchyl Group (CF3, Introduced):"
Metabolically stabie and hydrophobic but does not directly Interact with the protein. Its placement on the aromatic rng adids molecular weight but no funciionl enhancement to bunding. "Fyridine Fing (Introduced)." "*
Maintains hydrogen-bondingg with HIS117 and interacts hycrophobically with HIS60, penforming well
replacement for 1.2, 4.2.4-nrazine. Though not inherently diverse in interaction apportunties as its predecessor, it improves drug
the molecule.
4. "Benzzne Ring (Stmpulleo):"*
While it retains some hydrophoblc interaction capabilit, this fragment no longer engages reskcuue like TYR90 or PHE166 robustly original conjugeted aromatic syslem. This reducced binding allinty.
Figure 10: Reflection Module
20

---

Reflection Part 3
Output [R:]:
Fragments Retained
Binding
LLMp
"Criical Fistained Fragments."
1. "Pyndine Fing" for n-stacking and hydragen bonding with TYR90 and HIS117 "Hydragen Bonding Carbonyi Group" interacting HiS117 ARG152.
"Crlical Fragments Lost."
1. *Ovetane Fing" contribued unusually hydrogen bands to TYR246, which lost the redesign. restacking
"Conjugated Hydrophabic Core
hydrophoblc
LEU1-4 42 and TYR90.
3. "HHllgenstted Aromatic Ring" provided helogen bonds with PHEB1, contributing unique specificlty lost the redesign.
#** "Suggestions for Improvement"
"Rointroduce Hydragen Bonding Groups:*
back poiar fragments (e.g. hydroxyl, amide groups) increase hydrogen- bonding capecity with residves TYR246, GLY63, and GLU84.
"Revisi
Instead oversimplityng the hydrophabic care to bonzone, consider polycyalic aromatic or hoterocyolic system to balance hydrophoblolty and metabolic stabiltty.
3. "Optimize Trilluoromethll Group Placement:" Move or replace the CF3 group with fragment that directly engages residuess in the protein pocket (e.g.. dilluorapheny hydrophili substituent.
4. "Figiilly the THF Fing."
Lock the THF into constrained system (e.g.. fused bicyclic rings) reduce flexbilly and entropic penallies.
##8 "CConolusoo""
The moditictions resulted in molecule that synthetccally and metabolically improved but less effective bindling pocket, indicated redesign too many advantageous fragmente (e.g. oxetane, conjugatdd aromatic core). Future aptimizations should aim to restore lost interactions, improve hydrogen banding, and balance hychophobicily with polar contacts for stronger specific binding.
docking While retsining important interactions,
Figure 11: Reflection Module
21

---

Selection Part
[instrcctio]]
you protein
protein based you
you
binding anatyes
has potential resl drug. shoul output reason string of molecule amilse string should be encloed a pat SSS example: (Thh resson) The chusen
Query
Is: SECCCOSSS
This the molequle structure: [Xxx]
This the interaction
this molecule protein pocter: [(,]
This the molecule structure: [Xmm]
This Intoraction thss moleculs protain pocket. [mm]
[ppl
enatysis
protein pochet: [pp]
Please thint stop stop:
The chosen molecule is:
O=C[C1=CC=QIC-CICC222 CAG.C///CC...CCCCCCCCCCCCCC/CCCC
The docking score the chosen molecule is: -12.472
reason choosing molecule is: To select best molecule for binding the protein pooket, will step through the following considerations:
### SSeep 1:
Review Docking Scores
The docking score is key metric indicating the binding affinity between molecule and the protein pocket. Lower docking scores (more negative values) suggest stronger binding. docking scores for respective molecules are as follows:
Molecule
148
Molecule 2: -00.3
3. Molecule 3: -2.4772
From this, "Molecule 3", with the best score "'112.422", has the strongest binding affinity among candidates.
### "Step 2: Analyze Interaction Profiles**
Building off the docking scores, consider the rich interaction profiles af each molecule. A molecule that forms diverse interactions will often bind more robustly and specifically to the protein pocket.
#### 'MMolecule Interaction Highlights"*
""Hydraphobic Interactions:" ** Strong and multiple hydrophobic interactions with HIs60, TYR90, LEU147. and LEU149, anchored by the aromatic benzene ring. "Hydrogen Bonds."* Forms strong hydrogen bonds with key residues (e.g. SER150, ARG152). The interaction with SER150 (1.99 Á) and TYR246 (2. 13 Á) are particularly stabilizing.
"T-SStckkngg"" Strong r-staackin with TYR90 (T-shaped geometry).
"'n Cation interactions:" Robust recation interaction with ARG152 (3.85 Á),
stabilized further by ideal affset alignment (1.32 A).
"HHlogen Bonds:"" Two halogen bonds with GLU189 (3.84 Á) and ASN237 (3.40 A) provide an additional layer of interaction, making this molecule unique in its interaction diversity.
Figure 12: Selection Module
22

---

Selection Part
"Key Strengths:"
interaction diversity and strength of Molecule surpass molecules, leveraging hydrophobic forces, hydrogen bonding, n-stacking, r-cation interactions, and halogen bonding.
LLMg
The molecule effectively anchors itself to both polar and nonpolar regions, ensuring specificity for the protein pocket.
### "SStep 3: Assess Drug-Like Properties"
While binding affinity is crucial, the molecule must also exhibit potential drug.
Properties relevant to nug-likeness Include:
"Hydrophobic Components:" Ensure solubility and stability for membrane diffusion.
"Hydrogen Bonding Groups:"" Contribute to specificity and bioactivity.
"HHllgen Incorporation:" Enhances membrane permeabllity and metabolic stabillty. *Compact, Non-Bulky Structure:" Ensures the molecule fits within protein pocket without steric clashes.
For Molecule 3:
The combination aromatic rings, amide groups, and fluorine atoms supports balanced hydrophobic and polar properties, increasing stability and bioavailabillty. inclusion of halogems enhances drug-likeness further.
Other molecules lack some features of Molecule 3. For example:
Molecule 2 lacks halogen bonds and achieves a lower affinity score. Molecule have good binding but lack the breadth of interactions seen with Molecule
### SStee 4: Final Recommendation"
Based on docking score, interaction diversity, and drug-likeness potential, "Molecule 3 is the best candidate. Its strong binding affinity (-12.472), balanced polar and nonpolar interactions, and drug-like properties make it an excellent starting point for further optimization.
##* "Chosen Molecule"*
"Reason:" Molecule 3 exhibits the highest docking score, with diverse interaction types (hydraphabic, hydrogen bonding, restacking, recation, and halogen bonding) that secure oritical regions of the protein pocket. Additionally, offers promising drug- like well-optimized molecular structure.
features such as halogen incorporation
"Selected SMILES String:"" #boel//ccccciipoo........oooooor
Figure 13: Selection Module
23

---

D Algorithm for MRR and AUR
The complete calculation process for assessing the reasonability of molecule outlined in Algo- rithm !
Algorithm 1: Evaluation of Molecular Reasonability
Input: Molecule object (mol)
Output: Molecular Reasonability (MRR) and Atom Unreasonable Ratio (AUR) Step 1: Detect Carbonyl and Imine Group Carbons
Initialize empty list for carbonyl/mmine carbons.
foreach bond in mol do if bond double and one atom is carbon, the other is oxygen nitrogen then L Record the carbon atom in carbonyl/imine groups.
Step 2: Identification of Ring Systems
Identify all ring structures and their corresponding atom indices within mol. Calculate the number of atoms in each ring.
foreach ring in the molecule do if the ring shares or more aloms with another ring then
L Group the connected rings into single ring system.
Step 3: Evaluation of Molecular Reasonability
Exclude any atoms previously identified part of carbonyl imine groups.
Classify the remaining carbon atoms in each ring system as follows:
hybridized; Aromatic or unsaturated carbons. Ny Won-sp- hybrudized: Saturated carbons.
foreach ring system in the ring systems do if the ring system contains multiple rings and all carbon atoms non-sp² then
Mark the molecule as unreasonable.
Add the atoms to the unreasonable atom list.
foreach ring system in the remaining ring systems do
foreach ring in the ring system do
if all carbon atoms within the ring consistent in hybridization (either all sp² all
non-sp²) ) then
Mark the ring as reasonable.
else
Add the ring to the remaining ring list.
while the remaining ring list is not empty do
foreach ring in the remaining ring list do
Exclude atoms that have already been classified as reasonable.
if all remaining carbon atoms are consistent in hybridization (either all sp² or all
non-sp²) then
Mark the ring as reasonable.
no new reasonable rings are identified then
Mark the molecule as unreasonable.
Add the carbon atoms in the remaining rings the unreasonable atom list.
Exit the loop.
Calculate AUR the ratio of unreasonable atom count to the total ring atom count.
Return MRR and AUR.
24

---

E QikProp properties
The full set of properties used for the QikProp pass ratio analysis is presented in Table 3
The QikProp filter applied in the main text incorporates a comprehensive range of criteria pro- vided by QikProp, including "#stars", "#amine", "#amidine", "#acid", "#amide", "#rotor", "#ttvFG", "mol MW", "dipole", "SASA", "FOSA". "FISA", , "PISA", "WPSA". "volume", "donorHB", "ac- cptHB", "dip'/V", 5/SA", "glob", "OPpoliz". "OPlogPC16", "QPlogPoct", "QPlogPw". "QPlogPo/w", "QPlogS". "CIQPlogS", , "QPPCaco". "QPlogBB", "QPPMIDCK". "QPlogKp", "IP(eV)", "FA(eV)", "#metab", "QPlogKhsa", "PerentHummnnOallAloooriiii "SAFluorine", "SAamideO", "PSA", "#NandO", and "RuleOPThree".
Table 3: QikProp Properties and Descriptors
Property Descriphor
Description
Range Recommended Valuess
Molecule nome
The molecale's s ideniilier derived from the tile line the inpul structure life. If no tile
is provided, the file name used.
Count of descriptars or properties ralling ousside the 95% ringe for known drigs. A
0-3
higher count indicates reduced drug likeness.
Ramine
Treal noe-omjapped amine proupe present molecule.
Samidine
Number of amidine or grandine Functional groups in the structure.
Racid
Quantity carbonylic acid groups the molecule.
0-1
grops. Number of romatable bonds that are nother irivial nor sloncallyy himvered.
non-oomjageel
Arotor
rotal reactive functional group present in the molccule potentialy sillecing stability
0-2
or toxicity.
mol MW
Molecalar weight compound.
1000
7250
TNpole
Calcubiial dipole moment the molevals Dehyy
SASA
Solvent-accessible surface area (SASA) in square angstroms, measured with a probe of
3000 10000
ROSA
Hydiophobic part SASA, repeesenting saturated carhon and attache hydragos
00-7500
moms.
FISA
Hydipphilic friction the SASA, encompssing polar stoms like minrgen and oxygon
70-
PINA
SASA composent mirihurahle a-systems.
00- 4500
WTSA
Warky polar component of the SASA, including aooms like halugens, phosphorus, and sulfur.
0.0 175.0
Volume
Total colvent-ncosssible volume cublc angstroms, determined with 1.4 A rddus
5000 20000
probe:
doaveHR
Estimated number of hydragon bords donated to water in solution.
00-66
Estimated number of lyyrrges bonds accepted from water.
20- 2010
Dip"/V
Dipole momnent squared civaded by molecular volume, a key factor in solvution energy.
0.13
ACXDNI>/A
Orhesive intemerion index in solids, hased on molecular properties.
00-0000 0.05
glob
Disrrptor messuring how close the shape of a molecule to ophere.
0.75 099
pooll
Prediciod moleznlar polinzabiiity cubic angstrons.
CCo 16
Predictod puritiio coe coefiscient betwee hesadeccnee and gas phuses.
1800
Polg
Prodictod puriition sustiietent between octanol and yas pluses.
80 350
Predicsed partitine coefiicient bewween
and phases.
4.0 45.0
Prodicted partition coeflictint between octanol
water phanes.
20
QPlagS
Predicted solubility of the molecale in water (log S. in mol/L).
6.3 0.3
CrGPlogS
Conformation independent prediction of water solubility (log S).
6.5 0.5
CPPC
Predicted permeahility through Caco-2 cells, nm/s.
poor, 2500 great
QPlagBB
Predicted partitios coefficient brain/blood.
QPPMICCK
Predicted permcabiliny through MDCK cells, nm/s.
<25 poor, 2500 great
Qp
Predicted skin permeabulity (lng Kp)
-80 -1.0
IPGVV]
lonization potential calculated using PM3.
FXIVV
Flectine affinity calculated using PMT..
Amstub
Predicied number of possible mesabolic reuciions
1-8
Prediciel bending alliniyy to homan senum albumin.
-13-13
HunanOral Abberpption
Qualiarive assessment
absorption: 2 (modiuum), (hig).
Quaariative prediction of oral abeorption percentage.
2006 high, <25%% poor
SAFFuorinne
Solvent accessible fluorine surface arca.
0.0 1000
SAamideO
Solvent- aelennt-ceessiblleeeee surface
amide oxygen atoms.
0.- 35.0
PSA
surface area, calculated nitrogen, oxyges, and carbonyl groupe.
7.02200:0
#NandD)
Treal coint of nirrogon and oxygen aroms.
2-15 15
Number of Lipinsk? 5 Rule of Five Violations
Max
RoleO/Trree
Number of Jorgensen's Rule Theee violations.
Max
Wringstoms
Crent atoms within molocalar rings.
Number of siuums 3- or 4-meebeeed rings.
Number ol iioos in 6-membered rings.
Auoscon
Number ring stoms unable to form conjucstod anomatic systems..
Ancalhim
Coent of heavy (nun-lychggen) atoms in the structure.
Jm
Predicted maximam transtermal transport rate (vg cm -2 br hl.))
F Computing Resource
In this work, we primarily utilize pretrained 3D generative models and large language model (LLM) APIs to conduct our experiments. The 3D model sampling is performed using single NVIDIA A100 GPU. For the L.LM component, we rely on API-based access provided by the service provider, which requires no local computational resources.
25

---

G More Experiment Results
Based the different criteria presented in Table we provide additional pass ratio results in Table
Filter 1 is identical to the QikProp filter used in the main text.
Filter 2 removes some non-essential properties and focuses on well-defined physicochemical proper- ties, including "#rtvFG", "QPlogS", "QPlogPo/w", "mol_MW", "dipole", "SASA", "FOSA", "FISA", and "PSA".
"IP(eV)". "EA(eV)", "#metab", "PrrenthumannOrllAssrpiii
Filter assesses molecular compliance with the "RulcOfFive" criterion. However, instead of allowing up to four violations as typically recommended, this filter adopts a stricter definition, considering only molecules that fully comply (i.e., setting the maximum allowable violations zero).
Table 4: QikProp results for different methods with and without CIDD
Method
Filter
Filter
Filter 3
Pocket2Mol
Original
29.58% 56.97%
51.52%
89.58% 92.24%
CIDD
75.64%
TargetDiff
Original 26.32%
48.20% 75.60%
69. 69,47%
CIDD
53.37%
81.85%
DecompDiff
Original 29.04% CIDD
53.96% 68.48%
55. 55.14%
37.54%
65.64%
MolCRAFT
Original
22.37%
43.52%
66.45%
CIDD
35.22%
63.23%
74.09%
26

---

H
More cases
More generated molecules from CIDD are presented below. For each case, we display the initial supporting molecule derived from 3D-SBDD models alongside the final designed molecules produced by CIDD.
Supporting molecule
CIDD output
Supporting molecule
CIDD output
title: IDHP HUMAN440, 482_0
title: TOMP HUMAN_40,,LA 440_482_0 Reasonablo vina: -10, 106
title: IDHP HIMAN 40_462_0
title: IDRP JUMAN HU_MH_40MAABBBB 400_482_0 Reasonable wina: -8. -8.66
Inreasonable
Unreasonable vima: -7.911 911
vina: -7, 824
OH
JUMAN_302_728_
BUMAN 202_0022233_000000
HUMAN 30G__2GG
Inrossomable vina: 615
Rousonable vina: -10, 098
Unrvasomable vina: 054
Rousonable vina: -8. 432
title: NOS1 JIMA__002_28_0 Inreasonable vina:
title: NOS JAMAN_002_723LL0 Rensonable
title: NOSI J Unreasonable vina:
title: LLU_JINNNN22__22____0 Rensonable
HO
title: ABL2 HWWAA_224AAAAAAA 27100
title: ABL2 JUMA 33MA__24__611__0 Rensonable vina: -10, 946
title: ABL2 JUNNA_NNP2 274_61100 Unreasonable vine: -9. 916
title: ABL2 JUMAN HWM.H_22AAAAAA 274_6510 Reasonable vina: -10. 500
Inreasonable vina: -10, 007
AKTBL 3 Unroasonable vina: -10, 421
title: AKIRA_HIMN, 111661 Roasonable le vina: -10. 778
HN24_2_274_0
title: HUMAN HUMAN_2_2744_ Reasonable vina: -9.24
Unreasonable vina: -8.37
NQO1, 2_274_2_22400
NQ01, JKMMA_2,,24.0 Reasonable
PHP 1,34.00___131.00
$11.5011331400
Reasonable
-11.
27

---

Supporting molecule
CIDD output
Supporting molecule
CIDD output
HD
HD
title:
title:
title: BGLO7, 28
title: BELDT, BCLDF_ORYSJ__ 28 Reasorable vina: -BL 949
PIKGL_RABIT 0
PHIG BINGIRRABI LABIT 298 AS
Unreasonabl vina: -8. 041
Unreasonable
Reasonable v Via: -8. 727
vina: -8. 548
HO
Almne
title: CDGR JUMAN $0000000 Inremsorable vina:
title: CIOR 11M00_00130000 Ressomble
title: TNKS2 WWAN_9981116222
title: TNKS2 JWOMANL 148_1162_000 Reasorable vina: -10,
Unreasomable virm: 284
title: P2V12 1_322_1 14220
title: P2V12_ P 1_3420 Reasonable vina: -10. 674
title: HND METJAA11_358
title: HMD METJA___3ERR
Unrensonable vina: -10. 663
Unreasoeabl le vina: -9.250 259
Beusonable vina: -10. 458
LLNCAALIAD__631L__ Unreesonable
SOIA_ALTAD_LL660 -17.
title: SUBC ALIAD_1_631__ 626
vina: 278
-13,
title: BOAT HUMAN668,... 65L_0100 Unrvesonable vina: -7. 891
title: BGAT JUMAN 63_36330
title: CHIB 18990 Unrensonable vina: -10, 676
title: CHIB L_99A_1_19900
: Ressonuble vina: -9.201
Rensorable vina: -13. 113
CHIB_ 1180AA_1_998
title: NRIHHLHMAN_ACE BAWIIH__WMAN22___6666, Unreusceable 038
MRWSH__WWA__588__A Rensotuble
Inreasonable
Ressonnble
28

---

Supporting molecule
CIDD output
Supporting molecule
CIDD output
title: SOTA L
title: SOLA L Rensenable vina: -11. 291
title: BOLL
title: SWALLETLLLLLLLLL
Unreasotable vina: -11. 019
Iirensonable vima: -8. B2
Housonable vina: -13. 957
title: GSTPL JEMAN Unreasotable vine: -7. 591
title: GSTPL JWW.A_LAA22000 Resaenable vina: +8. 725
Inreasonable vina: -7. 709
Reasonable
vina: -8. 778
HBUAH WM4
OHH
title: 1CISL_1_885_0
Unreasopable vina: -8.5
Rensonable
Ibreasonable
RRnsonable vina: -12 979
vina: +0, 518
vinat -11. 943
title: IDSP JUMAN, 40_452_0 vine: 176
IIMP BWW____NN_AOA2202200 Renasnable vina: -10.774 774
NRI54 JEMAN25R 2AN_885888,,,,
title: NRIH JIWAN 20W_22558_00000 -5.578
vina: 824
title: 10MP JUMAN 40_488_0 vina: -7. 605
title: TIMP JUMN, 40_482_00 -11. 661
title: CPUE ANCML_A__RLL 607
title: CPUL BAL_LAABL__LLLLL 664_0
Ho
title: AKIBA LIWLLL3
title: AKIBA JEMAN L Renacnuble
title: P2Y12 JUMAN JEMAN_1.340 1,342 Inressonable
title: P2T12 JUMA, 1,342.0 Rensonable
title: OLINC CA Inreatotable
title: OLTAC CANSA_L Rensenable
sitle: SIRI JIMAN JIMAN117706600
title: SERO JUM 11T_38888 Ronsonablee
: Inressonable
29

---

Supporting molecule
CIDD output
Supporting molecule
CIDD output
title: MOOP 3HLO lnrensonuble
title: WOF P Reasonable
title: DED RAT 1.311,319,0 Enreasonable
técle: DIDE RAT_1_319_0 Reasonable
titll: TIMP JEMAK_404482 4400482200
title: TRP JKMAN_004452_, Reuennable vina: -11.583
title: OMA 1_ Enreasonable
title: DOAL LICTL_LL31R Ressonable
lnrwescnuble vinu: -7. 796
title: AKTI HMM
title: AKTL JKMA_L_IIZ Reasonoble vina: -9.
title: MIRIA JOMAN_12IEER LAT_O
title: M3K14 JEMAN NWW.__22666,,, Raaaonable vitas: -9, 209
lnrcesonable vinas +B. 005
Unreneorbble vina: -7.453
title: HNEL
title: METJA_L_BBBO Reusenable vina: -8. 55
title: CHIBL ASPPI_39_638L
title: WWUBBLASHHLLLLLLCCMM Ronsonable vina: -10, 096
Unronaceable vina: -7. 825
Unreasorable vine: -8. 061
tiell: 117_099110000
Eitle: JEMAN, 117,388_0 Rensonable vina: -10. 435
SUKL JEMN_ITT_RRRRRR..... Enrousorable vinu: -a. 991
JEMIN_ITT_BRRL___ Rassonable vira: -10. 768
lirresonable
vina: -7.
HEMIN_145_50000
Lnrresonable
Rensonoble vina: 359
Unreasoruble
Reasonubll vira:: -10, 652
vina: -7.
vina: -9. 583
L3180
title: 1_1_LAA_1_311_0
RAT_1_319_0
DIDELRAT 1_3_1
Unreasonable
Reasonable vira:: -96
vina: -0, 165
vina: -10, 631
vine: -8 563
30

---

Supporting molecule
CIDD output
Supporting molecule
CIDD output
title: INCE2 JKNAA_76_000 16000000 Eturossnabble vina: 2866
title: MARE2__MMM7066.0 4600 vina: 851
title: GOKL LAMMALL Inresecrable
title: GBR AARRL_ Rassoruble
sitle: P1OP1 1,38,_
title: FH6PL_WMA_L_SS 1,880 vina: -8, 653
title: CPAR BAOILL 064, vina: -10.
title: PPILLANNB_2_111
vina: 18.173
title: 20100
title: CAT 1,29,0 vina: -10. 184
title: BSD 1_39E_1_1900 vina: -f. 992
title: ISD 1 786
vina: 838
title: 1 vina: -7.208
title: RGL PA 1,51,0
title: WISRRL_500 1,58,8 vina: -11,978
vina: -8, 581
vina: -10, 06
titles 30
BEL_L__ENJ300000000 133_611000
title: OPCT 38L.818 vina: 356
Inroaserable
vina: 178
vina: -9. 527
vina: -7.
KS6AIJRRMN_ _
GUXL JALJ_______BBBLL Inrossenuble vina: -7. 25
title: JEL_HIJJE_RRBG vina: 58.
Inreasenable
Reasonable vina: +6, 738
vina: -B. 242
UPS_ BO Inressonable vina: -7, 258
title: 1PP__000____2511000 Reasorable vina: 315
Inreaserablle
Reasotable
vina: +6. 241
vina: +5. 794
31

---

Supporting molecule
CIDD output
Supporting molecule
CIDD output
title:
title: ECOEX 2
Reasonable 260_480__0
llarestonshle
Rosarmuble vina: 241
Lnreesomable
vins: -9.833
vina: -6. 133
vina: -8. 963
title: BAG____NN_78_00000 Rossceublo vina: -11. 006
NIS2 2RRANNNN__788880 788505_0
JRMAA_RAAARRRLL 78_555
lisressenbble vina: -10. 594
Unresaveable vina: -10.
vina: -12. 057
title: BTRN 2,001_
title: BIRN 2AC01_2,25000 -11.729 729
title: MEE NWA__GG0_120000 Lntresseaale 564
title: ACE ROMX__G0_122000 Rasactable
-0.306
titlle ADE JUMAN 6500 1250,0 D litreseonablle
title: ACE JUMAN, 6500_6200000000001 Rossccaah
title: AGE NAMXX_600_12000 Inressonable
titles ACE, JKNIX_660_1220, Reasonable
title: AKTI_JENN 118771118700 lnronsemable -11.185
title: AKTI 1_87_1_187_0 Rassonuble -11.203
title: THKI 1RN0A__
title: TEKI L_M0X_ Ressonable vina: -6, 858
Lirresseablle vina: -7, 893
title: SPHEC_ 30_03220000000 900L
title: SPOON_OO BWWNXNNN0_02____000 Roasonable vina: -Bi
title: BARA, SPOOO1100 OO2_O Unrrssceablee vina: -7.
titles SIPP_SHIXX3R0, Raasonable vina: -B. 198
Unrregonabll
vina: -7.
32

---

Supporting molecule
CIDD output
Supporting molecule
CIDD output
title: WWA_TTT_OS_____LO
6,290__AT0 1_.St_ePPstt__ lnrressnnble
Inreasenable
Rossenable vina: -10 582
Ronsonable vina: -9. 20.
vina: -10 446
vina: +8. 922
title: BGL 151300 Inreuamnel]
title: BGL JAUSE 1,9330 Ressenable
title: DP92 JUMAN JUMAN_37_4000 27_000 Inressonuble
title: DPP2 JUNA MINWW27_44R___ Reasonable
title: CDD8 JUMAN 3000000 Inreasenuble vina: +8.72
title: CIOR 12M00__30000 Rossonuble vina: -0.327
title: PIGIS JWW.A_MAA22,.0 2WWAA200_A00 Unrasaruuble vins: -6. 318
titler
Rassenablee
vina: -0. 448
Hal
26CT1_26_777_J
title: PPEA PPEAJIEMAN vina: 777
title: PPIA L -10 063
liarewenaelle le
vina: 058
vina:: -9, 583
HA
title: PRIAJAMAN 11680 lnreasenable
title: PPIAJIEMNN_LGG,, Rossonuble
title: HID METJA_L.B
title: HED 3,38__
Inresseruble
Resscnable vina:
vina: -6,
title:
1,631_
title: 1305__HM0_4115000
Uinressonbble
Rossonable
Inrossonsble
Roasenable visa: -11. 101
vina: +11.01
vina: +15, 292
vina: -8, 967
33

---

Supporting molecule
CIDD output
Supporting molecule
CIDD output
_BMMX_11_35__0
title: BOON___RMA_411300000
JRMA_145,55E
Inreasonable
Reasenable vina: 716
Inressoruble vina: -6, 725
Ressenable
vina: 434
vinor 337
title: NRIBL JEMAN 2BR (BRLO Inrmsscnable
Litle: NRIEL NRIELJEMA 28R. 208_2888__L 688 Reascnable
title: BACEZ JUMAN 76_0000 Unresscuuble
title: BACEZ
BACELLHMMN JIMMALR 460
title: SOIA L
title: SDIA LLLLL Reasenable vina: -13. 15
title: TRAR 1_IR0_1_23_00
title: TRAR 20180_1_2H400 Ressenable visa: -12 018
Unrossonable rina: -10. 997
Unrensenuble vina: +8. 953
Litles HIRA
title: HERR ROLLL_L.25 vina: 857
title: 1PP__0000_1__011 vina: -10. 001
Unrosacuuble vina: -7.717
vina: -9.845
title: IMM 10_4L Inrosacnuble
title: INMP JAMANLOO,,OO 40L40 Reascnable -10,
title: ARLS JUMAN 274, 274_58100 Inroasenuble
title: ARL2 JUNAN WTW._2745800 274_551_0 Reasonable
1_MA__113T_0
title: AKT NAT_J_NA__L_LRR
title: Rassenable
Enreasonable
Reascnable vina: -10.
Unrosacuuble vina: -10. 487
vina: -10.
vina: -10, 803
34

---

Supporting molecule
CIDD output
Supporting molecule
CIDD output
title: 28
BBt.LL1111___aaa111aa1 O
111111___aaallaaae catalytic_
Enreanonable vina: 187
Reasonahle vina: -0.241
vina: -8, 434
vina: -8, 295
tisle: BGLO7 291SJ_25_504_0
title: BELOTT 281S1_25_504. Reasonable vina: +6. 632
title: PAC BOILX_2T_4AA 846,0 Unremsonable vina:
title: PAC 20_4X,27_846_0 Reasonuble vina: 38.313
Inreosomable vinas =8. 028
title: FAC, 2T -9. 538
title: 2OW vina: -10. 042
title: P2V12 L,322_ vina: 514
title: P2E12_ 1_5220 -8. 942
title: ENGL CARAM_A1_688
title: ENGI CAAAR_
title: PARIA 1010
title: PARIA JUNNN_2_1ORL Reasonuble vina: -8. 786
Inreusomable vina: -9. -9.965
Reasonable vina: -18. 669
Unreasonable vine: 899
titll: CHIB 19990
title: CHIL, 1899911999.1 Heasenable 364
title: ESGA JUMAN 41,85700
title: KSGAJ JUMWN_411000 Reaseuuble 545
title: SDIA BOLLLLTLOO
title: SMAL 1 Ressonable vina: -11. 309
title: (XX__NNPJ__88_00 Unrmaonabble vina: -10, 767
title: BORI_HAJJR_RR.GG 18_41_8__5_0 Rensonsble vira: -12.3
Inreuamabbe vina: -10. 933
35

---

Supporting molecule
CIDD output
Supporting molecule
CIDD output
OH
HENL LOIL_
1_
GONELTHET T 1_181
Unreasenable vina: +8. -11
Reusonable
Unreasonable vina: 002
Reusorable vina: -9, 322
vina: +8. 995
title: IHM HIMAY, 10. 10_465200 Inroasenable
title: TDEP HIMAN 40. 482.0 Ressonable vina:
title: CP33 BACM 2. 164.0 Unressonable
title: CPIB BACMB 2. 464.6 Reasonable
JUMAN Unrosserable
title: WISLJIMA_JOO2L00
title: JRLMJ_AAOOLLLEE Reusnnable vina:: -10. 306
Rensnnable vina: 57.579
Unrmuaonable vina: -10. 062
vina: -+L 435
MN
title: CAT POOE__ 119,00 Inrrescnable
titles BOOK_11PP 1_911 vina: -13. 057
title: I8D0 13300
title: RSD 1_100_111000_
title: ABL2 JUMAN LB8LO
Litle: ABL2 2TMANL2TABBB Renannable vina: -11. 583
title: NOOL JWMWX22221.. Dnrmusonable vina:
title: SQOL JEMAX_2_274 Rensntable vina: -10. 424
Inrmuserable vina: 702
_MC0L_222000
title: VIBOILL3300 Unreasonable vina: 20.
VIBOLL_3300
Unreasenable
Reasonable vina: -8. 561
Reasonable
vina: +8. 786
vina: -8. 307
title: AKTL HIMAN 1,132.0
title: AKTL HIMAN 151.
title: AKTI HIMAN 1.137.0 Unreaeonable
title: ARTI JUMAN. 1_137_0
Unressernblee -10.
Reneonable vina:
Reasonable
36

---

Supporting molecule
CIDD output
Supporting molecule
CIDD output
title: PACTB PACIR_PIC_ P18E
title: PAZUR, PIG 198,
title: PINGI JABIT 5, 2.25,_ATPR ATR.ttDt
title:
Renaonable
PHKGL JABIT 296_ATPsitee
Inremecnable
Ressenable
HO
LEIEENNN2111L000 321_68800
Inreascnuble
Inrraacnable vina: -0. 44
vina: -6, 948
vima: 001
vinu: -B. 899
title: PTGIS, JUMAN 20. 20050000 Inreasonable vina: -7. 965
title: PIGIS 2OMAN20_50000 Rensomable vina: -9. 193
title: PAC 20 Unreasonable vina: -7.907
title: PAC BO 8788480 Reascnable vina: -8. 701
title: PAC 27.8X,7__86_0 Inreatenable
title: PAC 2X1EX_77_88_00 Rensrmable
title: TNKSI RMM, 118220 Sitrmannable
title: TNKS J18AJA998_11188222 Rouseruble
YANLY 2L Ronsonable vima: 991
PPIAHHINNLLLGGL Une entressonable vine: -Bi 243
PPIAJEMAN___66L
Inreasotuble
vina: -6, 991
vina: 43.435
title: PPIA 1185__ Inreasonable vina: 70.
title: PPIA 1115,0
title: P2Y12 H_MA_1 Unreasonable vina: -6.
title: NW112_HHWA_1_34R0 Reasonable vina: -10, 302
Rensonable vina: +5.457
37

---

Supporting molecule
CIDD output
Supporting molecule
CIDD output
OH
P2VI2 1,320
PAYIEJIRMNN 1_3220
title: ENG1 C1 Unreaacnable vina: -10 672
Rossonabll vina: -10.
vins: +8.31
vina: -R. 509
HO
title: MENE BACSILL 686_0 Inreasorable vina: 029
title: MENE_ JACSU 28600 Reusonable vina: -BL 622
title: SONC, ALTA0_1_6310
title: SUIC, ALTAA_1_0SLL Reasonable vinal -12 2911
Lareasonuble vina: -Bh 519
OH
title: CHIR SRRMAL 1_ 1_490_0 Inreupotable
titles CHTR, SERMA 1_490_0 Reusonable
title: SIRS JEMAN RRMAN117,5000 117_3998 Unreusenable -10,
titles STRS, JUMAN 117_08,,880000 O Reasonable
title: DYR82 164 0
DYESS JEMAN 145_560_0
title: MOSS_ARMAWGG.. SWWWW66_888_000000 Unreueruble vina: 821
Inreuporable vina: -7.898
Renscrable vina: -9, 098
Roosonable
vina: -10 535
HO
title: NRIH NEHHJJEMAW JEMAN_288AREE___
title: NRIH JWWAN_2NN_BARRRR 2288_186_0
title: BACE JEWIN_66_1600 Enreasenable vina: -0.362
title: NWE_JHHNN70GAAG RRnsonable vina: -9.947
lnremnorabll
Rensenable vina: -2, 959
vina: -8.99
title: RACES JUMAN_TO 7604066000000
tisle: RACEO JUMAN JKMAN_76_4600 766-66000 Reasenable -3t
title: SOIA L Unreasonable le
title: SOTA LOILLL Reasonable vina:
Inreasorable vina:
title: TRAI B 1_2400
title: TRAR 1H140_1_2440
title: TEAR 1H1RO_1__244 1_2340 vina: 89
title: TRAR _H110_1_234.0 vina: 836
vina: -10,
112
38

---

Supporting molecule
CIDD output
Supporting molecule
CIDD output
OH
bH
HN
title: POLL FOUR, BOWWN_B61_00000 1661_1000_0 Ilrreacttbble
title: FUL FOMMN FUWMV_861110000 1861_100000
title: HERL, 1866 Lirenaotable
title: HEBALOOLL 10 Resscnable
Ranacmuble
I Limitation on Computational Cost and Scalability
We acknowledge that our framework introduces additional computational steps due the use of LLM- based modules. Below, we provide detailed discussion of the computational overhead, empirical runtime, and justification for its practicality the context of drug discovery.
Computational Overhead of LLM Pipelines. The runtime and token-related costs associated with repeated LLM invocations such scaffold analysis, molecule generation, self-reflection, and candidate selection-can introduce non-trivial computational overhead. However, several factors mitigate this concern:
LLM Inference Efficiency Is Rapidly Improving. Recent advances model quantization, prompt batching, and optimized inference are dramatically reducing the cost of multi-step reasoning. For example, DeepSeek-V3/N V3/R1 achieves approximately 2.19 output tokens per dollar, reflecting 60-70% cost reduction compared to GPT-4 in 2023, Frameworks such vLLM [18), vTensor (36), and Splitwiser I1 further improve efficiency: vLLM enables up to 24x throughput; vTensor achieves 2x speedups with 70% less GPU memory; and Splitwiser reduces latency by 1.7x through prompt colocation. Hardware accelerators like TensorRT-LLM, also support highly scalable domain-speciff pipelines. These trends suggest that the cost of multi-step reasoning unlikely to remain a long-term bottleneck.
Empirical Runtime of Our Pipeline. To provide a more quantitative view, we report the empirical runtime of our full pipeline. Based on our experiments with the MolCraft model (used as the SBIG module in our method), under the setting of 100 sampling steps, it takes approximately 300 seconds to generate 100 molecules. For competitive baselines, previous work reports that the autoregressive method Pocket2Mol requires around 2827 seconds to generate the same number of molecules, and duffussionbbaed methods such as TargetDiff and DecompDiff require approximately 3428 seconds and 6189 seconds, respectively.
As method builds upon base SBDD models the SBIG module, only consider the additional time introduced by the LEDD module. With parallel LLM API requests, the total time required for generating 100 molecules approximately 15 minutes our experiments. Importantly, this added cost does not change the order of magnitude of the overall runtime.
The majority of this overhead stems from LLM inference latency, which is common across recent works that utilize LLMs. Nevertheless, this unlikely to remain a bottleneck as inference efficiency continues improve. For instance, recent advances in Diffusion-LLLMs 33 have demonstrated inference speeds exceeding 2000 tokens per second, suggesting the potential for up to a 20x acceleration of our pipeline in the near future.
Molecule Design Is Not Real-Time Task. Unlike latency-sensitive domains such as recommen- dation systems or autonomous control, molecule design prioritizes quality over speed. Drug discovery workflows are inherently iterative and require careful validation. While LLM-agent pipelines incur additional compute, their ability to generate higher-quality candidates with stronger binding plausi- bility aligns with the broader goal of improving experimental success rates, rather than immediate response times.
39

---

Relative Cost Compared to Wet-Lab Validation. In the broader context of drug develop- ment-which routinely spans several years and billions of dollars LLM-related inference costs are marginal. The true bottlenecks lie in experimental synthesis, biochemical testing, and preclinical validation. Even modest improvements in in silico candidate quality can translate to significant down- stream savings. Thus, the human-effort and resource reductions enabled by automated multi-step reasoning justify the modest computational overhead.
In summary, while our method introduces additional computational cost, it remains within a practical and acceptable range and does not diminish the significance, effectiveness, or applicability of our contributions. These tradeoffs will continue to improve as inference efficiency advances.
Results on more targets
tested CIDD additional targets: Isal, 1t8i and 7rpz, all known for their structural complexity and variability in ligand scaffolds. For Isal, Tubulin presents a challenging drug target because its binding sites highly dynamic, involving extensive protein-protein interfaces; furthermore, the ligand in this complex, podophyllotoxin, is a complex natural product scaffold(28). For 118i, Human DNA Topoisomerase I presents challenging drug target because its catalytic mechanism involves a highly dynamic binding site that accommodates DNA; moreover, the ligand in this initial complex camptothecin, natural product scaffold rather than a traditionally rationally designed small molecule For 7rpz, KRASGI2D is difficult to inhibit due to its high GDP/GTP affinity and absence of a nucleophilic residue near the switch II pocket, which hampers covalent strategies and limits prior chemotypes. The ligand uniquely overcomes these barriers with pyrido[4.3-dJpp -.yyimmmiee scaffold, fully engages the pocket, and achieves picomolar noncovalent inhibition with strong in vivo efficacy (35
Table 5: Comparison between MoICRAFT and CIDD on different targets
Target
Method
Success Ratio f
Vina Score! |
QED
SA t
MRR
1sal Isal
MoICRAFT
27.78%
-9.78
0.56
0.66
35.56%
CIDD
54.44%
-10.47
0.60
0.71
62.22%
1t8i
MolCRAFT
9.64%
-7.96
0.37
0.57
60.24%
1t8i
CIDD
27.71%
-8.38
0.39
0.62
68.67%
7rpz
MolCRAFT
1.35%
-9.71
0.27
0.57
1.35%
7rpz
CIDD
14.86%
-10.35
0.32
0.61
14.86%
K
Ablation Study: Reasoning Design vs. Simple Prompting
To investigate the source our framework's effectiveness, we conducted an ablation study comparing different prompting strategies. Specifically, replaced our original thain-of-thougee (CoT), domain- informed prompt with a simplified instruction: "Based on the pocket and interaction analysis, modify the original molecule."
This experiment isolates the effect of prompt engineering by using the same underlying LLM and tools, but with varying reasoning structure.
Table 6: Ablation results comparing the impact of prompt complexity on model performance.
Method
SA
MRR
Success
MolCRAFT (baseline)
0.685
58.47%
13.72%
CIDD (simple prompt)
0,678
63.69%
19.76%
CIDD (CoT prompt, ours)
0.735
81.74%
34.59%
Despite using the same L.M and external tools, the simplified prompting strategy performs sig- nificantly worse than our CoT-guided approach, and only marginally better than the MolCRAFT
40

---

baseline. This highlights that the primary gains of our method stem from structured, domain-informen informed reasoning embedded in the prompt design, rather than from tool integration alone.
L Extension: Applicability to Hit-to-Lead Optimization
While the CIDD framework was not originally designed for hit-to-lead optimization, it can be readily adapted to such scenarios. To explore this possibility, we initialized the generation process and the input to the LEDD module-usiioo using ground-truth ligands from the CrossDocked test set.
Despite these ligands already demonstrating strong synthetic accessibility and structural quality, CIDD was able to generate new molecules that exhibited further improvements in potency (as measured by docking score), synthesizability (QED and SA), and overall reasonability (MRR and success ratio).
Table 7: Evaluation of hit-to-lead potential by initializing CIDD with known binders from the CrossDocked test set.
Method
Vina Score +
QED
SA f
MRR
Success Ratio
Original Ligands CIDD (generated)
-7.53
0.473
0.738
0.8272
22.22%
-8.22
0.528
0.752
0.9136
37.04%
These results demonstrate that, even when starting from already high-quality ligands, our framework is capable discovering improved candidates through domain-aware multi-agent reasoning.
We note that fully supporting hit-to-lead tasks would require adapting the pipeline structure, modifying the of-thuot prompts, and refining the integration of design tools better align with lead optimization objectives. Nonetheless, the observed performance underscores the broader applicability of our framework and the potential of structured L.LM reasoning in more advanced drug development stages.
We plan to incorporate support for hit-to-lead optimization more formally in future work.
M Effect of Re-Docking SBIG-Generated Molecules
To ensure molecular stability and structural correctness, our pipeline includes a re-docking step following initial molecule generation. The rationale behind this design choice is that conformations directly produced by generative models may suffer from issues such as atomic clashes or physically unstable geometries, which can negatively affect downstream interaction analysis and overall pipeline reliability. This concern has also been discussed in recent work [? J. Re-docking provides a more stable and physically reasonable starting point for the LEDD module.
To evaluate whether this re-docking step is necessary, we conducted an ablation study comparing the performance of our pipeline with and without re-docking. Specifically, we initialized the LEDD module with either (1) re-docked poses or (2) the original generated conformations. All experiments used DeepSeek- V3 as the L.M..
Table 8: Ablation study evaluating the effect of re-docking versus directly using generated conforma- tions.
Method
Vina Score |
QED
SA 1
MRR 1
Success Ratio 1
Re-docking
-8.49
0.565
0.714
76.00%
29.66%
Original conformation
-8,403
0.575
0.721
76.09%
28.03%
As shown in the table, both variants achieve comparable results, with re-docking offering slight improvements in docking score and success ratio, while direct usage of the original conformation shows marginally better QED and SA. Overall, this suggests that our framework is robust to this design decision.
41

---

While re-docking provides additional geometric stability, the small performance gap indicates that either choice acceptable in useful in settings where runtime is more constrained...
practice, depending on computational tradeoffs. This flexibility may be
42

---

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Reviews:
[{"review_id": "jKtt3B9yAQ", "reviewer": "Reviewer_hXF6", "summary": "This paper accurately identifies the drawback of current 3D generative models: they often produce unreasonable molecular structures. Firstly, this paper proposes MRR as a novel metric, which evaluates molecules from diverse dimensions. Then, considering the great potential of LLMs in designing reasonable molecules and excellent binding affinity provided by 3D generative models, the authors propose CIDD to combine these complementary worlds. To be specific, a two-stage pipeline is established to generate initial candidates and further optimize them. Extensive experiments on CrossDocked2020 confirm the effectiveness of the CIDD pipeline, showing great potential of this work in drug design.", "questions": "1. Why is 3DSBDD not compared in Tab. 1? This should be a meaningful baseline.\n2. Following Q1, does the Vina score of generated molecules drop after modifications from LLMs?\n3. You claim to apply MolCRAFT in the SBIG step, while in Tab.1, the method is named as \"3DSBDD + LLM\". Why is that?", "limitations": "Yes.", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 4, "clarity": 4, "significance": 4, "originality": 4, "strengths_and_weaknesses": "Strengths:\n1. This paper aims to solve the most challenging dilemma in SBDD: the lack of reasonable structures. More surprisingly, this paper combines LLMs as a knowledge base to optimize generated molecules.\n2. Experiments are rigorously executed. Ablation studies are convincing, showing CIDD can be generalized to multiple SBDD methods and LLMs.\n3. Experiments show promising results. 3DSBDD+LLM shows overall SOTA performance on six metrics. Moreover, it generates plausible molecules with larger molecular weights, achieving the best balance between binding affinity and reasonable structures.\n4. MRR seems to be rational, considering multiple useful chemical aspects (although I am not an expert in this field). It can serve as a better metric than QED, SA, etc., boosting the entire community.\n\nWeaknesses:\n1. CIDD does not directly generate molecules with 3D conformations, which may take a little bit longer time to perform docking and other conformational tasks.\n2. No code is provided.", "was_revised": true, "final_justification": "I believe this is a good work, and maintain my score.", "rebuttal": "We sincerely thank the reviewer for their thoughtful evaluation and positive feedback on our paper. We appreciate the insightful comments and are happy to address the points raised below.\n\n## Regarding CIDD not directly generating 3D conformations and taking longer computation time\n\nWe acknowledge the reviewer’s observation that our proposed CIDD framework may incur longer computational time compared to traditional 3D structure-based drug design (SBDD) methods. While this is indeed a relevant concern, we would like to offer additional context to clarify this trade-off.\n\nDrug discovery is inherently a **time-intensive and resource-demanding process**, often spanning several years and incurring significant financial costs. A substantial portion of this effort is driven by human tasks—such as expert assessments, manual filtering, and iterative decision-making—rather than computational bottlenecks alone.\n\nCIDD explores a different trade-off: although it introduces additional computational steps, it significantly **reduces human involvement** by leveraging large language models (LLMs) to enhance chemical reasonability and drug-likeness. These are aspects that traditionally rely on deep domain expertise. In our experiments, CIDD consistently **improves the quality of generated molecules** in these dimensions, delivering results that are more chemically reasonable with less need for manual inspection.\n\nWe believe that, in practice, this translates to a net gain in efficiency: **the additional generation time is more than offset by the reduction in expert labor and manual validation efforts.**\n\nWe appreciate the reviewer’s observation and will incorporate this discussion into the limitations section of our revised manuscript.\n\n## Regarding the code \n\nWe apologize for not providing the codebase during the initial submission. We are fully committed to releasing the code and supporting reproducibility. However, due to file-upload restrictions during the rebuttal phase, we are currently unable to share the code publicly. We will release the full codebase upon acceptance.\n\nThat said, in LLM-based systems like ours, **prompt design** plays a central role in governing the model’s reasoning behavior. In the CIDD framework, prompts dictate how the LLM interprets structural context, coordinates tool usage, and executes multi-step molecular design decisions. As such, we have placed particular emphasis on reproducibility through prompt transparency.\n\nTo support this, **we provide detailed prompt templates and example model responses for each stage of the pipeline in the Appendix.** Coupled with the step-by-step descriptions in the main text, we believe these materials offer sufficient detail for readers to reproduce and understand the behavior of our system, even prior to code release.\n\nIf there are any ambiguities or questions regarding the prompts we’ve provided, we are more than happy to clarify them during the rebuttal process.\n\n## Regarding the 3D-SBDD baselines\n\nWe apologize for any confusion caused by the terminology and formatting used in the table. We understand the reviewer’s concern that “3DSBDD + LLM” may be interpreted as an individual method rather than a category.\n\nTo clarify: in our work, “3DSBDD” refers to the collection of **all baseline methods** we compare against, rather than a specific standalone approach. In particular, we do employ **MolCRAFT as our SBIG (structure-based Interation Generation) module** to generate the initial raw molecules, with the corresponding results shown in Table 1. And as shown in the table, the Vina score of generated molecules **actually becomes better compared to MolCRAFT(the second-to-last line in Tab. 1)** after modifications from LLMs. （-8.496 vs -7.783）\n\nTo avoid ambiguity, we will revise the table caption and terminology to make this categorization clearer and more precise in the updated version.\n\nThank you again for pointing this out—your careful review and valuable feedback have been very helpful in improving the clarity and rigor of our work."}, {"review_id": "pzFifHYKlE", "reviewer": "Reviewer_zFP8", "summary": "This work presents the Collaborative Intelligence Drug Design (CIDD) framework for LLM improvements to of small molecules generated by 3D structure-based drug design models. CIDD employs Chain-of-Thought (CoT) reasoning to iteratively analyze interaction profiles, propose substructural modifications, and select promising drug candidates. Additionally, the work introduces a new metric, the Molecule Reasonable Ratio (MRR) to illuminate the problem of improper aromatic ring hybridization states common to many 3D molecular generation models. The authors demonstrate that CIDD, when applied on top of pocket-based molecular generation models, achieves superior performance with respect to docking scores, traditional drug-likeness metrics like SA and QED score, and MRR compared to all baselines.", "questions": "1. Similarity is listed as a metric, but is it defined as the similarity of CIDD-generated candidates to the initial molecule or to each other?\n2. Has CIDD been evaluated on hit-to-lead tasks? That is, given a reasonable binder posed in the protein pocket, can CIDD still generate superior molecules with respect to both potency and synthesizability?\n3. Given an initial SBIG molecule, what is the time required for a single LEDD candidate to be generated by the LLM? Would it be viable to generate a screening set of, say, 1,000 or 10,000 molecules via LEDD for binding assays or more rigorous computational binding affinity calculations? \n4. Are SA scores re-scaled for the results? The interpretation of the original SA score (between 1 and 10) was that higher scores meant more difficulty.", "limitations": "Yes", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 2, "strengths_and_weaknesses": "Strengths:\n1. The interaction and design steps encourage the LLM to make decisions grounded in chemical intuition by making explicit the various interactions between an initial docked ligand and the protein. The authors additionally demonstrate that interaction profiling is necessary comparing to PDB-formatted receptor data.\n2. By design, CIDD is compatible with most 3-dimensional molecular generators that produce both a molecule and its pose in a target receptor.\n3. The explicit rationale outlined in LEDD allows for clear interpretability in molecular design and optimization.\n4. CIDD capitalizes on LLM tendencies to generate valid molecules to greatly improve MRR and other synthesizability metrics compared to initial designs.\n\nWeaknesses:\n1. In the experiments described, only 10 molecules are generated per protein pocket, making it difficult to draw conclusions about molecular diversity between LLM backends.\n2. Interaction profiles calculated by the interaction analysis module rely on re-docking SBIG-generated molecules to the pocket, which can sometimes generate badly posed molecules that constitute poor starting points for optimization with LEDD.\n3. Despite CIDD being a 2D molecular generation framework, the experiments omit comparisons against 2D generation methods guided by docking scores such as Cretu et al. (2024), Koziarski et al. (2024), Seo et al. (2024), Loeffler et al. (2024), and Swanson et al. (2025). \n\n\nReferences\n\n1. Cretu, M., Harris, C., Igashov, I., Schneuing, A., Segler, M., Correia, B., … Liò, P. (2025). SynFlowNet: Design of Diverse and Novel Molecules with Synthesis Constraints. arXiv [Cs.LG]. Retrieved from http://arxiv.org/abs/2405.01155\n2. Koziarski, M., Rekesh, A., Shevchuk, D., van der Sloot, A., Gaiński, P., Bengio, Y., … Batey, R. A. (2024). RGFN: Synthesizable Molecular Generation Using GFlowNets. arXiv [Physics.Chem-Ph]. Retrieved from http://arxiv.org/abs/2406.08506\n3. Loeffler, H.H., He, J., Tibo, A. et al. Reinvent 4: Modern AI–driven generative molecule design. J Cheminform 16, 20 (2024). https://doi.org/10.1186/s13321-024-00812-5\n4. Seo, S., Kim, M., Shen, T., Ester, M., Park, J., Ahn, S., & Kim, W. Y. (2025). Generative Flows on Synthetic Pathway for Drug Design. arXiv [q-Bio.BM]. Retrieved from http://arxiv.org/abs/2410.04542\n5. Swanson, K., Liu, G., Catacutan, D. B., McLellan, S., Arnold, A., Tu, M. M., … Stokes, J. M. (2025). SyntheMol-RL: a flexible reinforcement learning framework for designing novel and synthesizable antibiotics. bioRxiv. doi:10.1101/2025.05.17.654017", "was_revised": true, "final_justification": "The approach is a simple yet effective way to obtain interpretable improvements to molecules generated by 3D generation methods. The authors adequately addressed the concerns and provided evidence that the model was capable of generating improvements even for ground-truth ligands.", "rebuttal": "We sincerely thank the reviewer for their thoughtful evaluation and positive feedback on our paper. We appreciate the insightful comments and are happy to address the points raised below.\n\n## Regarding the diversity\n\nThank you for pointing out the diversity metric. Initially, we included diversity in the main results table, but later decided to remove it as it did not offer significant insights—most methods showed only marginal differences in this aspect. For completeness, we provide the diversity scores of the baseline models and CIDD below:\n\n| Method       | LiGAN | AR   | Pocket2Mol | TargetDiff | IPDiff | DecompDiff | TAGMol | DrugGPS | MolCraft | CIDD | CIDD-100 |\n|--------------|-------|------|------------|-------------|--------|------------|--------|---------|----------|------|-----------|\n| Diversity    | 0.802 | 0.836| 0.866      | 0.890       | 0.887  | 0.877      | 0.893  | 0.908   | 0.870    | 0.867| 0.871     |\n\n\nWe also evaluated the diversity when generating 100 molecules per target. As shown, CIDD achieves diversity comparable to its baseline model, MolCraft. Moreover, increasing the number of generated molecules from 10 to 100 does not significantly affect the average diversity, and diversity is not a concern of CIDD framework.\n\n## Regarding re-docking SBIG-generated molecules\n\nWe appreciate the reviewer’s insightful comment on the use of re-docking in our pipeline. The reason we perform re-docking, rather than directly using the generated molecular conformations, is that the original conformations produced by generative models may contain issues—such as atomic clashes or physically unstable geometries—that can negatively impact both interaction analysis and subsequent steps. This issue has been discussed in recent paper[1]. Re-docking helps ensure that we begin from a more stable and physically reasonable pose.\n\nThat said, we fully agree with the reviewer that it is important to test whether this re-docking step is necessary. To this end, we conducted an ablation study where we directly used the originally generated conformations without re-docking. We use deepseek-v3 as our LLMs. The results are shown below:\n\n| Method     | Vina Score ↓ | QED ↑  | SA ↑   | MRR ↑   |  Success Ratio ↑ |\n|------------|--------------|--------|--------|---------|------------|\n| re-docking | -8.49      | 0.565 | 0.714 | 76.0%   | 29.66%         |\n| ori conformation | -8.403       | 0.575  | 0.721  | 76.09%  | 28.03%     |\n\nAs shown, the performance differences are relatively small, suggesting that our framework is robust to this design choice. We thank the reviewer again for the valuable suggestion and will include this analysis and discussion in the appendix of the revised manuscript.\n\n## Regarding 2D generation methods guided by docking scores\n\nThank you for pointing us to these works. We appreciate the references and agree that those 2D molecular generation is an important and active line of research.\n \nWe would like to clarify that our method is fundamentally different in formulation from the works cited. Specifically, CIDD is not an optimization framework: we do not use reinforcement learning, docking-score-based fine-tuning, or iterative optimization guided by oracle feedback. Instead, our framework focuses on repairing and improving imperfect 3D SBDD-generated molecules by preserving key protein-ligand interactions while enhancing drug-likeness and synthesizability—without relying on explicit reward signals or gradient guidance.\n \nMoreover, the referenced methods are not evaluated on the CrossDocked dataset, which we adopt as our main benchmark to ensure consistency with our chosen baselines. Likewise, these methods are not included as baselines in the prior works we compare against. As a result, a fair and meaningful comparison would require non-trivial adaptation and retraining of those methods in our setting—especially considering that some of them are specifically designed for narrow domains such as antibiotic discovery—which is unfortunately infeasible within the limited rebuttal period. In addition, several of the referenced methods can be computationally intensive, which is why they are typically evaluated only on a small number of selected targets.\n\nImportantly, we view CIDD as complementary to these optimization-based methods. In fact, many of them could be incorporated into our pipeline as components within the SBIG (Structure-Based Initial Generation) stage. Our framework is modular by design, and can serve as a downstream interaction-preserving refinement module, enhancing the outputs of other generative methods that may not explicitly account for protein context or structural plausibility.\n\nWe thank the reviewer again for bringing these methods to our attention. We will cite the relevant works and include a discussion of their methodological differences, as well as their relationship to CIDD, in the Related Work section of the revised manuscript.\n\n## Regarding similarity metrics\n\nThank you for pointing out the metric on similarity. Yes, we do evaluate the similarity between the raw molecule from the SBIG module and the final molecule generated by CIDD. This choice is intentional and aligned with the design goal of the LEDD step: to make minimal but meaningful modifications that improve chemical quality while preserving the structural basis provided by the SBDD model.\n\nSpecifically, we treat the SBIG-generated molecule as a structure-based proposal that carries important protein-ligand interaction information. Rather than generating a completely new molecule, which may disrupt these interactions, we aim to refine the initial structure in a way that retains its interaction intent. Therefore, a higher similarity reflects a successful case where the molecule is improved in terms of drug-likeness or synthesizability, while maintaining the original design.\n\nThis design choice also supports a data-centric view. By producing pairs of molecules with small structural differences but large differences in quality, our framework naturally generates valuable data for future training of optimization models. These minimally perturbed, property-improved pairs can serve as useful examples for learning structure-to-property relationships.\n\nWe will clarify the motivation and implications of this similarity metric in the revised version of the manuscript. \n\n## Regarding hit-to-lead tasks\n\nWe thank the reviewer for highlighting the potential of applying the CIDD framework to hit-to-lead optimization. Although CIDD was not originally designed for this purpose, it can indeed be adapted to such scenarios. To explore this, we initialized the generation process—and the input to the LEDD module—using ground-truth ligands from the CrossDocked test set. As shown in the following table, despite these ligands already exhibiting good synthetic accessibility and a high reasonable ratio, CIDD was still able to generate molecules with further improvements in potency, synthesizability, and structural reasonability, demonstrating its effectiveness and potential in hit-to-lead optimization.\n\n|          | Vina Score ↓ | QED ↑  | SA ↑   | MRR ↑   |  Success Ratio ↑ |\n|-----------|------------|------|------|---------|----------------|\n| Ori ligands  | -7.53      | 0.473 | 0.738 | 0.8272  | 22.22%         |\n| CIDD gen| -8.22      | 0.528 | 0.752 | 0.9136  | 37.04%         |\n\nNotably, as we mentioned, the original CIDD (LEDD) framework was not specifically designed for hit-to-lead optimization tasks. Fully supporting such applications would require adjustments to the pipeline design, modifications to the chain-of-thought prompts, and changes in tool integration. Nonetheless, the current results already demonstrate **the potential of our proposed domain model and LLM collaboration paradigm in broader scenarios such as hit-to-lead**. We will incorporate this direction into the discussion of future work. \n\nWe sincerely thank the reviewer again for the valuable feedback.\n\n## Regarding generating 1,000 or 10,000 molecules\n\nThank you for the thoughtful question. We confirm that the CIDD pipeline supports large-scale candidate generation, including sets of 1,000 or even 10,000 molecules.\n\nThe end-to-end generation time for a single molecule—including interaction parsing, prompt construction, LLM inference, and generating for N=5 new designs—is approximately 5 minutes when run sequentially. However, the pipeline is **inherently parallelizable**. When deployed via cloud-based API services (such as OpenAI or DeepSeek), there is virtually no limit on parallelization, assuming adequate API throughput. In practice, this allows for generating 1,000 molecules **within just a few minutes** using parallel asynchronous requests.\n\nCompared to the time required for downstream experiments such as molecular dynamics simulations, or wet-lab synthesis and validation, this generation time remains extremely efficient, and does not constitute a computational bottleneck in the overall drug discovery pipeline.\n\n## Regarding SA scores\n\nThank you for the question. Yes, we re-scaled the original synthetic accessibility (SA) scores for consistency and interpretability in our evaluation.\n\nThe original SA score ranges from 1 (easy to synthesize) to 10 (difficult to synthesize). To make the interpretation consistent with other normalized scores (where higher is better), we applied the following linear transformation:\n\n\n    sa_norm = round((10 - sa) / 9, 2)\n\n\nThis maps the SA score to a normalized range between 0 and 1, where a higher value indicates easier synthesis. This rescaled score was used for reporting purposes in our tables and analysis."}, {"review_id": "MTXkkgGUrY", "reviewer": "Reviewer_M6aF", "summary": "The authors tackle a central shortcoming of contemporary structure-based generative models: though 3D methods (e.g., diffusion or autoregressive approaches like MolCRAFT or Pocket2Mol) can propose molecules that fit tightly into protein pockets, they frequently yield chemically implausible scaffolds--strained rings, partial unsaturation or over-fused polycycles--that fall outside the realm of drug-like chemical space . To quantify this disconnect, they introduce the Molecular Reasonability Ratio (MRR), a rule-based metric that flags rings whose non-aromatic, non-saturated cores exhibit mixed sp^2/sp^3 hybridization (Cyclohexene- or diene-like motifs), revealing that existing 3D models achieve only ~38–59% MRR versus ~86% for FDA-approved drugs .\n\nTo bridge the gap between geometric complementarity and chemical viability, the paper presents CIDD, a two-stage, modular pipeline that couples:\n\n1. SBIG, which uses any off-the-shelf 3D model to propose “raw” interaction-optimized scaffolds.\n2. LEDD, which employs Chain-of-Thought prompts in a sequence of LLM-powered modules--Interaction Analysis, Design, Reflection and Selection--to iteratively refine those scaffolds into drug-like candidates .\n\nOn the CrossDocked2020 benchmark (100 test pockets × 10 molecules each), CIDD more than doubles the prior best combined success rate (binding + drug-likeness) from 15.7% to 34.6%, while boosting QED (≈0.50 to 0.58), SA score (≈0.68 to 0.74) and MRR (≈58–67% to 81.7%) without sacrificing docking affinity (mean Vina ≈–8.5) . These results demonstrate that integrating precise 3D interaction proposals with LLM-driven chemical reasoning can yield molecules that are both potent binders and structurally rational--paving the way for more interpretable, expert-inspired AI workflows in early-stage drug discovery.", "questions": "How well does CIDD generalize to diverse or challenging targets?\n\nCan you provide detailed computational‐cost metrics?\n\nWhat is the synthetic feasibility of your top candidates?", "limitations": "Yes", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "## Strengths\n\nBy coupling any off-the-shelf 3D scaffold generator (SBIG) with an LLM-driven refinement loop, CIDD leverages both precise pocket fitting and human-like chemical intuition in a single pipeline.\n\nThe Molecular Reasonability Ratio (MRR) provides a clear, rule-based metric to expose and quantify chemically implausible scaffolds--an issue often hidden when optimizing purely for docking scores.\n\nOn the CrossDocked2020 benchmark, CIDD more than doubles the combined binding-and-drug-likeness success rate (from ~15.7 % to ~34.6 %) while also boosting QED, SA, and MRR scores without sacrificing docking affinity.\n\nThe clear SBIG→LEDD separation means practitioners can plug in newer 3D backbones or more advanced LLMs without retraining the entire system, facilitating future updates and improvements.\n\nChain-of-Thought prompts generate a transparent, step-by-step record—fragment analyses, flagged liabilities, planned edits, reflective assessments—that mirrors a medicinal chemist’s reasoning and supports human review.\n\n## Weaknesses\n\nRepeated LLM invocations per scaffold (analysis, design, reflection, selection) risk prohibitive runtimes and token-usage costs when screening large libraries, yet no wall-clock times, token counts or cost estimates are provided.\n\nValidation is confined to a single in silico benchmark (CrossDocked2020) with relatively narrow chemical diversity. It remains unknown how CIDD performs on more challenging targets or proprietary datasets with different chemotype distributions.\n\nWhile Chain-of-Thought prompting enhances interpretability, it may also introduce brittleness: small rephrasings or LLM updates could unpredictably alter design outcomes. The paper lacks any analysis of prompt sensitivity or strategies for automated prompt optimization.", "was_revised": true, "final_justification": "Authors did a good job on the rebuttal and solving my concerns. I believe this paper should be accepted.", "rebuttal": "We sincerely thank the reviewer for their thoughtful evaluation and positive feedback on our paper. We appreciate the insightful comments and are happy to address the points raised below.\n\n## Regarding costs\n\nWe acknowledge the reviewer’s concern regarding the runtime and token-related costs associated with repeated LLM invocations (e.g., scaffold analysis, molecule design, reflection, and selection). Indeed, LLM-agent pipelines—like ours, and as seen in many other LLM-agent use cases—can incur non-trivial computational overhead. However, we would like to offer the following clarifications:\n\n### Cost Is Becoming a Lesser Barrier\n\nThe cost of multi-step LLM reasoning is rapidly declining thanks to advances like quantization, batching, and optimized inference engines. For example, DeepSeek-V3/R1 supports inference at $0.55M/input and $2.19M/output tokens—a 60–70% reduction from GPT-4’s 2023 pricing. Efficiency-focused frameworks such as vLLM[1], vTensor[2], and Splitwiser[3] further accelerate inference: vLLM achieves up to 24× higher throughput, vTensor offers 2× speedups with 70% less GPU memory, and Splitwiser reduces latency by 1.7× via co-located prompt and generation. Combined with hardware accelerators like TensorRT-LLM, these developments make domain-informed, multi-step LLM workflows increasingly practical and scalable. For example, with inference frameworks like vLLM, the DeepSeekV3 model can be deployed on 8×A100 80GB GPUs.\n\nIn addition, attention-level optimizations have significantly accelerated LLM inference: FlashAttention‑2 achieves up to 4–5× speedup over standard attention [4], DuoAttention reduces memory usage by 2.55× and speeds up decoding by 2.18× [5], and MInference delivers up to 10× acceleration in pre-filling long contexts [6].\n\nThese examples confirm that the computational and token overhead associated with multi-step LLM pipelines is **rapidly shrinking** and is unlikely to pose a significant obstacle in the foreseeable future.\n\n[1] Kwon, Woosuk, et al. \"Efficient memory management for large language model serving with pagedattention.\" Proceedings of the 29th symposium on operating systems principles. 2023.\n[2] Xu, Jiale, et al. \"vTensor: Flexible Virtual Tensor Management for Efficient LLM Serving.\" CoRR (2024).\n[3] Aali, A., Cardoza, A., & Capo, M. (2025). Splitwiser: Efficient LM inference with constrained resources. arXiv preprint arXiv:2505.03763.\n[4] Dao, T. FlashAttention‑2: Faster Attention with Better Parallelism and Work Partitioning. arXiv preprint arXiv:2307.08691.\n[5] Xiao, G. et al. DuoAttention: Efficient Long‑Context LLM Inference with Retrieval and Streaming Heads. In International Conference on Learning Representations (ICLR), 2025. arXiv preprint arXiv:2410.10819.\n[6] Jiang, H. et al. MInference 1.0: Accelerating Pre‑filling for Long‑Context LLMs via Dynamic Sparse Attention. In Conference on Neural Information Processing Systems (NeurIPS), 2024. arXiv preprint arXiv:2407.02490. \n\n\n### Traditional Drug Development Is Far More Costly\n\nDrug development is slow and costly, often taking years and billions of dollars to bring a single drug to market. The true bottleneck lies not in design time, but in the lengthy, expensive trial-and-error process of testing candidate molecules. Unlike real-time tasks like recommendation or autonomous driving, drug discovery prioritizes precision over speed. What matters is generating molecules with higher chances of experimental success—a goal our framework supports by improving the quality and viability of SBDD-generated candidates.\n\nOur framework does not replace traditional SBDD models but **accelerates their downstream refinement** through a structured, multi-agent pipeline that preserves key interactions while improving structural plausibility. **Although multi-step reasoning introduces some computational overhead, the overall time savings from eliminating manual, expert-driven intervention far outweighs the additional inference cost**—especially in large-scale or high-throughput scenarios.\n\n### Case Study: Runtime and Token Usage\nTo provide a concrete estimate, we profiled a representative design iteration with the following statistics:\n\nAverage runtime per design : ~1 minutes;  Total token usage per design: ~10000 tokens\n\nIn this paper for each initial molecules we generate 5 designs sequentially so the cost would be 5 times. This can be adjusted based on requirements. In practice we found that one design is able to create molecules with better drug likeness and syteh\n\nImportantly, these operations are **highly parallelizable**, and large-scale molecules screening can be executed efficiently in batch across multiple machines, and still support generating large scale number of molecules in reasonable time.\n\nIn summary, we thank the reviewer for raising this important point. We acknowledge that cost is a common concern for frameworks involving multiple LLM agents, and we will include a discussion of this limitation in the revised version of the paper. That said, we believe recent advances in model efficiency and inference infrastructure are rapidly reducing this barrier, as discussed above.\n\n## Regarding more challenging targets\n\nWe thank the reviewer for the helpful suggestion to evaluate CIDD on more challenging targets with diverse chemotype distributions. In response, we tested CIDD on additional targets: 1sa1, 1t8i and 7rpz, all known for their structural complexity and variability in ligand scaffolds. For 1sa1, Tubulin presents a challenging drug target because its binding sites are highly dynamic, involving extensive protein–protein interfaces; furthermore, the ligand in this complex, podophyllotoxin, is a complex natural product scaffold[1]. For 1t8i, Human DNA Topoisomerase I presents a challenging drug target because its catalytic mechanism involves a highly dynamic binding site that accommodates DNA; moreover, the ligand in this initial complex is camptothecin, a natural product scaffold rather than a traditionally rationally designed small molecule[2]. For 7rpz, KRASG12D is difficult to inhibit due to its high GDP/GTP affinity and absence of a nucleophilic residue near the switch II pocket, which hampers covalent strategies and limits prior chemotypes. The ligand uniquely overcomes these barriers with a pyrido[4,3-d]pyrimidine scaffold, fully engages the pocket, and achieves picomolar noncovalent inhibition with strong in vivo efficacy[3].\n\n\nResults on 1sa1:\n\n|  | Vina Score ↓ | QED ↑  | SA ↑   | MRR ↑   |  Success Ratio ↑ |\n|-----------|--------|------|------|---------|--------|\n|MolCRAFT| -9.78 | 0.56 | 0.66 | 35.56% |27.78% |\n|CIDD | -10.47 | 0.60 | 0.71 | 62.22%  | 54.44% |\n\nResults on 1t8i:\n\n|   | Vina Score ↓ | QED ↑  | SA ↑   | MRR ↑   |  Success Ratio ↑ |\n|-----------|-------|------|------|---------|-------|\n| MolCRAFT| -7.96| 0.37 | 0.57 | 60.24%  | 9.64%|\n| CIDD| -8.38 | 0.39 | 0.62 | 68.67%| 27.71%|\n\nResults on 7rpz:\n\n| | Vina Score ↓ | QED ↑  | SA ↑   | MRR ↑   |  Success Ratio ↑ |\n|--------|------------|------|------|---------|-----------|\n| MolCRAFT | -9.71 | 0.27 | 0.57 | 1.35% | 1.35% |\n| CIDD | -10.35 | 0.32 | 0.61 | 14.86%  | 14.86% |\n\n\nAs shown, CIDD achieves substantially higher success ratios than the MolCRAFT baseline on these targets, even when the initial molecules generated by the basele model are of low quality. This demonstrates CIDD’s ability to be effective for chemically complex and challenging protein targets.\n\nWe thank the reviewer again for this insightful suggestion, and we will include these new results and discussions in the revised paper.\n\n[1] Ravelli, R.B.G. et al. Nature 2004, 428, 198–202.\n\n[2] Staker, B.L. et al. J. Med. Chem. 2005, 48, 2336–2345.\n\n[3] Wang, X., et al. J. Med. Chem. 65.4 (2021): 3123–3133.\n\n## Regarding prompt sensitivity \n\nWe thank the reviewer for highlighting the importance of prompt sensitivity.\n\nFirst, as shown in Table 2(a), even when using different large language models (GPT-4o, GPT-4o-mini, DeepSeek-V3), the outcome metrics remain fairly consistent. This suggests that our framework is robust and not overly sensitive to the choice of underlying LLM. In other words, model updates or substitutions are unlikely to cause failures or unpredictable behaviors in the system.\n\n### Table 2(a):Different LLM Backends in CIDD\n\n| LLM | Vina ↓  | MRR ↑    | Similarity ↑ |\n|---------------|---------|----------|----------------|\n| -   | -7.78   | 58.47%   | -  |\n| GPT-4o-mini| -8.29   | 80.02%   | 0.220 |\n| GPT-4o  | -8.50   | 81.37%   | 0.296 |\n| DeepSeek-v3 | -8.49   | 76.00%   | 0.379|\n\nTo further assess prompt sensitivity, we conducted an additional experiment in which we asked ChatGPT to automatically rephrase the core design prompts, while preserving their original intent.  We use deepseek-v3 as our LLMs.\n\nAs shown in the following table, the performance remained largely stable across the two prompt versions, indicating that the method is not highly sensitive to moderate rephrasings in Chain-of-Thought design instructions.\n\n|Case| Vina Score ↓ | QED ↑  | SA ↑   | MRR ↑   |  Success Ratio ↑ |\n|-----|---|------|------|------|-----|\n|ori prompt| -8.49| 0.565 | 0.714|76.0%|29.66%|\n|new prompt| -8.43| 0.581 | 0.728 |78.5%|29.67%|\n\nThese results suggest that while prompt formulation can influence LLM responses, **CIDD remains robust under reasonable variations in prompt phrasing.**\n\n## Regarding synthetic feasibility of our top candidates\n\nAs shown in Table 1, the SA score reflects the synthetic feasibility of the generated molecules, and our method improves this metric compared to baselines."}, {"review_id": "boYpQ14RP1", "reviewer": "Reviewer_DBAy", "summary": "Through this paper, the authors introduce the Collaborative Intelligence Drug Design (CIDD) framework. The CIDD framework employs CoT reasoning of LLMs to generate 3D molecules that are compatible with protein pockets and exhibit favorable chemical properties. The authors also propose a new metric, the Molecule Reasonable Ratio (MRR), to measure structural rationality of generated molecules.", "questions": "- The proposed CIDD framework heavily relies on the backbone LLM model, and is therefore inevitably vulnerable to hallucination or reasoning errors. Are there potential ways to overcome or alleviate this issue?\n- Please see *Weaknesses* above for my main concerns about this paper.", "limitations": "The authors did not discuss the potential negative societal impact. The limitations of this work is very briefly discussed (in one sentence). However, there seem to be a lot of limitations that can possibly invoke interesting views to the readers. For example, the performance of the CIDD framework heavily relies on the LLM model, and it can show degraded performance with less powerful or open-source LLMs. Moreover, how to overcome or alleviate the hallucination of the backbone LLM can be an interesting discussion.", "rating": 3, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 1, "originality": 1, "strengths_and_weaknesses": "**Strengths:**\n- The writing and figures are clear and overall easy to follow.\n- The direction of integrating SBDD with LLMs is interesting.\n- The proposed CIDD framework is modular and interpretable.\n\n**Weaknesses:**\n- The authors did not provide the codebase to reproduce the results.\n- The main weakness of this paper is its weak novelty. The framework proposed in this paper relies on many external rule-based libraries. Rather than generating molecules through the collaboration of SBDD model and LLM, it seems to be a heuristic framework through the combination of several rules such as BRICS, external chemical analysis tools, domain-knowledge prompts, etc. For example, the paper uses multiple modules for the LLM-enhanced drug designer (LEDD) step, and there is no in-depth ablation study for each module.\n- In addition, the use of external experts or oracles is not a fair comparison with existing SBDD models. In the field of drug discovery, sample efficiency, i.e., oracle call budget, is very important [1], and CIDD uses more oracles such as the external interaction analysis tool than existing models.\n- Furthermore, the computational cost of the proposed method can be high due to the repeated cycles with many external modules and multiple LLM inference steps. However, there is no discussion or comparison with existing SBDD methods regarding computational efficiency.\n- The justification for the new metric, MRR, seems to be insufficient. The results of how MRR is measured for known drugs are required. Furthermore, as also mentioned in the paper, MRR uses heuristic rules, and it is not explained how these rules are supported by existing chemical knowledge. For example, in Algorithm 1, why are carbonyl and imine groups excluded in calculating MRR? A detailed explanation should be included.\n\n---\n**References:**\n\n[1] Gao et al., Sample Efficiency Matters: A Benchmark for Practical Molecular Optimization, NeurIPS Track on Datasets and Benchmarks, 2022.", "was_revised": true, "final_justification": "I considered author rebuttal and read other reviewer's reviews. My concerns on the novelty and other limitations of the proposed method were not fully resolved during the rebuttal, therefore I keep my original score.", "rebuttal": "We sincerely thank the reviewer for their thoughtful feedback. Below are our responses to the main points:\n\n## Regarding Code\nWe apologize for not including the code in the initial submission. Due to file-upload restrictions during the rebuttal phase, we cannot share it publicly now but are fully committed to open-sourcing upon acceptance. In LLM-based systems like ours, prompt design is central to model behavior. To support reproducibility, **we provide detailed prompt templates and example outputs in Appendix**, and step-by-step pipeline descriptions (main text). **If any part of the prompt design is unclear, we’re happy to clarify during the rebuttal.**\n\n## Regarding Novelty and Contribution\nWe thank the reviewer for their thoughtful comments. Our contribution may have been misunderstood as a heuristic combination of tools, but in fact, **the LLM plays a central and decisive role** in our framework, reasoning over structured molecular knowledge and coordinating all key design decisions. Rather than a simple toolchain, our method is definitely a novel **collaboration between LLMs and 3D SBDD models**, where the LLM drives the refinement process with domain-informed reasoning.\n\n### Integrating LLMs into SBDD is Non-Trivial and Scientifically Novel\nLLMs are not inherently designed to understand molecular graphs, 3D structures, or protein-ligand interactions. Their reasoning is based on symbolic text, not geometry or chemistry. Therefore, applying LLMs to tasks that require spatial reasoning and  chemistry knowledge—as is essential in SBDD—is a major challenge. Our contribution lies in bridging this gap through a carefully designed pipeline. \n\n### Ablation Study: Effectiveness Comes from LLM Reasoning Design, not external rule-based libraries.\n\nFollowing the reviewer’s suggestion, we conducted an ablation study to further support the non-triviality and novelty of our LLM-SBDD integration. Specifically, we replaced our original chain-of-thought, domain-informed prompts with a simple instruction: “Based on the pocket and interaction analysis, modify the original molecule.”\n\n| Method| SA ↑ | MRR ↑| Success ↑ |\n|-------------|--------|---------|----------|\n| MolCRAFT|0.685|58.47%|13.72%|\n| CIDD (simple prompt)|0.678|63.69%|19.76%|\n| CIDD (CoT prompt)|0.735 |81.74%| 34.59%|\n\nNotably, despite using the same LLM and tools, the simplified prompting strategy performs significantly worse than the CoT-guided version and is only marginally better than the MolCRAFT baseline. **This underscores that the effectiveness of our framework lies not in tool usage, but in domain-informed, structured reasoning via prompt design.**\n\n**We are prepared to conduct additional ablation studies should reviewers request further clarification or analysis.**\n\n### The Core Novelty: A Domain-Guided, LLM-Centered Framework\n\nOur key innovation is a framework where the LLM serves not just as a molecule generator, but as a decision-making coordinator that mimics expert reasoning in structure-based drug design (SBDD). We encode domain knowledge into prompts, allowing the LLM to interpret chemical and structural constraints in a human-like, structured way. Specialized tools (e.g., PLIP, RDKit) handle atomic-level tasks like interaction extraction or fragment generation, while the LLM focuses on high-level reasoning and decision-making. This division of labor plays to each component’s strength and offsets the LLM’s limitations with raw 3D geometry.\n\nThis mirrors how medicinal chemists operate—translating 3D interactions into symbolic reasoning before modifying structures. Our framework replicates this workflow, refining SBDD outputs into drug-like molecules without relying on oracle-based optimization.\n\n## Regarding oracle budget\n\nWe thank the reviewer for raising this concern and offer the following clarifications:\n\n### CIDD is not an oracle-based optimization framework.\nOur goal is to preserve the 3D interaction patterns from the initial SBDD output while improving drug-likeness (e.g., stability, synthesizability, plausibility). We do not query any drug-likeness-related oracles (e.g., QikProp, MRR, SA, QED) during generation; these metrics are used only post hoc for evaluation. This clearly distinguishes our method from optimization pipelines that rely on repeated oracle feedback.\n\n### PLIP enables interaction representation—not optimization.\nSince LLMs cannot directly process 3D structures, we use PLIP to extract symbolic interaction patterns (e.g., hydrogen bonds, hydrophobic contacts) from the SBDD-generated molecule. These are passed to the LLM in natural language to support reasoning. PLIP is used solely as a structural-to-symbolic translator—not a scoring or feedback oracle.\n\n### Relations with existing SBDD model\nAs we are not oracle based optimization method, we believe it is still a fair comparasion with existing SBDD model. And actually we are not \"competitive\" with existing SBDD models. Instead, we are trying to build upon, or to say help them to solve unresolved problems such as structure reasonability and syntehzi ability.\n\n## Regarding computational costs\nWe acknowledge the reviewer’s concern regarding the runtime and token-related costs associated with repeated LLM invocations (e.g., scaffold analysis, molecule design, reflection, and selection). Indeed, LLM-agent pipelines—like ours, and as seen in many other LLM-agent use cases—can incur non-trivial computational overhead. However, we would like to offer the following justifications:\n\n### Cost Is Becoming a Lesser Barrier\nThe cost of multi-step LLM reasoning is rapidly shrinking thanks to advances in quantization, batching, and optimized inference. DeepSeek-V3/R1 achieves ~$0.55M/input and ~$2.19M/output tokens—a 60–70% drop from GPT-4’s 2023 cost. Frameworks like vLLM[1], vTensor[2], and Splitwiser[3] further boost efficiency: vLLM offers up to 24× throughput, vTensor gives 2× speedups with 70% less GPU memory, and Splitwiser reduces latency by 1.7× via prompt-generation colocation. With accelerators like TensorRT-LLM, domain-aware multi-step pipelines are becoming increasingly scalable. Overall, **computational overhead is shrinking** and is unlikely to be a major limitation in the future.\n\n[1] Kwon et al., SOSP 2023 — PagedAttention for efficient memory management in LLM serving.\n\n[2] Xu et al., CoRR 2024 — vTensor: virtual tensor management for efficient LLM inference.\n\n[3] Aali et al., arXiv 2025 — Splitwiser: latency-optimized LLM inference under resource constraints.\n\n### Traditional Drug Development Is Far More Costly\n\nDrug development is slow and costly, often taking years and billions of dollars to bring a single drug to market. The true bottleneck lies not in design time, but in the lengthy, expensive trial-and-error process of testing candidate molecules. Unlike real-time tasks like recommendation or autonomous driving, drug discovery prioritizes precision over speed. What matters is generating molecules with higher chances of experimental success—a goal our framework supports by improving the quality and viability of SBDD-generated candidates.\n\nOur framework does not replace traditional SBDD models but **accelerates their downstream refinement** through a structured, multi-agent pipeline that preserves key interactions while improving structural plausibility. **Although multi-step reasoning introduces some computational overhead, the significant reduction in human effort more than compensates—especially in large-scale or high-throughput scenarios.**\n\nWe thank the reviewer for raising this point. **We will discuss this tradeoff in the Limitations section and note that ongoing advances in LLM efficiency continue to reduce these barriers.**\n\n## Regarding new metric, MRR\n\nDefining strict criteria for drug-likeness remains elusive, yet long-standing empirical evidence has delineated recurrent structural motifs. In cyclic scaffolds, extranuclear electron distributions can enforce stability when specific electronic configurations, notably continuous sp² hybridization, are satisfied[1, 2]. Carbonyl and imine substituents often conform to sp² hybridization yet fail to establish a fully delocalized π-system around the ring—a feature frequently observed in approved drugs[3, 4, 5]. The MRR formalizes these principles, accurately distinguishing drug-like from non-drug-like molecules: **85.90% of FDA-approved small molecules comply with MRR criteria**, substantially exceeding that of current 3D-SBDD models. By focusing on ring systems, present in **83.31% of approved drugs**, MRR captures a fundamental chemical constraint: stable rings adopt either fully saturated or fully aromatic configurations, thereby conferring thermodynamic stability, synthetic tractability, and favorable interaction profiles—properties systematically exploited in both natural products and rationally designed therapeutics.\n\n[1] Morsch et al., Organic Chemistry, LibreTexts, Ch. 15.2.\n\n[2] Aromatic Compounds, ChemistryTalk, accessed 30 Jul 2025.\n\n[3] Saudagar PS et al., Biotechnol Adv, 26(4):335–351, 2008.\n\n[4] Poupaert JH et al., J Med Chem, 27(1):76–78, 1984.\n\n[5] Stezowski JJ, J Am Chem Soc, 98(19):6012–6018, 1976.\n\n## Regarding hallucination or reasoning errors.\n\nWe thank the reviewer for raising this important concern. We mitigate hallucination and reasoning errors via:\n\nControlled decoding (low temperature) to reduce randomness and improve consistency.\n\nInstructional prompting with clear reasoning steps and domain constraints, leveraging strong instruction-following models (GPT-4o, DeepSeek).\n\nSelf-correction, where invalid molecules trigger reflection and regeneration, enhancing robustness without external feedback.\n\nWe will include this discussion and future directions in the final version.\n\n## Regarding Limitation\nWe thank the reviewer for raising these important concerns. We will include discussions on the societal impact, as well as the  limitations mentioned above, in the final version."}]

Author rebuttal:

Meta-review: This work presents the CIDD framework for small-molecule generation. The experiments are rigorous, the metrics are meaningful and the ablation studies are convincing.  Reviewers generally agreed that the problem framing is relevant and that the proposed metric is clearly defined. Remaining concerns focus mainly on validation and external oracles. The rebuttal added useful analyses and clarifications that addressed several points, though questions about efficiency and experimental validation are only partially resolved. I find this to be an inspiring multimodal study that combines SBDD with LLM reasoning, and overall I tend to support its acceptance.
Decision: Accept (poster)