Title: CIDD: Collaborative Intelligence for Structure-Based Drug Design Empowered by LLMs
Venue: NeurIPS 2025
Accepted: yes
Keywords: Large Language Model

Drug Design
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

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 uncommon and intrinsically unreasonable 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 LLMbased Chain-of-Thought reasoning, CIDD generates molecules that are not only compatible with protein pockets but also exhibit favorable drug-likeness, structural 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. 3

1 Introduction

Structure-based drug design (SBDD) enables the direct generation of compounds tailored to protein binding sites, making it a powerful tool in drug discovery. Recent 3D generative models-including autoregressive approaches like AR [24] and Pocket2Mol [25], and diffusion-based methods such as TargetDiff [12], DecompDiff [13], and MolCRAFT [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 1a). While these distortions may improve

docking scores via geometric complementarity, they often compromise intramolecular stability and result in poor pharmacokinetic properties.

(a)

(b)

MRR Comparison Across Methods

Molecule Reasonable Ratio (MRR, %)

100%

FDA Drugs MRR (85.9%)

97.5%

%06

81.7%

80%

70%

67.9%

62.6%

%09

56.7%

58.5%

50%

40%

37.8%

37.3%

Structurewithunconventional

Imatinib

30%

polycyclic systems

unreasonable ring

20%

Correcting

AR

Pocket2Mol

TargetDiff

DecompDiff

MolCRAFT

TAGMol

LLM

CIDD

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 a fundamental mismatch: the narrow focus on binding-site compatibility often comes at the expense of structural reasonability and drug-likeness, which depend solely on the chemical structure itself and often conflict with geometric fit. To quantify the gap, we 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 1b, based on our experiments, existing 3D generative models perform poorly under this metric (e.g., TargetDiff: 37.8%, MolCRAFT: 58.5%), falling well below the 85.9% observed in FDA-approved drugs. Some recent methods, such as TAGMol, build on TargetDiff by incorporating gradient-based guidance to jointly optimize for drug-likeness properties like QED [3]. While this leads to improvements in QED scores, it does not result in a general enhancement of drug potential, as evidenced by TAGMol'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-likeness.

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-competent and structurally plausible. CIDD decomposes the generation task into a 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 a 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 state-of-the-art baselines, boosting the overall success ratio from 15.72% to 34.59% . It consistently improves key drug-likeness metrics-including Quantitative Estimation of Drug-likeness (QED) [3], synthetic accessibility (SA), MRR, and the proportion of molecules meeting QikProp [30] 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

intrinsically irrational and rare molecules that deviate from drug-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 Chain-of-Thought 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-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 a molecule x 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. Autoregressive models such as AR [24] and Pocket2Mol [25] sequentially construct molecules in 3D space. Diffusion-based methods like TargetDiff [12], 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], a Bayesian flow model that learns the distribution of drug-like molecules conditioned on pocket structures. All these methods generate intermediate 3D point clouds or graphs, which are 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 [11], 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-based 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 a molecule will reach its biological target-an assessment that precedes, and is distinct from, estimating binding affinity. Widely used machine-learning surrogates, most notably QED [3] and Lipinski's Rule of Five [20], are inadequate for two principal reasons: (i) Legacy bias: These metrics 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 is below 0.5, illustrating its limited discriminative power.

To close this gap, we introduce a broader evaluation framework comprising two complementary tiers. Structural reasonability screens for fundamental medicinal-chemistry principles such as aromaticity, ring-system stability, and hybridization consistency, while physicochemical and pharmacokinetic suitability evaluates absorption, distribution, metabolism, excretion, and toxicity (ADME/T) properties using established descriptors. Together, these tiers ensure that generated molecules are both chemically plausible and therapeutically relevant, providing a more reliable benchmark for modern AI-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 a targeted solution. MRR is a rule-based, empirically motivated metric designed to flag structures that are uncommon in 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 conjugation-particularly aromaticity-that often deviates from patterns observed in clinically validated drugs.

MRR operates by analyzing the hybridization states of atoms within a 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 sp³, 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 failure to correctly generate either fully aromatic or fully saturated systems. Although not intended as an exhaustive classifier, MRR provides an interpretable, medicinal-chemistry-guided 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 is shown in Appendix D.

QikProp Multiple Property Requirements. To further evaluate the physicochemical and pharmacokinetic properties of the generated molecules, we employ QikProp [30], a 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: P 1 , P 2 , . . . , P N . If any of the properties fall outside the acceptable range, the molecule is classified as failing.

3.2 Bridging the Gap with CIDD framework

We propose the Collaborative Intelligence Drug Design (CIDD) framework (Figure 2), a modular system for target-specific molecule generation that combines 3D interaction modeling with LLMenhanced 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 to produce interaction-oriented molecular structures -intermediates that fit the protein pocket but might be chemically incomplete.

LEDD then refines these raw proposals into drug-like molecules using LLMs. The whole process is formalized as:

Here, x 0 encodes the intended spatial interactions, and LEDD completes it into a chemically valid, synthetically accessible compound x . A key limitation of prior approaches is the absence of ground-truth mappings from interaction scaffolds to drug-like molecules, making supervised training infeasible. Traditional generative models-based on direct optimization or limited heuristics-struggle to bridge this gap. In contrast, LEDD leverages the implicit drug-relevant knowledge embedded in largescale pretrained LLMs. With strong instruction-following, reasoning, and coordination abilities ,

CIDD Framework

Structure-Based InteractionGenerator(SBlG)

LLM-Enhanced Drug Designer(LEDD)

Interaction

Analysis

Deep

Raw

CIDD

Generative

molecules

Selection

output

CIDD input

Model

Designed

Design

Reflection

molecules

LEDD Generation

Fragmentation

Design the

Step 1

Interaction-CriticalFragment

Identification

NCI Annotation

Design

CoTsreasoning

Step 2

DetectionofUnfavorableor

QED

Analyzethe

Reflecton the

二

Step3

StrategicDesignPlanning

SA

MRR

interactionbased

previous design,

!Interaction

Analysis

andNCI

onfragmentation

Reflection

evaluatedesign

success.

Step 4

onDesign

GenerateMoleculebased

the 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 formalizes this reasoning process: SBIG captures spatial interaction intent, while LEDD-powered by LLMs-assumes the role of a virtual chemist to transform raw scaffolds into viable drug-like molecules. By leveraging the LLM's instruction-following 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 a structured pipeline of modular reasoning steps. It begins with SBIG, which generates interaction-focused intermediate structures conditioned on the target pocket. These intermediates are passed to the Interaction Analysis Module , which extracts fragment-level binding profiles using docking, fragmentation, and rule-based NCI detection. The Design Module then interprets these profiles to generate chemically plausible molecules that maintain critical interaction motifs. Each design is re-evaluated, and its updated interaction profile is 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.

3.2.2 Interaction Analysis Module

This module evaluates the molecular interaction between a candidate molecule x i and a target protein pocket P . The process begins by re-docking x i into the binding site of P to generate a protein-ligand complex conformation. The molecule x i is then decomposed into chemically meaningful fragments via the BRICS algorithm [6], enabling fragment-level attribution of interaction features.

Next, non-covalent interactions (NCIs), such as hydrogen bonding, π -π stacking, salt bridges, and hydrophobic contacts, are identified from the docked complex based on geometric and chemical features. The resulting interaction map-capturing both atomic contacts and fragment-level organization-is then analyzed by an interaction-specialized large language model, LLM I . Conditioned on spatial interaction cues and chemical context, LLM I synthesizes this information into a structured

interaction profile I i , which semantically links molecular fragments to their respective roles in target engagement.

Formally, the process can be represented as:

The resulting profile I i 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 x 0 , its interaction report I 0 , and prior feedback from the reflection module R = { R 1 , R 2 , . . . , R i -1 } , the Design Module leverages an LLM with a Design role ( LLM D ) to produce a refinement plan D i and an updated molecule x i .

Here, D i is a structured and interpretable set of design decisions grounded in medicinal chemistry, and x i is the generated compound for further evaluation.

To emulate real-world drug design workflows, we implement a Chain-of-Thought prompting strategy that guides the LLM through a domain-informed reasoning pipeline with four sequential stages. (1) Interaction-Critical Fragment Identification: the model analyzes x 0 and highlights fragments forming key non-covalent interactions with the target, based on I 0 . (2) Detection of Unfavorable or Atypical Substructures: chemically undesirable fragments-such as synthetically inaccessible motifs, strained rings, or poor physicochemical regions-are 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 x i is generated based on the design plan, ensuring both target interaction and drug-like properties.

By encoding expert priors into the prompt structure, the Design Module reframes molecule generation as a 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-offs between affinity and drug-likeness 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 i achieves design goals by comparing it to the initial raw molecule x 0 and their respective interaction profiles:

The Selection Module evaluate all the designed molecules { x 1 , x 2 , . . . , x N } and their interaction reports to identify the most promising candidate:

Here, x best balances interaction quality and chemical viability. Full prompt examples for each module are in Appendix C.

4 Experiments

4.1 Experiment Settings

Dataset. 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.

Metrics. We evaluate models using standard metrics: Vina docking score [34] for binding affinity, QED[3] for drug-likeness, SA score [8] for synthetic accessibility, and molecular diversity (computed as 1 -ECFP4 similarity [29]) for structural variety. As we do not directly generate 3D conformations,

Vina Score/Min is excluded. Beyond conventional metrics, we emphasize the evaluation of drug potential via two additional metrics: MRR(Molecular Rule-based Reasonability) , which captures domain-informed structural plausibility, and QikProp pass ratio , which assesses a wide range of physicochemical and pharmacokinetic 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 of the following criteria: Vina < -8 . 18 , QED > 0 . 25 , and SA > 0 . 59 , following [23]. In addition, we introduce molecular reasonability as an extra constraint for defining success. We also evaluate the proportion of molecules that pass all QikProp filters.

Baseline Models. We compare CIDD against a diverse set of 3D-SBDD baselines spanning multiple generative paradigms: the VAE-based LiGAN [27]; autoregressive models AR [24] and Pocket2Mol [25]; and diffusion-based approaches including TargetDiff [12], IPDiff [15], DecompDiff [13], and TAGMol [7]. IPDiff uses interaction-guided sampling, while DecompDiff leverages interaction-aware priors; TAGMol adds gradient-based optimization for drug-likeness. We also evaluate DrugGPS [37], a fragment-based method, and MolCRAFT [26], a Bayesian flow network shown to be state-of-the-art in recent benchmarks [19, 10].

CIDD Settings. We use MolCRAFT 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 1, 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 a 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-guided 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 metrics such as MRR and SA, performing comparably to the unguided diffusion model TargetDiff. This suggests that optimization-driven 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 a more robust and comprehensive molecular design capability. These results stem from CIDD's ability to integrate the complementary strengths of distinct modeling

paradigms-combining the strong binding affinity modeling capability of 3D generative models with the drug-likeness reasoning and instruction-following 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 a result, it achieves a significantly higher success ratio of 34.59% , compared to 15.72% for the best

pocket2mol

targetdiff

decompdiff

molcraft

0.3

0.4

0.5

0.6

0.7

0.8

QED

19.4%

31.4%

13.7%

14.7%

with LEDD (Darker Shade)

SBIG only (Lighter Shade)

(a) QED

pocket2mol

targetdiff

decompdiff

molcraft

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

SA Score

3.4%

20.0%

13.8%

7.6%

with LEDD (Darker Shade)

SBIG only (Lighter Shade)

(b) SA Score

pocket2mol

targetdiff

decompdiff

molcraft

20

30

40

50

60

70

80

90

100

25.3%

85.2%

15.3%

39.2%

with LEDD (Darker Shade)

SBIG only (Lighter Shade)

pocket2mol

targetdiff

decompdiff

molcraft

0

20

40

60

80

100

QikProp Pass Ratio (%)

92.6%

102.8%

29.3%

57.4%

with LEDD (Darker Shade)

SBIG only (Lighter Shade)

(d) QikProp Pass Ratio

baseline-demonstrating 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 are approximately 50% larger on average in terms of molecular weight (336.70 vs. 234.30). Moreover, it 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-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-including QED, 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 to enhance diverse, diverse aspects of druglikeness 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.

4.4 Ablation and Analysis

4.4.1 Impact of Different LLMs

We evaluate GPT-4o, GPT-4o-mini, DeepSeek-v3 [5], and DeepSeek-r1 [14] using MolCRAFT as the SBIG module (Table 2a). All models improve drug-likeness metrics (MRR, QikProp) and docking scores. DeepSeek-v3 achieves property gains with minimal edits, while GPT-4o-mini struggles with similarity, and DeepSeek-r1 makes broader, less controllable changes. GPT-4o and DeepSeek-v3 best support CIDD's goal of generating similar yet improved molecules. Smaller models like LLAMA-7B fail to follow design instructions. CIDD remains plug-and-play, benefiting from future LLM advances.

(b) LLM-Only vs. CIDD Comparison

↓

MRR

LLM-SBDD

CIDD-LLM

CIDD

Vina

-6.244

-7.230

-8.496

↑

97.45%

90.97%

81.74%

Success Ratio

↑

5.95%

17.59%

35.22%

Supporting molecule

CIDD strategy

CIDD result

Interaction analysis

1.StabilityandSimplicity:Replace

Unreasonable

unstable or complexfragments(e.g.,

Reasonable

Vina:-7.796

conjugated dienes,highlyfluorinated

Vina:-11.383

QED:0.202

chains)withmorestable,common,

QED:0.860

SA: 0.52

2.KeyInteractions:Retain critical

anddrug-likealternatives.

SA:0.83

B:GIn316

A:GIn316

interactionpointswithresidueslike

OH

NH

**GLN316,TRP164,VAL294,VAL315,

and ILE319**

3.SyntheticFeasibility:...

OH

5.Limited Modifications:..

4.Drug-Likeness:...

Target:IDHP_HUMAN_40_452_0

(b)

40%

Potency

Success Ratio (Line)

Success Ratio (Bar)

36.4%

Estimated byVina

Drug

35%

33.0%

Safe,Effective,

30.5%

31.2%

3D-SBDDModel Outputs

CIDD

☆

Controllable

Traditional

Gap/Misalignment

Hit-to-lead

25%

WidelyAccessible

Molecules

(StructuresPreferred

20%

by Chemists)

15%

0

5

10

15

Reasonability

Molecule

ii.Physiochemical;

i. QED/SA/Rule of Five;

Drug-likeness

NumberofPreviousExperiences

(MRR)

ii. Biological

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 2b, 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 of the CIDD Framework

Interpretable Molecule Design. Figure 4a illustrates the CIDD generation process. The LLMpowered 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-are automatically identified and replaced (e.g., with a benzene ring), while side chains are adjusted to preserve key hydrogen bonds with Gln316 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 a transparent, expert-assisting workflow. CIDD also enables the automated creation of molecule pairs that differ in drug-likeness with minimal structural edits. These pairs effectively capture how small chemical changes influence pharmaceutical viability, offering highquality, distributionally aligned data for 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 is their ability to leverage prior experience, provided as context, to generate insightful outputs. To illustrate this, we conducted a proof-of-concept experiment using a relatively lightweight LLM: GPT-4o-mini. The model was given varying numbers of previous design reports and results (0, 5, 10, and 15) as contextual input and tasked with generating insights to support the design module of CIDD. As shown in Figure 4b, the success rate improved as more prior reports were included. These results demonstrate CIDD's capacity for continual evolution by incorporating accumulated experience-without requiring model retraining. This mirrors the way human experts enhance their performance through repeated exposure and practice.

Generating Small Molecules with Both High Potency and Drug-Likeness. Drug potential hinges on two key factors: potency and drug-likeness. While most 3D-SBDD models emphasize target fit, they often produce chemically unreasonable structures. As shown in Figure 4c, our CIDD framework bridges this gap by combining geometric modeling with LLM-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-driven reasoning for structure-based drug design. CIDD addresses a key limitation of current generative models: the tendency to generate interaction-compatible but chemically unreasonable molecules. Through a modular, interpretable generation process, CIDD achieves state-of-the-art results on the CrossDocked2020 benchmark-substantially improving drug-likeness metrics (QED, SA, MRR, QikProp) while maintaining high binding affinity. By bridging 3D geometric modeling with languageguided 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 early-stage drug discovery.

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A Discussion on the Pharmaceutical Terminology

In this work, we employ a comprehensive set of evaluation metricsVina , QED , SA , our proposed MRR , the Success Ratio , and QikProp -to analyze different facets of molecular generation performance. Following established practices, we use Vina scores to assess the 3D complementarity between generated molecules and their target binding sites. Meanwhile, we evaluate broader molecular properties using standard metrics (QED and SA), the domain-informed MRR, and physicochemical 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 a drug-like molecule must fulfill. We introduce the term drug potential to describe the overall suitability of a 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 a 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 suitability of a molecule as a drug candidate. Drug discovery and development (DDD), however, are deeply influenced by human expertise-including implicit preferences for molecular scaffolds and nuanced, often tacit, domain knowledge that is difficult to formalize or quantify. As a result, even experienced medicinal chemists struggle to define or approximate the true probability function p ( drug ) , which represents the likelihood that a molecule will become a viable therapeutic candidate. However, the machine learning community often oversimplifies drug-likeness to metrics such as QED or Lipinski's Rule of Five, which capture only a narrow range of basic physicochemical properties. This simplification overlooks critical factors such as oral bioavailability, metabolic stability, and toxicity risks (e.g., hERG liability).

At a more fundamental level, a molecule must be chemically valid , meaning it adheres to basic chemical rules such as proper valence and atom types. However, we observe that many modelgenerated molecules-while technically valid-contain rare or unstable structural substructures that 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 are formally valid but unrealistic. To fill this gap, we propose MRR, a rule-based metric that reflects medicinal chemistry heuristics. It identifies implausible features such as unstable ring systems and uncommon conjugation patterns, offering an interpretable and practical means of identifying unrealistic model outputs.

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 is 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 5 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 a new molecule is designed, the Evaluation

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 6 presents the prompt and example response for the Interaction Analysis Module.

Figures 7 and 8 display the prompt and example response for the Design Module.

Figures 9, 10, and 11 illustrate the prompt and example responses for the Reflection Module.

Figures 12 and 13 show the prompt and example response for the Selection Module.

SBIG

LEDD:

Generation

Selection

LEDD:

PDB id:

7KS1

P, xo

LLM;

Ig/I;

protein pocket:

crucial forinteractions with the

LLMs

Seleetion and reasoning:

Docking score = -11.76

Original Molecule

3. Aromatic m-System

2. Carbonyl(-C=O)

L.

Docking score =-10.148

Molecule 1

Doxking score = -10.3

Molecule 2

Docking sooe = -12.472

Molecule 3

4. Halogenated Group

###*Step 1:Review Docking Scores**

*Molecule 3, with theest score12.472has the strongest binding affinity among th

candidates.

Io, I

###*Step 2:Analyze Interaction Profiles**

LLMR

R

Sabiliy

Hydrogen Bonding ↓

Compared to .xa

Synthesizability ↑

LLMo

D

ARG152). The interaction with SER150 (1.99 A) and TYR246 (2.13 A) are particularly

stabilizing...

- Hydrophobic Interactions: Strong and multiple hydrophobic interactions with HIS60, TYR90,

####**Molecule 3: Interaction Highiights**

-Hydrogen Bonds:Forms strong hydrogen bonds with key residues(e.g.SER150,

### **Strengths of the Modifications**

LEU147, and LEU149

-The CF3 group improvesmetabole stabilly

R

Step 1:Core Scaffold

Replacement

HalogenatedReglon

Step 2: Optimize the

Whilebindingaffinityiscrucialthemoleculemustalsoexhibit potentialasarug.

##**Step 3: Assess Drug-Like Properties**

The redesignedmoleculeretains cnticalinteractions suchas

hydrogebnding  HIS7andARG5

2. **Retention of Key Interaction Modes:**

# *Weaknesses of the Modifications**

1. *Over-Simplification of the Aromatic Core:**

ringTHFismore stable,

-Replace the oxetane ring with

synthetically accessible, and

retains the abilty to form

**Proposed Modification:**

CIr)inthehalogenatedringwith

a trifluoromethyf group (-CF3）),

whichismetaboliallymore

Proposed Modification:**

- Replace the chlorine atom ('-

stable and provides similar

x

###*Step 4:FinalRecommendation**

Based ondockingscoreinteractiondiversityanddrug-likenesspotential Molecule*s

Other molecules lack some features of Molecule 3. For example:

- Molecule 2 lacks halogen bonds and achieves a lower affinity score ..

2.“Reduced Hydrogen Bonds

bon

d pu

hydrogen bonds vla its oxygen

atom...

hydrophobic character...

interactions, and drug-like properties make it an excellent starting point for further

optimization.

D,x

Interaction analysis

The interaction analysis results are as follows: [PLIP(Docking(xo, P)]

The smiles string of the molecule is: [xo]

Query

matching,youneedtoidentifythekeyfragmentonthemoleculethatconducttheinteractionwiththeproteinpocket.

[lmportant]Theformatshouldbe,whichfragmentcaninteractwithwhichresidueintheproteinpocket.Forexample:

Thefragment'ethanol'caninteractwiththeresidueASP123'intheproteinpocket.

[lmportant] Itshouldbeatfragmentlevel,insteadofatomlevel.Youneedusetheinformationfromthefragment labels

toidentifywhichatombelongstowhichfragment.

:[%] ndno

LLM

###**KeyFragmentsof Importance**

Basedontheanalysis,thefollowingfragmentsaremostcrucialforinteractionswiththeproteinpocket:

1.*Aromaticand HydrophobicCore:**

-[C]C1=C[C]=[C][C]=C1(Atoms:5-11)

-CriticalforhydrophobicinteractionswithILE113,LEU142,andLEU147.Thisfragmentanchorsthemoleculeinthe

hydrophobicpocket.

2. **Carbonyl(-C=O):**

-Atoms12,13′

-Vital forhydrogenbonds,particularlywithGLY63andGLU64.Thisfragmentcontributestothespecificityof the

interaction.

3.**Oxetane Ring:**

-[C]1[C]IC]O1(Atoms:15-18)

4.**AromaticT-System:**

-[C]1=NN[C]=C1(AtomS:0-4)

-EngagedinTt-stackingwithTYR9o,stabilizingtheligandinthebindingpocket.

5.**Halogenated Group:**

-FC1=CC(CI)=[C][C]=[C]1(Atoms:23-30)

-Responsibleforhalogenbonding withPHE61,contributingto unique andspecificinteractions.

Design - Part 1

[nstruction]

This is the original molecule: [xo]

This is the interaction analysis: [o]

thecorescaffoldstructurewhileretainingkeypharmacophores.

Query

[DesignObjectives]

1.Themodifiedmoleculeshouldbemorestableandeasiertosynthesizethantheoriginalmolecule.Thenew

fragmentsshouldbecommon,stable and notweird.

3.Themodifiedmoleculeshouldcontainthekeyinteractiontypeswiththesameresiduesastheoriginalmolecule.

4.The original moleculemay contain uncommonorunstablefragments.Identify thesefragments and replace them

withmorecommonandstablecounterparts,ensuringthatthemodificationsretainkeypropertiesof theoriginal

5.The modifiedmolecule shouldbe more like a drug candidate than theoriginal molecule.

[DesignRequirements]

1. Do not modify the molecule too much.

2.Pleaseonlygive themodification steps,donot generatesmilesby yourself.Wewilluse anotheragent togenerate

the new moleculebased on your design.

[Experience Summary]

Wehavedonesomemodificationsbefore.Youcanlearnfromthepreviousdesignsandreflectionstomakebetter

modifications.

Here is the summary of the previous designs and reflections: [R]

Now,giveusthemodificationsuggestionsandrequirements.

Output [D1]:

###**Step1:CoreScaffoldReplacement**

LLMD

**UnstableorUncommonScaffold:**

bonding,canbechemicallyunstableundermetabolicconditionsand issyntheticallychallenging.

**Proposed Modification:**

-Replacetheoxetaneringwitha**tetrahydrofuran（THF)ring**THFismorestable,syntheticallyaccessible,andretains

theabilitytoformhydrogenbondsviaitsoxygenatom.mportantly,itmaintainsasimilarsize,shape,andpolarity.

###**Step2:OptimizetheHalogenatedRegion**

**UncommonorUnstableFragment:**

-Thehalogenatedaromaticfragment(FC1=CC(Cl)=[C]IC]=[C]1)cancontributetometabolicinstabilityortoxicity

**Proposed Modification:**

-Replace thechlorine atom(-Cl) in thehalogenated ringwitha trifluoromethylgroup(-CF3),which ismetabolically

interactions.Thefluorineatomcanremainunaltered.

###**Step3:RevisetheAromaticCore**

**Uncommon orUnstableFragment:**

-The core ring[C]C1=C[C]=[CJIC]=C1 is a polycyclic conjugated system prone to oxidative metabolism.While it

provideshydrophobicityandanchorsthemolecule,replacingitwithamorecommonaromaticfragment(e.g.,phenyl)can

reducemetabolicliabilitieswithoutcompromisinginteractions.

**Proposed Modification:**

-Replace[C]C1=C[C]=[C]IC]=C1with**aphenyl ring(benzene)**.Phenylringsretainhydrophobicityandsizewhile

reducing the likelihoodof oxidation andincreasingsynthetic accessibility.

Design - Part 2

:[a] ndino

###**Step4:Replace the1,2,4-TriazineRing**

LLMD

**UncommonorUnstableFragment:**

**ProposedModification:**

###**Step5:SimplifyLinkerChains**

**UncommonorUnstableFragment:**

-The linkerfragment connecting theoxetane ring and otherparts of the moleculemay introduceunnecessary

flexibility,whichcanlead toentropicpenaltiesuponbinding.

**ProposedModification:**

-Shortenthelinkerchainbyremoving orsimplifying alkyl groups.Usefewermethylenegroupswhere applicableto

reduceflexibilitywhilemaintainingshapeandsizeforproperbinding.

###**Summaryof Modifications**

whileretaininghydrogen-bondingcapability.

2.**Halogenated AromaticRing(MetabolicLiability):**Replace chlorine(CI)with**trifluoromethyl group(CF3）)**to improvemetabolicstabilitywhilemaintaininghydrophobicinteractions andhalogenbonding.

reducemetabolicliabilitywhileretaininghydrophobicity.

4.**1,2,4-TriazineRing(UncommonScaffold):**Replace with a **pyridine ring**to retain T-stacking and hydrogen- bondacceptorpotentialwhileimprovingdrug-likeness.

5.**FlexibleLinkers(EntropicPenalty):**Simplify and rigidify linkerregionsby shortening alkyl chains,reducing flexibility.

This is the original molecule: [xo]

This is the modification designed by the design expert:[D1]

molecule should bevalid and make sense in the context of medicinal chemistry.

Query

Nowpleaseonlyreturnthesmilesstringofthegeneratedmolecule.Thesmilesstringshouldbeenclosedinapair of $$$. For example:$$$cccc$$$

:[lx] 1ndino

$$$C1=CC(C(F)(F)F)=C(C(=O)NC(C2CCOC2)NC(=O)C3=CC=CN3)C=C1$$$

LLMp

Reflection - Part 1

[nstruction]

This is the original molecule structure: [xo]

This is the interaction analysis of this molecule and the protein pocket: [o]

Query

This is the modification design generated by you: [D1]

This is themolecule structure designedbased on yourmodification:[x1]

This is the interaction analysis generated by interaction export agent: [1]

Previously,weaskedyoutomodifyamoleculetogeta newmoleculethatbindsbettertotheproteinpocket.We

analyzed thebindingof thenewmoleculeto theproteinpocket andput it togetherwiththebindingbefore the

modificationforcomparison.

to theprotein pocket.

Please use critical thinking toanalyze,pointingoutboththegood and thebadpoints.Yourreflectionis:

Output [R1]:

###**StrengthsoftheModifications**

LLMR

1.**ImprovedStability andSyntheticAccessibility:**

Thiswasasensiblechangethatalignswell withdrug-likenessprinciples.

TheCF3groupimprovesmetabolicstabilityandreducespotentialtoxicityfromreactivehalogens,whilemaintaining

theoriginalhydrophobicityandshape.

2.**Retention of Key Interaction Modes:**

-Theredesignedmoleculeretainscritical interactionssuchashydrogenbondingwithHIS117andARG152,

hydrophobicinteractionswithHIS60,LEU147，andPHE188,andT-stackingwithTYR90.Theseinteractionsare

-The**pyridine ring**（replacing thelesscommon1,2,4-triazinering)alignsbetterwith drug-likenessruleswhile

maintaininghydrogenbondingandhydrophobiccapabilities.Thissubstitutionstrengthensthemolecule's

pharmacokineticprospects.

3.**Refinement of Scaffolds and Simplification:**

hydrophobicinteractions.

###**WeaknessesoftheModifications**

1.**Reduction in Docking Score:**

cleardropinbindingaffinity.Whilethemodifiedmoleculeretainskeyinteractions，somecriticalbindingcontributions

betweentheligandandproteinpocket.

2.**Over-Simplificationof theAromaticCore:**

withresidueslikeTYR90,ILE113,andLEU142werediminished，andthemoleculelostsomeofitsabilitytostabilize

keyTt-interactionswithinthepocket.

3.**UnutilizedPotentialof theTrifluoromethylGroup:**

-Whilethetrifluoromethyl(CF3)groupimprovedmetabolicstability,itdidnotdirectlycontributetobinding

replacementcouldhaveyieldedgreaterinteractionstrength.

Reflection - Part 2

:[y] indino

4.**Reduced Hydrogen Bonds:**

LLMR

GLU64,andTYR246viaitsoxetaneringandcarbonylgroup.Thesehydrogenbondswereeitherweakenedorlost in

depletion.

5.**Flexibility in the THFRing**

hybridizedcarbons.Thisflexibilitymayleadtoentropicpenaltiesuponbinding,making themoleculelessefficient in

achievingapreorganized，bioactiveconformation.

6.**Over-RelianceonHydrophobicInteractions:**

-Similartotheoriginalmolecule，themodifiedstructurestillreliesheavilyon**hydrophobicinteractions**forbinding

(e.g.,HIS60,90,EU147）.Hydrophobicintractionsarelesspecificthanpolarrionicintractionswhichare

criticalforselectivityand affinity.Bynotintroducingadditionalpolargroups,theredesignmissed anopportunityto

improveoverallbindingspecificity.

###**Comparison of BindingFragments**

####**OriginalMolecule:**

1.**Oxetane Ring(Lost in the Redesign):**

-FormskeyhydrogenbondswithTYR246inthebindingpocketcontributingsignificantlytospecificandstrong

ligandbinding.Itsremovalweakenedoverallbindingaffinity.

2. **Conjugated HydrophobicCore([CjC1=C[C]=[CIIC]=C1):**

-ProvidesextensivehydrophobicandT-stackinginteractionswithresiduessuchasILE113,LEU142,andTYR90.

Replacing thisricharomaticstructurewithabenzeneringreduced thehydrophobicandrt-electroncontributionto

binding.

3.*Halogenated AromaticRing(FC1=CC(Cl)=[C]IC]=[C]1):**

####**ModifiedMolecule:**

1.**THFRing（Introduced)**

flexibilityandentropicpenaltiesthatmayoffsetitsstabilizingcontributions.

2.**Trifluoromethyl Group(CF3,Introduced):**

ringaddsmolecularweightbutnofunctionalenhancementtobinding.

3.**PyridineRing(Introduced):**

-Maintainshydrogen-bondingwithHIS117andinteractshydrophobicallywithHiS60,performingwellasa

replacementfor1,2,4-triazine.Thoughnot asinherentlydiverseininteractionopportunitiesasitspredecessor,it

improvesthedrug-likenessof themolecule.

4.**Benzene Ring(Simplified):**

Reflection -Part 3

2

Output [R1]:

###**KeyFragmentsRetainedorLostforBinding**

LLMR

**CriticalRetainedFragments:**

1.**PyridineRing**forTt-stackingandhydrogenbondingwithTYR90andHIS117.

**CriticalFragmentsLost:**

2.**Conjugated HydrophobicCore**ensured multisite hydrophobic interactions and Tt-stacking withresidues like

LEU142andTYR90.

3.**Halogenated AromaticRing**provided halogenbondswithPHE61,contributing unique specificity lost in the

redesign.

###**SuggestionsforImprovement**

1.**ReintroduceHydrogenBondingGroups:**

TYR246,GLY63,and GLU64.

2.**Revisit theAromaticCore:**

-Insteadof oversimplifying thehydrophobiccoretobenzene,considerapolycyclicaromaticorheterocyclicsystem

tobalancehydrophobicity and metabolicstability.

3.**OptimizeTrifluoromethylGroupPlacement:**

difluorophenylorhydrophilicsubstituent).

4.**Rigidify theTHFRing:**

###**Conclusion**

totheproteinpocket,asindicatedbythediminisheddockingscore.Whileretainingimportantinteractions,the

stronger andmore specific binding.

Selection - Part 1

[lnstruction]

Givenaproteinpocketandanumberofdifferentmolecules,Ineedyoutofindamoleculethatisbestsuitedto askyoutomakechoicesbasedonthisinformation.Youshouldconsiderboththebindinganalysisandwhether themoleculehaspotentialtobeareal drug.Youshouldoutput thereasonand thesmilesstringof thechosen molecule.Thesmilesstringshouldbeenclosed inapairof$$$.Forexample:..(Thereason)Thechosensmiles is:$$$CCO$$$

Query

This is the molecule structure: [xn]

This is the interaction analysis of thismolecule and the protein pocket:[n]

This is the molecule structure: [xm]

This is the interaction analysis of this molecule and the protein pocket: [m]

This is the molecule structure: [xp]

This is the interaction analysis of this molecule and the protein pocket: [p]

Please thinkstepby step:

The chosenmoleculeis:

O=C(C1=CC=C(C=C1)C2=CC=CC=C2)N(C3CC0C3)C(NC4=C(F)N=CC=C4)=0

LLMs

The docking score of the chosen molecule is: -12.472

Thereasonforchoosing themoleculeis:Toselect thebestmoleculeforbinding to the proteinpocket,wewill step through thefollowingconsiderations:

###**Step 1: Review Docking Scores**

The docking score is a key metric indicating the binding affinity between a molecule and the protein pocket. Lower docking scores (more negative values) suggest stronger binding.Thedocking scoresfortherespectivemoleculesare asfollows:

Molecule 1: -10.148

2. Molecule 2: -10.3

3.Molecule3:-12.472

From this, **Molecule 3**, with the best score of **-12.472**, has the strongest binding affinityamongthecandidates.

###**Step 2: Analyze Interaction Profiles**

Building off the docking scores, we now consider the rich interaction profiles of each molecule.Amoleculethatformsdiverseinteractionswilloftenbindmorerobustlyand specifically to the protein pocket.

####**Molecule 3: Interaction Highlights**

**Hydrophobic Interactions:** Strong and multiple hydrophobic interactions with HIS60,TYR90,LEU147,andLEU149,anchoredbythearomaticbenzenering.

ARG152). The interaction with SER150 (1.99 A) and TYR246 (2.13 A) are particularly stabilizing.

**t-Cation Interactions:** Robust Tt-cation interaction with ARG152 (3.85 A), stabilized further by ideal offset alignment (1.32 A).

*Halogen Bonds:** Two halogen bonds with GLU189 (3.84 A) and ASN237 (3.40 A) provide an additional layer of interaction, making this molecule unique in its interaction diversity.

Selection -Part 2

**Key Strengths:**

- The interaction diversity and strength of Molecule 3 far surpass other molecules,

LLMs

leveraginghydrophobicforces,hydrogenbonding,Tstacking,Tcationinteractions,and

halogen bonding.

-Themolecule effectively anchorsitself tobothpolar and nonpolarregions,ensuring

specificity for the protein pocket.

###**Step 3:AssessDrug-LikeProperties**

While binding affinity is crucial, the molecule must also exhibit potential as a drug.

Propertiesrelevanttodrug-likenessinclude:

**Hydrophobic Components:** Ensure solubility and stability for membrane diffusion.

**Halogen Incorporation:**Enhances membrane permeability and metabolic stability.

- **Compact, Non-Bulky Structure:** Ensures the molecule fits within the protein pocket

withoutstericclashes.

ForMolecule3:

- The combination of aromatic rings, amide groups, and fluorine atoms supports

balanced hydrophobic and polar properties, increasing stability and bioavailability.

-The inclusion ofhalogensenhances drug-likenessfurther.

OthermoleculeslacksomefeaturesofMolecule3.Forexample:

-Molecule2lackshalogenbondsand achievesaloweraffinityscore.

- Molecule 1 have good binding but lack the breadth of interactions seen with Molecule

3.

###**Step4:FinalRecommendation**

is thebestcandidate.Its strong binding affinity(-12.472),balancedpolar and nonpolar

interactions,anddrug-likepropertiesmakeitanexcellentstartingpointforfurther

optimization.

###**ChosenMolecule**

**Reason:**Molecule3 exhibits thehighest docking score,with diverse interaction types

(hydrophobic, hydrogen bonding, T-stacking, T-cation, and halogen bonding) that secure

featuressuchashalogenincorporationand awell-optimizedmolecularstructure.

**Selected SMILES String:**

$$$O=C(C1=CC=C(C=C1)C2=CC=CC=C2)N(C3CCOC3)C(NC4=C(F)N=CC=C4)=O$$$

D Algorithm for MRR and AUR

The complete calculation process for assessing the reasonability of a molecule is outlined in Algorithm 1.

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 an empty list for carbonyl/imine carbons . foreach bond in mol do if bond is double and one atom is carbon, the other is oxygen or nitrogen then 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 one or more atoms with another ring then Group the connected rings into a single ring system . Step 3: Evaluation of Molecular Reasonability Exclude any atoms previously identified as part of carbonyl or imine groups. Classify the remaining carbon atoms in each ring system as follows: · sp 2 hybridized : Aromatic or unsaturated carbons. · Non-sp 2 hybridized : Saturated carbons. foreach ring system in the ring systems do if the ring system contains multiple rings and all carbon atoms are non-sp 2 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 are consistent in hybridization (either all sp 2 or all non-sp 2 ) 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 sp 2 or all non-sp 2 ) then Mark the ring as reasonable. if no new reasonable rings are identified then Mark the molecule as unreasonable. Add the carbon atoms in the remaining rings to the unreasonable atom list. Exit the loop. Calculate AUR as the ratio of unreasonable atom count to the total ring atom count. Return MRR and AUR .

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 provided by QikProp, including "#stars", "#amine", "#amidine", "#acid", "#amide", "#rotor", "#rtvFG", "mol_MW", "dipole", "SASA", "FOSA", "FISA", "PISA", "WPSA", "volume", "donorHB", "accptHB", "dip 2 /V ", "ACxDN . 5 / SA", "glob", "QPpolrz", "QPlogPC16", "QPlogPoct", "QPlogPw", "QPlogPo/w", "QPlogS", "CIQPlogS", "QPPCaco", "QPlogBB", "QPPMDCK", "QPlogKp", "IP(eV)", "EA(eV)", "#metab", "QPlogKhsa", "PercentHumanOralAbsorption", "SAFluorine", "SAamideO", "PSA", "#NandO", and "RuleOfThree".

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 a single NVIDIA A100 GPU. For the LLM component, we rely on API-based access provided by the service provider, which requires no local computational resources.

G More Experiment Results

Based on the different criteria presented in Table 3, we provide additional pass ratio results in Table 4.

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 properties, including "#rtvFG", "QPlogS", "QPlogPo/w", "mol_MW", "dipole", "SASA", "FOSA", "FISA", "IP(eV)", "EA(eV)", "#metab", "PercentHumanOralAbsorption", and "PSA".

Filter 3 assesses molecular compliance with the "RuleOfFive" 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 to zero).

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

OH

HO

title:IDHP_HUMAN_40_452_0

title:IDHP_HUMAN_40_452_0

title:IDHP_HUMAN_40_452_0

title:IDHP_HUMAN_40_452_0

：Unreasonable

：Reasonable

：Unreasonable

：Reasonable

vina:-7.824

vina:-10.106

vina:-7.941

vina:-9.66

OH

HO

OH

title:N0S1_HUMAN_302_723_0

title:NOS1_HUMAN_302_723_0

title:N0S1_HUMAN_302_723_0

title:NOS1_HUMAN_302_723_0

：Unreasonable

：Reasonable

:Unreasonable

:Reasonable

vina: -9.415

vina: -10.098

vina:-8.054

vina: -8.432

HgN

OH

H2N

HO

title:NOS1_HUMAN_302_723_0

title: NOS1_HUMAN_302_723_0

title: NOS1_HUMAN_302_723_0

title:NOS1_HUMAN_302_723_0

：Unreasonable

：Reasonable

：Unreasonable

：Reasonable

vina:-10.069

vina:-10.22

vina:-9.287

vina: -9.6

HO

HO

HzN

HO

HO

OH

HC

title:ABL2_HUMAN_274_551_0

title:ABL2_HUMAN_274_551_0

title:ABL2_HUMAN_274_551_0

title:ABL2_HUMAN_274_551_0

：Unreasonable

：Reasonable

：Unreasonable

：Reasonable

vina:-10.007

vina:-10.946

vina: -9.916

vina:-10.509

OH

HO

OH

OH

OH

OH

HO

HO

OH

OH

title:AK1BA_HUMAN_1_316_0

title:AK1BA_HUMAN_1_316_0

title:NQ01_HUMAN_2_274_0

title:NQO1_HUMAN_2_274_0

：Unreasonable

：Reasonable

:Unreasonable

：Reasonable

vina: -10.421

vina:-10.778

vina:-8.37

vina: -9.24

HN

HO

HO

HO

OH

title: NQO1_HUMAN_2_274_0

title:NQO1_HUMAN_2_274_0

title:PHP_SULSO_1_314_0

title: PHP_SULS0_1_314_0

：Unreasonable

:Reasonable

：Unreasonable

:Reasonable

vina:-7.951

vina: -9.51

vina:-11.034

vina:-11.397

Supporting molecule

CIDD output

Supporting molecule

CIDD output

OH

IH

HO-

OH

OH

HO*

HO

title:

title:

title: BGL07_ORYSJ_25_504_0

title: BGL07_ORYSJ_25_504_0

PHKG1_RABIT_6_296_ATPsite_0

PHKG1_RABIT_6_296_ATPsite_0

：Unreasonable

：Reasonable

:Unreasonable

：Reasonable

vina:-8.041

vina:-9.949

vina:-8.548

vina: -8.727

O

HO

HO

title:CD38_HUMAN_44_300_0

title:CD38_HUMAN_44_300_0

title:TNKS2_HUMAN_948_1162_0

title:TNKS2_HUMAN_948_1162_0

:Unreasonable

：Reasonable

：Unreasonable

：Reasonable

vina:-7.443

vina:-8.863

vina:-10.974

HOO

OH

OH

HN

HO

OHHO

HN

HO

NHHO

OH

title: P2Y12_HUMAN_1_342_0

title: P2Y12_HUMAN_1_342_0

title: HMD_METJA_1_358_0

title:HMD_METJA_1_358_0

：Unreasonable

：Reasonable

:Unreasonable

：Reasonable

vina:-10.463

vina:-10.674

vina:-9.259

vina:-10.458

HO

OH

title:SQHC_ALIAD_1_631_0

title:SQHC_ALIAD_1_631_0

title:SQHC_ALIAD_1_631_0

title:SQHC_ALIAD_1_631_0

: Unreasonable

：Reasonable

:Unreasonable

：Reasonable

vina:-14.278

vina:-17.408

vina:-13.626

vina:-13.825

title:BGAT_HUMAN_63_353_0

title:BGAT_HUMAN_63_353_0

title:CHIB_SERMA_1_499_0

title:CHIB_SERMA_1_499_0

：Unreasonable

：Reasonable

：Unreasonable

: Reasonable

vina: -7.891

vina: -9.201

vina:-10.676

vina: -13.113

HO

HO

HO

HO

title:CHIB_SERMA_1_499_0

title:CHIB_SERMA_1_499_0

title:NR1H4_HUMAN_258_486_0

title:NR1H4_HUMAN_258_486_0

：Unreasonable

:Reasonable

:Unreasonable

：Reasonable

vina: -7.115

vina:-8.719

vina:-10.038

vina:-11.021

Supporting molecule

CIDD output

title: SDIA_ECOLI_1_171_0

title: SDIA_ECOLI_1_171_0

:Unreasonable

：Reasonable

vina: -11. 019

vina: -11.294

title:GSTP1_HUMAN_2_210_0

title:GSTP1_HUMAN_2_210_0

:Unreasonable

：Reasonable

vina: -7.591

vina:-8.725

HNOH

OH

HO

title:HDHA_ECOLI_1_255_0

title:HDHA_ECOLI_1_255_0

：Unreasonable

：Reasonable

vina:-8.5

vina:-8.515

title:IDHP_HUMAN_40_452_0

title:IDHP_HUMAN_40_452_0

：Unreasonable

：Reasonable

vina:-10.774

title:IDHP_HUMAN_40_452_0

title:IDHP_HUMAN_40_452_0

：Unreasonable

：Reasonable

vina: -11.661

HO

HO

title:AK1BA_HUMAN_1_316_0

title:AK1BA_HUMAN_1_316_0

:Unreasonable

：Reasonable

vina:-9.845

vina:-9.981

HO

title: OLIAC_CANSA_1_101_0

title: OLIAC_CANSA_1_101_0

：Unreasonable

:Reasonable

vina:-7.631

Supporting molecule CIDD output

title: SDIA_ECOLI_1_171_0

title: SDIA_ECOLI_1_171_0

:Unreasonable

：Reasonable

vina:-8.52

vina:-13.937

HO

title:HDHA_ECOLI_1_255_0

title:HDHA_ECOLI_1_255_0

：Unreasonable

：Reasonable

vina: -7.709

vina: -8.778

title:NOS1_HUMAN_302_723_0

title:NOS1_HUMAN_302_723_0

：Unreasonable

：Reasonable

vina:-11.943

vina: -12.979

title:NR1H4_HUMAN_258_486_0

title:NR1H4_HUMAN_258_486_0

：Unreasonable

：Reasonable

vina:-6.824

vina:-8.578

HO

：Unreasonable

：Reasonable

vina: -9.607

vina:-10.132

title:P2Y12_HUMAN_1_342_0

title:P2Y12_HUMAN_1_342_0

:Unreasonable

：Reasonable

vina: -7.75

vina:-16.253

D

OH

title:SIR3_HUMAN_117_398_0

title:SIR3_HUMAN_117_398_0

:Unreasonable

：Reasonable

vina:-8.238

vina:-8.331

Supporting molecule

CIDD output

OH

HO

title:MCCF_ECOLX_1_344_0

title:MCCF_ECOLX_1_344_0

：Unreasonable

:Reasonable

vina:-8.726

vina:-8.947

OH

title:IDHP_HUMAN_40_452_0

title:IDHP_HUMAN_40_452_0

：Unreasonable

：Reasonable

vina:-7.796

vina:-11.383

OH

title:AKT1_HUMAN_1_137_0

title:AKT1_HUMAN_1_137_0

：Unreasonable

:Reasonable

vina:-8.605

vina:-9.926

OH

OH

HN

OH

title: HMD_METJA_1_358_0

title: HMD_METJA_1_358_0

:Unreasonable

:Reasonable

vina:-7.825

vina:-8.55

HO

title:SIR3_HUMAN_117_398_0

title:SIR3_HUMAN_117_398_0

:Unreasonable

：Reasonable

vina:-7.93

vina:-10.435

HO

title:DYRK2_HUMAN_145_550_0

title:DYRK2_HUMAN_145_550_0

:Unreasonable

:Reasonable

vina: -7.961

vina:-8.399

title:DIDH_RAT_1_319_0

title:DIDH_RAT_1_319_0

：Unreasonable

：Reasonable

vina: -9.165

vina: -10.631

Supporting molecule

HO

OH

title:DIDH_RAT_1_319_0

title:DIDH_RAT_1_319_0

：Unreasonable

：Reasonable

vina:-8.26

title:COAA_MYCTU_1_312_0

title:COAA_MYCTU_1_312_0

:Unreasonable

:Reasonable

vina: -7.572

vina: -8.731

title:M3K14_HUMAN_321_678_0

title:M3K14_HUMAN_321_678_0

：Unreasonable

：Reasonable

vina:-7.453

vina:-9.209

HN

title:CHIB1_ASPFM_39_433_0

title: CHIB1_ASPFM_39_433_0

:Unreasonable

：Reasonable

vina:-8.054

vina:-10.096

title:SIR3_HUMAN_117_398_0

title:SIR3_HUMAN_117_398_0

：Unreasonable

：Reasonable

vina: -6.991

vina:-10.758

title:NOS3_HUMAN_65_480_0

title:NOS3_HUMAN_65_480_0

: Unreasonable

: Reasonable

vina:-9.583

vina:-10.652

HO

HO

HO

HO

title:DIDH_RAT_1_319_0

title:DIDH_RAT_1_319_0

：Unreasonable

：Reasonable

CIDD output

Supporting molecule

CIDD output

DH

title:BACE2_HUMAN_76_460_0

title:BACE2_HUMAN_76_460_0

:Unreasonable

：Reasonable

vina:-8.586

vina:-9.854

HC

OH

OH

OH

NH

title:F16P1_HUMAN_1_338_0

title:F16P1_HUMAN_1_338_0

：Unreasonable

：Reasonable

vina:-8.173

vina:-8.633

title: CAT_ECOLX_1_219_0

title: CAT_ECOLX_1_219_0

:Unreasonable

:Reasonable

vina:-9.838

vina: -10. 164

OH

title:DFPA_LOLVU_2_314_0

title:DFPA_LOLVU_2_314_0

:Unreasonable

：Reasonable

vina: -7.208

vina:-8.581

title:QPCT_HUMAN_33_361_0

title:QPCT_HUMAN_33_361_0

：Unreasonable

:Reasonable

vina:-8.178

vina:-9.527

HO'

OH

title:KS6A3_HUMAN_41_357_0

title:KS6A3_HUMAN_41_357_0

:Unreasonable

：Reasonable

vina:-8.738

title: AROE_THET8_1_263_0

title:AROE_THET8_1_263_0

:Unreasonable

:Reasonable

vina: -8.741

vina: -8.794

Supporting molecule

CIDD output

HO-

HO

OH

title:GRK4_HUMAN_1_578_0

title:GRK4_HUMAN_1_578_0

：Unreasonable

：Reasonable

vina: -7.158

vina:-8.93

日

:Unreasonable

：Reasonable

vina:-10.201

vina: -11. 121

title:BSD_ASPTE_1_130_0

title:BSD_ASPTE_1_130_0

：Unreasonable

:Reasonable

vina:-8.786

HN

title:RG1_RAUSE_1_513_0

title:RG1_RAUSE_1_513_0

：Unreasonable

：Reasonable

vina: -10. 06

vina:-11.978

title:QPCT_HUMAN_33_361_0

title:QPCT_HUMAN_33_361_0

：Unreasonable

: Reasonable

vina: -7.531

vina:-8.356

HN

title:GUX1_HYPJE_18_451_0

title:GUX1_HYPJE_18_451_0

:Unreasonable

：Reasonable

vina: -7.25

vina:-8.375

title: UPPS_ECOLI_1_253_0

title: UPPS_ECOLI_1_253_0

:Unreasonable

：Reasonable

vina:-7.288

vina:-8.315

Supporting molecule

CIDD output

title:CAT_ECOLX_1_219_0

title:CAT_ECOLX_1_219_0

: Unreasonable

: Reasonable

vina:-9.836

vina: -11. 246

title:NOS2_HUMAN_78_505_0

title:NOS2_HUMAN_78_505_0

：Unreasonable

：Reasonable

vina:-10.504

vina: -11.006

title:BTRN_BACCI_2_250_0

title:BTRN_BACCI_2_250_0

: Unreasonable

：Reasonable

vina: -4.306

vina: -11. 729

title:ACE_HUMAN_650_1230_0

title:ACE_HUMAN_650_1230_0

:Unreasonable

:Reasonable

vina:-9.498

vina:-10.987

title:AKT1_HUMAN_1_137_0

title: AKT1_HUMAN_1_137_0

：Unreasonable

:Reasonable

vina: -11.185

vina: -11.293

OH

title:BAPA_SPHXN_30_402_0

title:BAPA_SPHXN_30_402_0

:Unreasonable

:Reasonable

vina:-8.683

Supporting molecule

CIDD output

OH

OH

OH

title:ATS5_HUMAN_262_480_0

title:ATS5_HUMAN_262_480_0

: Unreasonable

：Reasonable

vina: -8.133

vina:-8.863

HO

HO

HO

title:NOS2_HUMAN_78_505_0

title:NOS2_HUMAN_78_505_0

：Unreasonable

：Reasonable

vina: -10.76

vina:-12.037

title:ACE_HUMAN_650_1230_0

title:ACE_HUMAN_650_1230_0

：Unreasonable

：Reasonable

vina:-8.564

vina:-10.503

N

NH

HN

title:ACE_HUMAN_650_1230_0

title:ACE_HUMAN_650_1230_0

：Unreasonable

：Reasonable

vina:-8.69

title:TBK1_HUMAN_1_303_0

title:TBK1_HUMAN_1_303_0

:Unreasonable

：Reasonable

vina:-8.868

H,N

title:BAPA_SPHXN_30_402_0

title:BAPA_SPHXN_30_402_0

：Unreasonable

:Reasonable

vina: -7.794

vina: -8.198

Supporting molecule

CIDD output

title:

title:

PHKG1_RABIT_6_296_ATPsite_0

PHKG1_RABIT_6_296_ATPsite_0

：Unreasonable

：Reasonable

vina:-8.922

vina:-9.205

title:RG1_RAUSE_1_513_0

title:RG1_RAUSE_1_513_0

：Unreasonable

：Reasonable

vina:-10.484

vina:-11.024

title:CD38_HUMAN_44_300_0

title:CD38_HUMAN_44_300_0

:Unreasonable

：Reasonable

vina: =8.72

vina: -9.327

title:XANLY_BACGL_26_777_0

title:XANLY_BACGL_26_777_0

：Unreasonable

:Reasonable

vina:-9.053

vina:-9.483

HO

HO

title:PPIA_HUMAN_1_165_0

title:PPIA_HUMAN_1_165_0

:Unreasonable

：Reasonable

vina: -8.023

vina:-8.605

title:SQHC_ALIAD_1_631_0

title:SQHC_ALIAD_1_631_0

:Unreasonable

:Reasonable

vina: -11.51

vina:-14.292

Supporting molecule

CIDD output

HO

HN

title: BGL07_ORYSJ_25_504_0

title: BGL07_ORYSJ_25_504_0

:Unreasonable

：Reasonable

vina: -10.446

vina:-10.592

title:DPP2_HUMAN_27_492_0

title:DPP2_HUMAN_27_492_0

:Unreasonable

：Reasonable

vina:-9.855

vina:-11.224

title:PTGIS_HUMAN_20_500_0

title:PTGIS_HUMAN_20_500_0

：Unreasonable

：Reasonable

vina:-8.318

vina:-9.448

NHz

HzN

title:PPIA_HUMAN_1_165_0

title:PPIA_HUMAN_1_165_0

:Unreasonable

：Reasonable

vina:-8.777

vina:-10.083

HN

title:HMD_METJA_1_358_0

title:HMD_METJA_1_358_0

:Unreasonable

：Reasonable

vina:-8.349

vina: -9.593

title:KS6A3_HUMAN_41_357_0

title:KS6A3_HUMAN_41_357_0

：Unreasonable

：Reasonable

vina:-8.967

vina: -11.101

Supporting molecule

CIDD output

title:KS6A3_HUMAN_41_357_0

title:KS6A3_HUMAN_41_357_0

:Unreasonable

：Reasonable

vina: -9.414

title:NR1H4_HUMAN_258_486_0

title:NR1H4_HUMAN_258_486_0

：Unreasonable

：Reasonable

vina: -14.043

title:SDIA_ECOLI_1_171_0

title:SDIA_ECOLI_1_171_0

:Unreasonable

：Reasonable

vina:-10.997

vina: -13. 15

OH

HO

title:HDHA_ECOLI_1_255_0

title:HDHA_ECOLI_1_255_0

:Unreasonable

:Reasonable

vina:-9.845

vina:-9.957

title:IDHP_HUMAN_40_452_0

title:IDHP_HUMAN_40_452_0

:Unreasonable

：Reasonable

vina:-8.034

title:AKT1_HUMAN_1_137_0

title:AKT1_HUMAN_1_137_0

：Unreasonable

：Reasonable

vina: -10.124

vina:-10.13

Supporting molecule

CIDD output

HN

HO

title:DYRK2_HUMAN_145_550_0

title:DYRK2_HUMAN_145_550_0

:Unreasonable

:Reasonable

vina:-6.725

vina:-9.337

HO

HO

title:BACE2_HUMAN_76_460_0

title:BACE2_HUMAN_76_460_0

：Unreasonable

：Reasonable

vina: -9.145

vina:-9.806

title:TRAR_RHIRD_1_234_0

title:TRAR_RHIRD_1_234_0

:Unreasonable

：Reasonable

vina:-8.935

vina:-12.018

title:UPPS_ECOLI_1_253_0

title:UPPS_ECOLI_1_253_0

：Unreasonable

：Reasonable

vina:-7.717

vina:-10.031

title:ABL2_HUMAN_274_551_0

title:ABL2_HUMAN_274_551_0

：Unreasonable

：Reasonable

vina: -10. 776

OH

title:

title:

: Unreasonable

:Reasonable

vina: -10.487

vina:-10.893

Supporting molecule

CIDD output

HO

OH

HO

OH

title:

title:

MURA_ECOLI_1_419_catalytic_0

MURA_ECOLI_1_419_catalytic_0

:Unreasonable

：Reasonable

vina:-8.295

vina: =9.241

HzN-

title:BGL07_ORYSJ_25_504_0

title:BGL07_ORYSJ_25_504_0

：Unreasonable

:Reasonable

vina:-8.028

vina: -9.632

title:PAC_ECOLX_27_846_0

title:PAC_ECOLX_27_846_0

：Unreasonable

：Reasonable

vina:-9.538

vina:-10.042

HO

title: EXG1_CANAL_41_438_0

title:EXG1_CANAL_41_438_0

:Unreasonable

：Reasonable

vina:-9.965

vina:-10.569

title:CHIB_SERMA_1_499_0

title:CHIB_SERMA_1_499_0

：Unreasonable

:Reasonable

vina:-10.354

title:SDIA_ECOLI_1_171_0

title:SDIA_ECOLI_1_171_0

：Unreasonable

：Reasonable

vina:-11.339

Supporting molecule

CIDD output

10

title:BGL07_ORYSJ_25_504_0

title:BGL07_ORYSJ_25_504_0

:Unreasonable

:Reasonable

vina:-8.187

OH

title:PAC_ECOLX_27_846_0

title:PAC_EC0LX_27_846_0

:Unreasonable

:Reasonable

vina:-8.313

HO-

-OH

HO

HO

SHHO

title:P2Y12_HUMAN_1_342_0

title:P2Y12_HUMAN_1_342_0

:Unreasonable

：Reasonable

vina:-8.514

vina:-8.942

HO

title:FKB1A_HUMAN_2_108_0

title: FKB1A_HUMAN_2_108_0

:Unreasonable

:Reasonable

vina: -7.899

vina:-8.786

title:KS6A3_HUMAN_41_357_0

title:KS6A3_HUMAN_41_357_0

:Unreasonable

：Reasonable

title:GUX1_HYPJE_18_451_0

title:GUX1_HYPJE_18_451_0

：Unreasonable

：Reasonable

vina:-12.3

Supporting molecule

CIDD output

HO.NH2

OH

title:HDHA_ECOLI_1_255_0

title:HDHA_ECOLI_1_255_0

:Unreasonable

:Reasonable

vina:-8.511

vina:-9.995

title:IDHP_HUMAN_40_452_0

title:IDHP_HUMAN_40_452_0

：Unreasonable

:Reasonable

vina: -8.039

vina: -9.819

title:NOS1_HUMAN_302_723_0

title:NOS1_HUMAN_302_723_0

：Unreasonable

:Reasonable

vina:-9.495

HO

HO

HzN

title:CAT_ECOLX_1_219_0

title:CAT_ECOLX_1_219_0

:Unreasonable

:Reasonable

vina:-13.057

title:ABL2_HUMAN_274_551_0

title:ABL2_HUMAN_274_551_0

:Unreasonable

：Reasonable

vina: -10.702

vina:-11.583

OH

HN

HzN

title:BTRN_BACCI_2_250_0

title:BTRN_BACCI_2_250_0

：Unreasonable

:Reasonable

vina:-8.786

vina:-8.951

title:AKT1_HUMAN_1_137_0

title:AKT1_HUMAN_1_137_0

:Unreasonable

:Reasonable

vina:-10.1

vina: -11. 478

Supporting molecule

CIDD output

title:ODBB_THET8_1_324_0

title:ODBB_THET8_1_324_0

: Unreasonable

：Reasonable

vina:-9.322

：Unreasonable

：Reasonable

vina: -9.196

vina:-12.034

HO

HO

OH

title:N0S1_HUMAN_302_723_0

title:NOS1_HUMAN_302_723_0

:Unreasonable

：Reasonable

vina:-10.062

vina:-10.305

HO

HO

title:BSD_ASPTE_1_130_0

title:BSD_ASPTE_1_130_0

：Unreasonable

：Reasonable

vina:-8.807

vina: -9.501

title:NQO1_HUMAN_2_274_0

title:NQ01_HUMAN_2_274_0

:Unreasonable

：Reasonable

vina: -9.262

vina: -10.424

：Unreasonable

：Reasonable

vina:-7.206

vina:-8.307

H0

title:AKT1_HUMAN_1_137_0

title:AKT1_HUMAN_1_137_0

:Unreasonable

:Reasonable

vina: -12.405

vina:-13.048

Supporting molecule

title:PA21B_PIG_23_146_0

title:PA21B_PIG_23_146_0

:Unreasonable

:Reasonable

vina:-8.391

vina:-10.029

N

NH

HO

OH

HO

NH

HO

OH

title:M3K14_HUMAN_321_678_0

title:M3K14_HUMAN_321_678_0

:Unreasonable

:Reasonable

vina:-9.001

NH

-OH

title:PTGIS_HUMAN_20_500_0

title:PTGIS_HUMAN_20_500_0

：Unreasonable

：Reasonable

vina: -7.955

title:PAC_ECOLX_27_846_0

title:PAC_ECOLX_27_846_0

：Unreasonable

:Reasonable

vina:-9.272

vina: =9.421

OH

OH

OH

title:XANLY_BACGL_26_777_0

title:XANLY_BACGL_26_777_0

:Unreasonable

:Reasonable

vina:-6.991

vina:-8.994

title:PPIA_HUMAN_1_165_0

title:PPIA_HUMAN_1_165_0

：Unreasonable

：Reasonable

vina: -7.765

vina: -8.447

CIDD output

Supporting molecule

CIDD output

title:

title:

PHKG1_RABIT_6_296_ATPsite_0

PHKG1_RABIT_6_296_ATPsite_0

:Unreasonable

:Reasonable

vina:-10.937

vina:-13.466

NH

NH

title:M3K14_HUMAN_321_678_0

title:M3K14_HUMAN_321_678_0

:Unreasonable

:Reasonable

vina:-9.44

vina:-9.899

title:PAC_ECOLX_27_846_0

title:PAC_EC0LX_27_846_0

：Unreasonable

：Reasonable

vina: -7.907

vina: -8.704

title:TNKS2_HUMAN_948_1162_0

title:TNKS2_HUMAN_948_1162_0

:Unreasonable

：Reasonable

vina:-10.215

vina:-12.537

H2N

title:PPIA_HUMAN_1_165_0

title:PPIA_HUMAN_1_165_0

:Unreasonable

：Reasonable

vina:-9.243

HO

OH

HO

OH

title:P2Y12_HUMAN_1_342_0

title:P2Y12_HUMAN_1_342_0

：Unreasonable

：Reasonable

vina:-8.744

vina:-10.352

Supporting molecule

CIDD output

DE

OF

HO-

-OH

O

HO

HO

H

title:P2Y12_HUMAN_1_342_0

title:P2Y12_HUMAN_1_342_0

：Unreasonable

：Reasonable

vina:-8.31

vina:-9.509

HO

HO

HO

title:MENE_BACSU_2_486_0

title:MENE_BACSU_2_486_0

:Unreasonable

:Reasonable

vina:-8.029

vina:-8.629

N

OH

HO

title:CHIB_SERMA_1_499_0

title:CHIB_SERMA_1_499_0

:Unreasonable

：Reasonable

vina:-7.389

vina:-9.388

title:DYRK2_HUMAN_145_550_0

title:DYRK2_HUMAN_145_550_0

:Unreasonable

:Reasonable

vina:-7.898

vina:-9.098

title:NR1H4_HUMAN_258_486_0

title:NR1H4_HUMAN_258_486_0

:Unreasonable

：Reasonable

vina:-8.974

title:BACE2_HUMAN_76_460_0

title:BACE2_HUMAN_76_460_0

:Unreasonable

:Reasonable

vina:-9.473

vina:-9.828

title:TRAR_RHIRD_1_234_0

title:TRAR_RHIRD_1_234_0

:Unreasonable

：Reasonable

vina:-10.182

vina:-13.112

Supporting molecule

CIDD output

title:EXG1_CANAL_41_438_0

title:EXG1_CANAL_41_438_0

：Unreasonable

：Reasonable

vina:-10.072

vina:-10.17

HN

HO

HO

title: SQHC_ALIAD_1_631_0

title:SQHC_ALIAD_1_631_0

:Unreasonable

:Reasonable

vina:-9.519

title:SIR3_HUMAN_117_398_0

title:SIR3_HUMAN_117_398_0

：Unreasonable

：Reasonable

vina:-10.258

vina: -10.419

title:N0S3_HUMAN_65_480_0

title:N0S3_HUMAN_65_480_0

:Unreasonable

：Reasonable

vina:-10.535

HO-

HO

OH

title:BACE2_HUMAN_76_460_0

title:BACE2_HUMAN_76_460_0

：Unreasonable

：Reasonable

vina:-9.302

vina: =9.947

title:SDIA_ECOLI_1_171_0

title:SDIA_ECOLI_1_171_0

：Unreasonable

：Reasonable

vina:-8.889

vina:-13.779

title: TRAR_RHIRD_1_234_0

title:TRAR_RHIRD_1_234_0

:Unreasonable

：Reasonable

vina:-9.69

Supporting molecule

CIDD output

Supporting molecule

CIDD output

C

NH

OH

OH

OH

HO

OH

HO

OH

o-S=o

HzN

NH

title:POL_F0AMV_861_1060_0

title:P0L_F0AMV_861_1060_0

title:HDHA_ECOLI_1_255_0

title:HDHA_ECOLI_1_255_0

：Unreasonable

：Reasonable

：Unreasonable

：Reasonable

vina:-7.797

vina:-8.698

vina:-9.535

I Limitation on Computational Cost and Scalability

We acknowledge that our framework introduces additional computational steps due to the use of LLMbased modules. Below, we provide a detailed discussion of the computational overhead, empirical runtime, and justification for its practicality in the context of drug discovery.

Computational Overhead of LLM Pipelines. The runtime and token-related costs associated with repeated LLM invocations-such as scaffold analysis, molecule generation, self-reflection, and candidate selection-can introduce non-trivial computational overhead. However, several factors mitigate this concern:

LLMInference Efficiency Is Rapidly Improving. Recent advances in model quantization, prompt batching, and optimized inference are dramatically reducing the cost of multi-step reasoning. For example, DeepSeek-V3/R1 achieves approximately 2.19M output tokens per dollar, reflecting a 60-70% cost reduction compared to GPT-4 in 2023. Frameworks such as vLLM [18], vTensor [36], and Splitwiser [1] further improve efficiency: vLLM enables up to 24 × throughput; vTensor achieves 2 × speedups with 70% less GPU memory; and Splitwiser reduces latency by 1 . 7 × through prompt colocation. Hardware accelerators like TensorRT-LLM also support highly scalable domain-specific pipelines. These trends suggest that the cost of multi-step reasoning is 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 diffusion-based methods such as TargetDiff and DecompDiff require approximately 3428 seconds and 6189 seconds, respectively.

As our method builds upon base SBDD models as the SBIG module, we only consider the additional time introduced by the LEDD module. With parallel LLM API requests, the total time required for generating 100 molecules is approximately 15 minutes in 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 is unlikely to remain a bottleneck as inference efficiency continues to improve. For instance, recent advances in Diffusion-LLMs [31] have demonstrated inference speeds exceeding 2000 tokens per second, suggesting the potential for up to a 20 × acceleration of our pipeline in the near future.

Molecule Design Is Not a Real-Time Task. Unlike latency-sensitive domains such as recommendation 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 plausibility aligns with the broader goal of improving experimental success rates, rather than immediate response times.

Relative Cost Compared to Wet-Lab Validation. In the broader context of drug development-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 downstream 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.

J Results on more targets

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[28]. 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[32]. 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[35].

K Ablation Study: Reasoning Design vs. Simple Prompting

To investigate the source of our framework's effectiveness, we conducted an ablation study comparing different prompting strategies. Specifically, we replaced our original chain-of-thought (CoT), domaininformed 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.

Despite using the same LLM and external tools, the simplified prompting strategy performs significantly worse than our CoT-guided approach, and only marginally better than the MolCRAFT

baseline. This highlights that the primary gains of our method stem from structured, domain-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-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).

These results demonstrate that, even when starting from already high-quality ligands, our framework is capable of discovering improved candidates through domain-aware multi-agent reasoning.

Wenote that fully supporting hit-to-lead tasks would require adapting the pipeline structure, modifying the chain-of-thought prompts, and refining the integration of design tools to better align with lead optimization objectives. Nonetheless, the observed performance underscores the broader applicability of our framework and the potential of structured LLM 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 [ ? ]. 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 LLM.

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.

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

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Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThrough 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.\n\nSTRENGTHS AND WEAKNESSES:\n**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.\n\nQUESTIONS:\n- 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.\n\nLIMITATIONS:\nThe 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.", "TITLE": "Official Review", "RECOMMENDATION": 3, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 1, "ORIGINALITY": 1, "DATE": "2025-06-17"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe 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.\n\nSTRENGTHS AND WEAKNESSES:\n## 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.\n\nQUESTIONS:\nHow 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?\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-01"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\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\n\nQUESTIONS:\n1. 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.\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-07-01"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\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.\n\nQUESTIONS:\n1. 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?\n\nLIMITATIONS:\nYes.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 4, "IMPACT": 4, "ORIGINALITY": 4, "DATE": "2025-07-02"}