DrugTrail: Interpretable Drug Discovery via Structured Reasoning and Druggability‑Tailored Preference Optimization
Authors: Yurou Liu
Mingyang Li
Xinyuan Zhu
Rui Jiao
Yiming Dong
Xinyu Tang
Yang Liu
Jieping Ye
Bing Su
Zheng Wang
Venue: ICLR 2026 Poster (2026)
Keywords: LLM-based drug discovery
Explainability
Structured reasoning
Druggability‑tailored preference optimization
Primary area: applications to physical sciences (physics, chemistry, biology, etc.)
TL;DR: We propose DrugTrail, a novel paradigm for explainable drug discovery that integrates both structured reasoning trajectories and druggability‑tailored preference optimization.

Abstract: Machine learning promises to revolutionize drug discovery, but its "black-box" nature and narrow focus limit adoption by experts. While Large Language Models (LLMs) offer a path forward with their broad knowledge and interactivity, existing methods remain data-intensive and lack transparent reasoning. To address these issues, we present DrugTrail, an LLM-based framework for explainable drug discovery that integrates structured reasoning trajectories with a Druggability‑Tailored Preference Optimization (DTPO) strategy. It not only introduces structured reasoning traces to articulate the "how" and "why" behind its conclusions but also serve to guide task-specific reasoning pathways within the LLM's vast knowledge space, thereby enhancing its interpretability and reliability of its final outputs. Furthermore, based on the fact that optimizing for binding affinity alone does not equate to optimizing for druggability, DTPO explicitly moves beyond single-metric optimization and opens up a broader search space that balances affinity with other essential factors. Extensive experiments demonstrate the effectiveness of our approach and its generalizability to a wider range of biomolecular optimization domains, bridging the gap between LLM reasoning capabilities and trustworthy AI-assisted drug discovery.

Published as a conference paper at ICLR 2026
DRUGTRAIL: INTERPRETABLE DRUG DISCOVERY VIA STRUCTURED REASONING AND BRUGGABILITY-AILLORE
PREFERENCE OPTIMIZATION
Yurou Liu1.2.455 Mingyang Li,, Xinyuan Zhu², Rui Jiao", Yiming Dong", Xinyu Tang1.4.5,
Yang Liu Jieping Ye Bing Su ZZhnng Wang?*
1 Gaoling School of Artificial Intelligence, Renmin University of China, Beijing, China
2 Alibaba Cloud Computing, Beijing, China
3 Department of Computer Science and Technology, Tsinghua University, Beijing, China
4 Beijing Key Laboratory of Research on Large Models and Intelligent Governance
5 Engineering Research Center of -ext-reenrr Intelligent Search and Recommendation, MOE yurouliug9@gmail. com
ABSTRACT
Machine learning promises revolutionize drug discovery, but its "black-box" nature and narrow focus limit adoption by experts. While Large Language Models (LLMs) offer path forward with their broad knowledge and interactivity, existing methods remain data intenssieevvv and lack transparent reasoning. To address these is- sues, we present DRUGTRAIH AII., an L.LM-based framework for interpretable drug dis- covery that integrates structured reasoning trajectories with Druggability-Tailore Preference Optimization (DTPO) strategy. It not only introduces structured rea- soning traces articulate the "how" and "why" behind its conclusions but also serve to guide task-specific reasoning pathways within the LLM's vast knowledge space, thereby enhancing its interpretability and reliability of its final outputs. Furthermore, based on the fact that optimizing for binding affinity alone does not equate optimizing for druggability, DTPO explicitly moves beyond single- metric optimization and opens up a broader search space that balances affinity with other essential factors. Extensive experiments demonstrate the effectiveness of our approach and its generalizability to a wider range of biomolecular optimization domains, bridging the gap between LLM reasoning capabilities and trustworthy Al-assisted drug discovery.
INTRODUCTION
The rapid development of machine learning (ML) techniques has opened up tremendous potential to accelerate drug discovery (Schneider et al., 2019 Irwin & Shoichet 2016). By automating virtual screening, molecular docking, and molecular editing, Ma-ed methods can greatly reduce both time
and cost in carly-stage drug development (Gorgulla et al. 2020 Lyu al. 2019 Gentile et al.) 2020). However, most existing AI tools remain constrained by narrow, domain-specifer knowledge and tend to operate black boxes, offering limited transparency in their decision-making processes Yu et al. 2024). Such limited interpretability impedes collaborations with domain experts, constraining effective analysis and refinement within real biomedical workflows (Li al. 2023).
Recently, large language models (LLMs) have emerged as general-purpose systems with broad cross-domain knowledge and the capability generate human-readable explanations (Edwards et al.) 2022 2021). While these features position them promising candidates for assisting drug discovery. most existing LLM-based methods require large-scale biologically relevant data for pre-training (Xia et al. 2025). This requirement not only poses challenges due to the scarcity of high-quality biological datasets, but also entails substantial computational costs. Moreover, current methods typically focus on producing final predictions without explicitly revealing the intermediate reasoning steps. In high-stakes field like drug discovery, understanding the reasoning process is as important as the
"Correspondence Bing Su chingsu@ruc.edu.coo Zheng Wang <w738877966 @ aatir/99aaliaaiiinee...a alibaha-ine.com..

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Published as a conference paper at ICLR 2026
results themselves, it improves interpretability and allows domain experts to contextualize and adapt Al-generated hypotheses. Notably, prior work has demonstrated that instead of building domain-specifie models through costly pre-training, reinforcement learning (RL) can unlock domain- relevant knowledge already embedded in general-purpose LLMs, as evidenced in tasks such as mathematics and programming (Yu et al. (025). Plus, modest amount of supervised fine-tuning (SFT) is sufficient to bootstrap their reasoning capabilities (Guo et al., 2025a). Therefore, exploring RL approaches with reasoning holds significant potential for advancing LLM-based drug discovery.
However, enabling a seasoning-enhaneee RL framework for drug discovery faces two core challenges. First, the reasoning process should follow biochemistry-nffome however, lack structural reasoning trajectories that can serve as guidance for learning domain-specins reasoning patterns. Second, designing an appropriate reward remains a critical bottleneck. Previous methods often rely on affinity scores for guidance, but directly optimizing binding affinity does not necessarily translate into drug-likeness in real-worid scenarios. Factors such as binding duration, synthetic accessibilityy and potential off-target inhibition also play essential roles in determining druggability (Zhang et al. 2025). Therefore, an effective RL framework must incorporate a more comprehensive reward design that better aligns with practical pharmacological criteria.
modes thinking. Current datasets,
In this work, we introduce DRUGTRAIL, an LLM-based framework for interpretable drug discovery. To address the scarcity of explicit reasoning data, we leverage existing pocket-ligand pairs and prompt the language model to complete multi-dimensional reasoning trajectories under biochemistry- informed rules. Based the resulting pecket-resoonig-- -igand data, we apply supervised fine-tuning (SFT) to guide the model toward generating structural and interpretable reasoning. Subsequently, rather than relying on time-consuming affinity scores as optimization targets, DRUGTRAIL incor- porates a Druggability-Tailord Preference Optimization (DTPO) strategy. This strategy jointly considers the similarity between generated candidates and known drug-like molecules, as well as rule-based indicators of intrinsic druggability, thereby enabling efficient online computation while maintaining strong connection between rewards and drug-likeness. sum up, our contributions are as follows:
Interpretable Framework: We design DRUGTRAIL, a novel paradigm for drug design, in which lightweight reinforcement learning activates LLMs' capability to generate interpretable, high-quality biomolecular candidates.
Druggable Optimization: We propose Druggability-Tailon Preference Optimization (DTPO), a computationally efficient, online-computable preference scheme with a hybrid reward coupling candidate-dru similarity with rule-based intrinsic drug- likenesss indicators. We also release a large- scale bioactive reference dataset to facilitate future research.
Generalizable Performance: Extensive experiments across de novo pocket-based drug design, small-molecule editing, and protein optimization demonstrate consistently strong performance and transferability, paving the way toward more interpretable and widely adoptable Al tools in pharma- ceutical research.
2
METHOD
In this section, we introduce DRUGTRAIL, an interpretable framework for drug discovery designed to overcome two primary limitations of existing methods: (1) the absence of transparent reasoning processes that align with established principles of medicinal chemistry, and (2) the constrained opti- mization focus on binding affinity. which often neglects other critical factors for druggability (Pantsar & Poso 2018) As illustrated in Fig. [ DRUGTRAIL is composed of two core modules. First, a Clinical Seremistry-Infomee Reasoning (CCIR) module generates structured and interpretable reasoning trajectories. Second, Druggability-Tailln -aiilored Preference Optimization (DTPO) strategy optimizes for a comprehensive set of drug-like properties, moving beyond single-metric objectives.
'Generally, binding affinity refers to the strength of the interaction between a ligand and its biological target, while druggability describes whether a molecule has the properties and potential required to be developed into safe and effective therapeutic drug.
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Clinical Chemistry-Inforeed Informed Reasoning
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The overall framework of DRUGTRAIL. The upper part illustrates the Clinical Chem- Inforeed Reasoning (CCIR) process. The lower part presents the workflow of Druggability- Tailored Preference Optimization (DTPO).
Figure 1:
2.1
CLINICAL CHEMISTRY-INFORMEE REASONING (CCIR)
2.1.1
STRUCTURED REASONING DIMENSION CONSTRUCTION
To address the limited interpretability of current language models in drug discovery, our primary objective to construct reasoning framework that emulates the decision-making making processes of medicinal chemists. The goal is to enhance the credibility and reliability of the model's predictions by rendering its reasoning process transparent and concordant with established scientific principles. A central challenge in this endeavor is the definition of structured reasoning dimensions capable of effectively guiding the model's inference pathway.
Drawing upon the demonstrated success of Large Language Models (LLMs) in complex reasoning tasks such as mathematics and code generation, we hypothesize that general-purpose LLMS possess substantial latent knowledge relevant to specialized domains. To clicit this knowledge, designed structured prompt to query foundational model, Qwen3-235B- A22B, thereby extracting common reasoning perspectives pertinent to clinical drug discovery. This process yielded five reason- ing dimensions: (1) In-depth physicochemical profiling; (2) Maintenance of core structural and functional integrity; (3) Prior knowledge and chemical structural space guidance; (4) Conser- vation analysis and identification of critical sites; (5) Optimization and multi-attributt balancing. Together, these five dimensions form the foundation of reasoning framework, which we further operationalize in subsequent sections.
2.1.2 TRAJECTORY GENERATION FROM MULTI-OBIECTIVE QUALITY CONTROL
To enable the model to explicitly learn these clinical chemistry-informed thinking modes, we constructed a high-quality dataset for supervised fine-tuning. We sampled data from the training split of the Cross-Docked2020 dataset (Francoeur et al.) 2020 where each sample comprises a protein binding pocket and its corresponding interacting ligand. For each sam- ple, we formulated question and corresponding answer that contains reasoning trajec- tory structured according to the five aforemmentioeed dimensions. Specifically, we instructed the large language model complete the reasoning process that connects the pocket its
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interacting ligand. To ensure clarity and consistency, the reasoning output formatted with special tokens assigned to each dimension: Sataratterraatioo...c/caaaaaraae rStability2../$ttbbiittt and http:iaaiio.....p/iiiiiiiiiiiiia comprehensive Meakket-Rasoning--- reliability of these generated trajectories, we implemented a three-stage data filtering pipeline.
Through this design, we obtained the initial version of structural dataset. To further ensure the quality and
Conflict Resolving, To enhance the logical coherence the reasoning trajectories, we implemented conflict resolution protocol. For each imput sample, multiple reasoning candidates were generated using stochastic decoding or an ensemble of models. These diverse trajectories were then evaluated by a powerful adjudicator LLM assess their internal consistency. Trajectories containing logical fallacies, self-contrddictions, or conflicting biochemical claims were identified and discarded, ensuring that only the most robust and logically sound reasoning paths were retained for fine-tuning.
Thinking Budget Instruction. We hypothesize that effective reasoning should be both comprehen- sive and concise, avoiding unnecessary verbosity or overly simplistic explanations. To enforce this, introduce "thinking budget" constraint. This operationalized through a filtering mechanism that penalizes reasoning trajectories whose lengths fall outside a predefined range, thereby discourag- ing both excessively long and perfunctory outputs. Trajectories that are identified outliers in length are discarded, guiding the model to learn more focused and efficient reasoning style. Further details on the implementation of this heuristic are provided in the Appendix
Domain Consistency. To ensure that the model-generated reasoning aligns with the cognitive patterns of human experts, we employ a domain consistency check grounded in an adversarial validation framework. We curate small set of "golden" reasoning trajectories authored by domain experts. These expert examples are then combined with the LLM-generated trajectories, and a separate LLM serves classifier reject the generated trajectories that deviate significantly from ones. By selectively retaining these consistent samples, we ensure the final the training data closely aligned with established expert knowledge and thought processes.
2.1.3 DOMAIN KNOWLEDGE ENHANCEMENT FROM SUPERVISED FINE-TUNING
Following the multi-stage data curation and quality control pipeline described above, we obtained corpus of 2,000 high-quality annotated samples for domain-specifie Supervised Fine-Tuning (SFT). Each sample consists of problem statement (question) and structured expert-informed solution (answer). The answer is organized a structured reasoning trajectory consisting of six parts: five clinical chemistry-informed analytical aspects introduced in $ 2.2.2 and the final answer segment <Answer>, </Answer> containing the final analysis and the proposed molecule in canonical SMILES notation (Weininger 1988) (e.g. (Answer>Based
the above analysis, the final answer is \boxed{ CcO)<</nswer>>) Given the collected SFT datasct, we adopt a standard cross-entropy loss to align the base model with the structural reasoning format and medicinal chemistry knowledge. This SFT phase provides the model with a structured cognitive prior, enhancing both its interpretability and reliability in downstream drug design tasks.
2.2
DRUGGABILITY- TATTCABRR.TYY-TTOTORRR PREFERENCE OPTIMIZATION (DTPO)
2.2.1
GROUP RELATIVE POLICY OPTIMIZATION (GRPO)
GRPO is reinforcement learning framework that eliminates value models by estimating group-wise relative advantages. Specifically, for each question q D, first sample group of G responses {o.) from the old policy model Told. The policy model is then optimized using a clipped surrogate objective with a KI. regularization term to enhance training stability, given by.
(or = -BByD, (o (oTa((l))))))))
min(re, Ar,, clip(ri,t, - e,, 1 ++))A.) ( ). ())
Σ
G
10.
1=1
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where ri,a To( 19,0,<t) / Teai (0s, I 9, 01,<1) is the importance weights for the t-th token Ot, The advantage is estimated from group-normalizee rewards {R, )
R. mean ((R:)
std (R.,)
2.2.2 CONSTRUCTION PREFERENCE DATA
To steer the generative model towards regions of the chemical space with superior druggability, implemented hybrid ligand- and structure-basee based data curation pipeline. The objective to construct a preference dataset that reflects a nuanced combination of established bioactivity and structural compatibility, transcending simple binding affinity metrics.
Target-Centric Retrieval of Bioactive Compounds. For each protein pocket in the CrossDocked dataset, we first identified its biological target, and then systematically queried the PubChem database (Kim et al., 2016) to compile comprehensive set of molecules with experimentally validated activity against this target. This step enriches our initial candidate pool with biologically relevant chemical scaffolds.
Structure-Bassdt Virtual Sereening and Validation. The retrieved bioactive compounds subse- quently underwent virtual screening via molecular docking with AutoDock Vina (Trott & Olson 2010). For each pocket, we retained the ground truth ligand and candidate molecules from the top 20 docking results with Vina score lower than -7. This strategy ensures that every molecule in final reference set exhibits strong binding potential while also expanding the structural diversity beyond the single co-crystallized ligand in the original dataset.
This curated set provides a rich collection of positive examples that forms the empirical foundation for our reward structure.
2.2.3 REWARD DESIGN
To effectively guide the model's optimization, designed multi component reward function that incorporates principles of medicinal chemistry and structured reasoning. The total reward is a composite of three components, which are detailed as follows:
Ligand-based Reward with Adaptive Ranking. Based on the expanded reference dataset, this component is defined
Riigand (m)
ank Tanimoto(m,,:)
Structural similarity between de novo molecule m and each reference compound r; is quantified via the Tanimoto coefficient (Chen Reynolds, 2002).. References, pre-ranked by docking affinity, are weighted by runk; using an attenuation factor 0.95, thus exponentially prioritizing resemblance to top-ranked ligands and rewarding molecules inheriting their key structural features.
Rule-Based Reward via Soft Lipinski's Rule of Five. To encourage the generation of molecules with favorable physicochemical properties, we implemented Lipinski's 's Rule of Five (ROS) (Lipinski) 2004a) as a soft scoring function. Fach of the four RO5 properties moleeula weight (MW), octanol- water partition coefficient (LogP), hydrogen bond donors (HBD), and hydrogen bond acceptors (HBA)-is mapped to an individual score in (0, 1). The soft score s(x) for property value x with threshold t can be computed using a bounded linear or sigmoid decay, such as:
s(z)
(4)
+ exp(k(z t))
where ls is steepness parameter. The overall RO5 soft score for molecule m is the average of the four property scores:
(m) SMW + LogP + 5HBD + SHBA
(5)
where Ruste (m)) (0, 1), with higher values indicating better diig-likennss.
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Table 1: Results of interaction analysis.
Dock IMP
PLIP Interaction
Model
MPBG (%)
JSD
MABoa
JSDpo J
MAE
Baselines (w Reasoning)
DeepSock-R1
-0.36
0.2
-101.33
0,0814
0.3521
0.5076
0.7041
0.8974
Qwen3-235B-A220
-1.21
0.82
-91.26
0.0576
0.3273
0.5023
0.6815
0,9428
QwQ-32B
10.23
-282.14
-0.4447
0.6674
0,9723
0.7912
1.6940
-h-ni-iB
11.11
0.00
0.7149
1,0461
0.8066
1,5482
Baselines (w/o Reasoning)
DeepSeek-V3 V3
5.91
-176.43
-0,3421
0.4136
0.5276
0.7415
1.3874
Qwen3-2353 232--223B--BB2- non son-thin
7.36
0.00
241.23
-0,3881
0.5021
0.8710
0.7123
1,2136
Llama-3.1-8 1-8B
21.44
0.00
0.7792
1.1613
0.8159
1.7192
Qwen3-1.7B
Base
9.77
0.6762
0,9572
0.7535
1,5012
CCIR
-3.06
8.59
-62.33
0.2599
0.0677
0,1454
0.2311
0.5609
+ CCIR + DTPO
-6.65
33.48
0.83
0,2892
0.0428
0.1197
0.2135
0.4927
Qwen3-4B
Base
12.49
0.7158
0.9791
0.7620
1.4099
CCIR
-3.09
11.18
-61.58
0.2621
0.0603
0.1372
0.2270
0,5341
+ CCIR + DTPO
6.79
38.19
2.59
0.3361
0.0465
0.1263
0.2044
0.4753
Qwen3-8B
0.7328
1.4753
+ CCIR
-3.10
10.93
-60.91
0.2653
0.0625
0.1410
0.1868
0,5269
-6.82
41.01
6.47
0.3407
0.0395
0,0922
0.1782
0.4506
Reasoning Quality Reward. To enforce the correct use of the structured reasoning format, designed token-completeness reward. For each of the six predefined pairs of special tokens (e.g.. <Characterizats weweiiittinn............ http...c/caaaattrriiiioaao generated output, the model receives an additive reward. The total reasoning quality reward for given trace is:
that correctly and completely included in the
Rreasoning (trace) 0.1 x paais (trace)
where panis (trace) E (0. 1. 6) is the count of correctly matched token pairs. This incentivizes the inclusion of all reasoning dimensions, ensuring both completeness and structural clarity.
2.2.4 TRAINING OBJECTIVE
The final training objective of DRUGTRAIL is to optimize the policy model T9 using the GRPO framework. The core of this optimization lies in the design of the reward signal, which guides the model's learning process. The total reward, Reotat- used to compute the advantages within the GRPO loss function is weighted linear combination of the three reward components detailed in $.2.3
Rixal Wiigand Biggand + Wrale Rmile Wreasoning Rrensoning
where the coefficients Wiggand Wrales and Wressoning are non-negative hyperparameters that balance the contribution of each component the total reward. These weights crucial for tuning the model's behavior, allowing us to modulate the emphasis between generating structurally relevant molecules, ensuring their druggability, and maintaining coherent, structured reasoning process.
EXPERIMENTS
3.1 SETTINGS
We employ the CBGBench framework (Lin et al. 2024) to evaluate DRUGTRAIL Cross- docked2020 dataset (Francoeur et al. 2020) across three dimensions: substructure, chemical prop- erties and interaction which are detailed in Appendix. [ We use its established splits with 100 complexes for testing (Peng et al., 2022). . Noted that our evaluation excludes the geometric dimension our method does not perform direct 3D generation. The baselines are detailed in Appendix. C.1.2

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Table 2: Results of chemical property analysis. It should be noted that for drug candidates, optimal LogP values are typically in the range of -0.4 to 5.6, showing no preference toward either higher or lower values within this in- terval Lin et al. 2024).
Table 3: Results of substructure analysis. Functional groups evaluated based on 25 categories identified using the EFG (Salmina et al. 2013). JSD and MAE mea- sure distribution divergence and occurrence frequency dif- ferences between generated and reference substructures.
Atom Type
Ring Type
Function Group
Method
JSD
MAE
JSD 4
JSD 1 MAE
Method
QED1
Loge
SA
LPSK
Qwen3-1.7B
Qwen3-1.78
0.2786
1.7654
0.5528
4:17
+ +CCIR
0,1423
1.0620
0.3015
0.2931
0.3457
0.0677
CCR
0.31
2:56
0.40
4.37
-CCIR
0.0689
0.3113
0.2014
0.1789
0.2476
CCIR DIPO
0:55
1.72
0.70
4,82
Qwen3-IB
QwenI-4B
0,2676
1.5654
4.10
+ CCIR
0.0897
0.3362
0.2311
0.2059
0.2718
0.0518
CCCR
0.30
2.58
0.40
DIPO
0.0453
0.3065
0.1681
0.1219
0,2043
0,0442
CCIR DYPP
0.57
1.71
0.69
4.82
Qwen3-8B
Qwen3-8R
Base
4.12
0.78
0,2612
1.5472
0.5040
0.4929
2.58
+ CCIR
0.0698
0.3107
0.2188
0.1958
0.2399
0.0489
CCIR DTPO
0.57
1.71
0.72
4.81
CCIR + DTPO
0.0401
0.2989
0.1471
0.1108
0.1869
0.0303
3.2
MAIN RESULTS
The interaction analysis results are presented in Tab. Across all model sizes (Qwen3-1.7B. 4B, 8B), our method consistently achieves substantial improvements especially in docking energy (E) and the proportion of superior binders (IMP). Incorporating the reasoning- markedly activates the models' capacity to generate pocket-specific ligands, while the addition of DTPO delivers even more pronounced gains, e.g., reducing to -6.65 -682 and raising IMP to 33-41%, far surpassing all baselines.
CCIR module
Beyond interaction-based metries, we further evaluate the chemical and structural properties of the generated candidates in Tab. 2 and respectively. Incorporating DTPO consistently improves QED (.55) and SA (0.69 0.72), while maintaining high LPSK scores, indicating candidates with better synthetic feasibility and rule-based druggability. Moreover, DTPO leads to closer match to the reference set in functional group distribution, suggesting that it not only optimizes general chemical features but also implicitly captures pocket-specific structural motifs essential for ligand binding, thereby enhancing the plausibility and pharmacological relevance of the generated molecules.
3.3 GENERALIZATION EXPLORATION
To assess the potential of our method to generalize across a broader range of biomolecular domains, we further conduct exploratory experiments in this section on two representative tasks: zero-shot small-molecule editing and large-molecule protein optimization, aiming to provide initial empirical evidence of its broader applicability beyond the specific settings in which it is originally developed.
3.3.1 MOLECULE EDITING
Table 4: Results of protein editing analysis (GFP optimiza- tion). Noted that higher diversity and novelty are not equiva- lent to better performance, but provide insight into the explo- ration and exploitation trade-offs of different methods (Kir jner al. 2023
Experimental setup. We first ex- amine the zero-shot performance of DRUGTRAIL on small-molecee edit-
ing. Following (Liu et al., 2024b).
we use 200 molecules sampled from the ZINC dataset (Irwin et al.) 2012) for evaluation. Note that for this task, we directly use the same model trained CrossDocked2020 with- out additional post-training. See Appendix C for more details.
Medium Difficulty
Hard Difficulty
Method
Fitness f Diversity
Novelty
Fitness 1 Diversity
Novelty
Qwen3-1.7B
Base
0.07
112
201
0.05
94
156
DRUCTRAI.
0.53
22.3
26.1
0.54
18.7
27.8
Qwen3-4B
Base
0.08
113
129
0.07
131
146
DRECTRAI.
0.60
19.4
29.7
0.57
20.5
18.9
Results. As shown in Tab. T across all configurations, the performance
Qwen3-8B
Base
0.08
142
187
0.1
125
193
DRUGTRAIL
0.62
13.1
7.9
0.57
17.6
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0.90
000
VS141 QU0111
QED
QED(1)
6.70
0.20
R11 80.1
VS(
R.1.122, 8Q.1
-64
0.86
R21,222 L:2, 8Q/2
R.1 R02
R22,1-3 L:3, R821
VSL Diverkat
1060
SA(T)
R/2 1:1, R02
LPSK(T)
236
209
RQ
vS
Attenuation Factor
DTPO+CCIR DTPO
(a)
(b)
(c)
(d)
Figure 2: Visualization of ablation studies. a. Weights of different components reward function.
The setting adopted in DRUGTRAIL is highlighted in red. VS denotes the Vina Score (dock), R, L, RQ denote the area in the figure represents the ideal region. b. Value of attenuation factor y in the Ligand-Based Reward. c. Components of reward function. d. Necessity of the CCIR stage.
Ligand-Bar -Bsedd, and Reasoning Quality Reward, respectively. The top-right
Please design coule binding
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wond riingg armer engce consened pobr
acluls. -To connnnn--noo Cveral byen cae sembir moder mod bully hedrsshable mistiuurt termit tegi foad fwo andyyst resilti
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Score 12.6
Figure 3: Visualization of candidate molecule generated by DRUGTRAIL along with its correspond- ing reasoning process. Our reasoning process aligns well with the structure of the generated candidate molecule and enables successful, high-quality docking with the corresponding binding pocket. Text segments and molecular substructures highlighted in the same color represent matched pairs.
gap between DRUGTRAIL and the Base method remains significant, with gains exceeding 30 per- centage points in most scenarios. It suggests that the interpretable design, bio-active chemical space construction, and rule-based constraints have guided the model to learn editing mechanisms that effectively capture transferable structure- funnctio--unctionn relationships, enabling robust zero-shot adaptation molecule editing tasks.
3.3.2 PROTEIN EDITING
Experimental setup. In addition, evaluate the generalizability of this interpretable training paradigm macromolecule protein optimiza- tion tasks. Following Kirjner et al. (2023). we perform evaluation on the Green Fluorescent Protein (GFP), with details provided in Ap-
Table 5: Results of molecule editing analysis with perfor- mance assessed by the hit ratio the property change (t).
Solubility
Drug-llleness
Permseailliiy
More Hydrogen
Mure
Less
More
Less
Mure
Less
Accepturs
Donoes
Qwen3-1.78
Base
36.33
40.33
32.17
35.17
29.50
21.67
10.87 39,50
13:83
DRUGTRRAL
75.67
09.83
50.17
66.83
51.67
61.83
41.00
Qwen3-IB
pendix C.3.2
40.50
33.67
14.17
70.17
53.50
Results. From Tab. 4 we can see that across all model scales, DRUG-
Qwen3-8B
Base
38.33
42.83
34,50
33.33
31.50
23.17
10.50
15.67
TRAIL achieves markedly higher fit- ness scores than the Base method, with improvements from below 0.1 to above 0.5 in both difficulty levels. While DRUGTRAIL'S diversity and novelty scores are lower than those of the Base method, the substantially higher fitness values reflect a stronger exploitation of beneficial sequence modifications, rather than broad but low-quality cxploration. It suggests that the model leverages the structured thinking mode to focus the search within regions of the sequence space that are more likely to yield functional improvements, enabling precise optimization orientation.
DRUCTRAAL
76.50
72.50
52.00
71.17
$4.67
66.83
44.33
41.83
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Published as a conference paper at ICLR 2026
4 ANALYSIS
4.1 ABLATION STUDIES
Weights of Different Components of Reward Function. Determining appropriate weight assign- ments for each component of the reward function is critical to the reinforcement learning process. As shown in Fig. 2a when higher weight is assigned to the Rule-Based Reward, the QED score of the generated candidate molecules tends to increase, while their binding affinity decreases. Conversely, increasing the weight of the Ligand-Baan -Bseed Reward produces the opposite trend. It highlights the need to balance and trade off between the two performance aspects. Based on this analysis, we select a R:L:RQ=1:2:: weighting scheme that lies within the optimal region.
The Attenuation Factor 7,. In DTPO preference data construction, each protein pocket is paired with experimentally validated bioactive candidates ranked by docking scores. The model is guided to favor higher-ranked compounds through the attenuation factor y. As shown in Fig. 2b larger increases molecular diversity. While diversity remains valuable, in this setting we priontizee binding affinity and thus select y 0.95, corresponding to its extremum.
Components of Reward Function. As illustrated in Fig. 2c] we evaluate five aspects reflecting biological efficacy and chemical properties to analyze the three reward components proposed in $ |.2.3 We first notice that removing the Reasoning Quality Reward leads to a marked drop across all aspects, indicating that the structured biological reasoning mode is crucial for RL. Considering the other two rewards, the gggand-Based Reward plays a key role in enhancing interaction awareness, as its removal results in substantial declines in IMP and VS. Complementarily, the Rule-Based Reward primarily improves
with its removal causing a pronounced drop in LPSK. Together, these results reinforce the notion that effective drug discovery requires balancing binding affinity and drug-likenn lrug-likeness, neither of which can be compromised
CCIR. We further investigate the role of the Clinical Chemistry-Ifoormed Reasoning (CCIR) process in enhancing the model's 's capability for drug discovery tasks. As illustrated in Fig. 2d removing the CCIR process and directly applying DTPO resulted in significant drop in performance with respect to both binding affinity and drug- drug-likenness Once again this confirms the importance of incorporating an interpretable drug discovery prior into our method.
Geeaetry-related Reasoning Dimensions. Geometric information is essential for drug design. We apply five dimensions in constructing the reasoning trajectory, among which three implicitly embed geometric considerations. We refer to those as the Geometry-related Reasoming Dimensions (GRD): 1. Maintenance of core structural and functional integrity captures the scaffold and the orientation of groups that form hydrogen bonds, salt bridges, and π-π stacking; 2. Prior knowledge and strricall space guidance utilizes known binding patterns within the target family to guide candidate structures; 3. Conservation analysis and identification of critical sites leverages homology and pocket conservation to locate key residues and constrain binding modes. As shown in Tab. 6 removing these dimensions significantly reduces the binding quality between the generated ligands and the receptor pocket. This indicates that the geometric analysis introduced by GRD provides substantial positive information gain for drug design. GRD acts as soft constraints on pocket shape and interaction geometry, steering the model toward candidates that better satisfy the pocket's spatial constraints and known interaction motifs. Consequently, DRUGTRAIL can maintain a certain degree of geometric consistency and druggability without explicit 3D coordinate input.
Affinity Evaluation. Accurate and rigorous affinity evaluation is essential for improving and validating model performance. We use multiple affinity prediction methods to screen and rank the preference dataset composed of bioactive compounds. and we evaluate with multiple metrics. Specifically, we build three variants: (ranked by Boltz2 (Passaro et al., (025) predicted affinity), and DRUGTRAIL-SSI (ranked by PIC50 labels provided in the SIU (Huang et al. 2024) dataset from wet-lab assays). As shown in Tab. 7 the model is robust across all combinations of "screening strategy x evaluation metric". It is worth noting that in the DRUGTRAIL-SIU setting we obtain 78 Uniprot IDs by taking the intersection of our training set and the SIU dataset, in which the preference dataset is only about half the size of the other settings (some compounds are not included in SIU), yet the results remain competitive. This indicates that DRUGTRAIL does not depend on certain affinity evaluation tools but serves as a generalizable framework.
(ranked by Vina score), DRUGTRAIL- TRAIL-Boltz2

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Published as a conference paper at ICLR 2026
Table 6: Ablation study on GRD. Boltz2 Affinity refers to PIC50 (kcal/mol). The two metrics do not exhibit a strong linear relationship.
Table 7: Ablation study on different binding affinity evaluation methods.
Method
Vina Score v
Boltz2 Affinity
Method
Vina Score
Boltz2 Affinity
DRUGTRAIL-BolzZ DRUGTRAIL-SIU DRUGTRAIL-VimA
-6.1
6.75
DRUGTRAII.(Me) GRD)
-461
3.99
6.52
6.47
DRUGTRAIL
-6.79
-6.79
6,68
4.2
CASE STUDIES
We randomly sample the 3TV6 pocket from the validation set to show our interpretable drug design process, which is illustrated in Fig. [ and detailed in Appendix E The reasoning analysis of the selected pocket reveals dominant hydrophobic and aromatic residues alongside discrete polar hot spots, suggesting requirements for aromatic core to engage stacking and a polar functional group for directional bydrogen bonding. After docking the generated candidate into the pocket, we find that these analyses lead features such as the phenyl ring stacking with aromatic side chains and the carbonyl group oriented toward a polar region. This alignment between reasoning and observed binding illustrates the mechanism underlying the interaction. With such an interpretable reasoning process, we can understand "why" and "how" the model designs and this binding occurs.
5 RELATED WORK
LLMs for Drug Discovery. Large language models (LLMs) have attracted growing attention drug discovery across diverse domains and tasks. Existing approaches to adapting L.LMs for drug discovery can be categorized into three main types: training models from scratch for specific domains (Chen
ct al.
2024 Hayes et al. 2025 Zhu et al.) 2025 Nguyen et al. 2024 Liu et al. 2023 Luo et al. Taylor et al. 2022). fine-tuning general-purpon LLMs (Chaves et al. 2024 Lv et al, 2025
2022
Wu et al. 2026 2025).
) and incorporating pretrained scientific encoders using adapters (Liang et al. Luo et al. 2023 Li et al. 2025). More recently, unified frameworks like NatureLM (Xia 2025) have been pretrained on large mixed-domain corpora achieve broad coverage. Despite their promise, these models are still data-intensive and prioritize final predictions over interpretable reasoning, highlighting the need for more efficient and interpretable methods. We provide more discussions in Appendix D
2023
al.
Post-Training for LLM Reasoning. The powerful reasoning capabilities of LLMs have been significantly enhanced through post-training techniques. Supervised
(SFT) (Yagg et al. 2024. Min et al.) (2024) has played an important role by training models on high-quality, step-by-step reasoning sequences, improving their ability to generate coherent Chhiin-of-Thougee (CoT) reasoning. More recently, Reinforcement Learning (RL) (Guo al. 2025aa] Yu et al., 2025 Yue et al.) 2025 Wang et al. 2025) has been introduced to further refine reasoning processes by optimizing outcome-
based rewards, allowing models to explore their reasoning paths. In this paper, we aim to enhance the reasoning capabilities of LLMs in drug discovery.
6 CONCLUSION
In this paper, we propose DRUGTRAIL, a reasoning reinforcement learning framework that acti- vates LLMs for interpretable and drug-likeness oriented molecule design. By combining Clinical Chemistry-Informer Reasoning trajectory and Druggability-Tailon egy, DRUGTR TRAIL achieves efficient and transparent drug discovery. Experiments across multiple tasks confirm its strong generalization, offering a viable path toward AI tools that are both effective and trustworthy for pharmaceutical research.
Preference Optimization strat-
ETHICS STATEMENT
We confirm that work is fully compliant with the ICLR Code of Ethics. This research does not involve any human subjects, sensitive data, or other ethical concerns. All methodologies and applications described align with standard research integrity practices.
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Published as a conference paper at ICLR 2026
REPRODUCIBILITY STATEMENT
To facilitate reproducibility, we provide detailed document of the setup in the appendix, covering the overall experimental procedure, dataset acquisition, and specific parameters throughout the process. Furthermore, we validated the robustness our results in the paper through systematic evaluation and detailed ablation studies. The code is available at http://gghhubbcoom/eeeeedipppyip Trail.
ACKNOWLEDGEMENTS
This work was supported in part by the National Natural Science Foundation of China No. 62376277, Public Computing Cloud, Renmin University of China, and fund for building world-class universities (disciplines) of Renmin University of China.
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Yu Yue, Yufeng Yuan, Qiying Yu, Xiaochen Zuo. Ruofei Zhu, Wenyuan Xu, Jiaze Chen, Cheng-Xian Wang. Tiantian Fan, Zhengyin Du, Xiangpeng Wei, Xiangyu Yu, Gaohong Liu, Juncai Liu, Lingjun Liu, Haibin Lin, Zhiqi Lin, Bole Ma. Chi Zhang, Mofan Zhang, Wang Zhang, Hang Zhu, Ru Zhang, Xin Liu, Mingxuan Wang. Yonghui Wu, and Lin Yan. VAPO: efficient and reliable reinforcement learning for advanced reasoning tasks. CoRR, abs/2504.051 2025.
Kang Zhang, Xin Yang, Yifei Wang, Yunfang Yu, Niu Huang, Gen Li, Xiaokun Li, Joseph Wu, and Shengyong Yang. Artificial intelligence in drug development. Nature medicine, 2025. URL
https://api. www.steeeattntoooeeeeee htt.:///r..orrrrrrrpo/o/////////
Zaixi Zhang, Yaosen Min, Shuxin Zheng, and Qi Liu. Molecule generation for target protein binding with structural motifs. In The eleventh international conference on learning representations, 2023.
Xinyuan Zhu, Jiadong Lu, Xinting Hu, Tengchuan Jin, Shan Lu, and Fuli Feng. Hicrarchical progressive learning for zero-shot peptide-hie hla binding prediction and automated antigenic pep- tide design. Cell Reports, 44(6):115763. 2025. ISSN 2211-1247. doi: https://doi.org/10.. c016//.celrep.2222 $2211124725005348
URL https:/ www.ssieeneddrett.ccmm www.ecccdreett.oooossciie/rrrrrd
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A THE USAGE OF LLMS
In this paper, Large Language Models are utilized for the purpose of polishing the writing.
B DETAILS OF TRAJECTORY GENERATION FROM MULTI-OBJECTIVE QUALITY CONTROL
We follow a three-stage sequence for data filtering: Conflict Resolving, Thinking Budget Instruction, and finally Domain Consistency. In the Conflict Resolving stage, we generate ten responses for each sample and perform conflict detection across these responses. For samples that fail confict detection, we regenerate
additional responses and reapply the detection process. This procedure is repeated up to three times; samples that still fail after three iterations are discarded. Samples that pass this stage proceed to the Thinking Budget Instruction phase. In this phase, we calculate the mean i and standard deviation of token lengths across all responses for all samples. Any response whose token length exceeds 44 + 30 is removed. Finally, the remaining responses advance to the Domain Consistency stage. Samples that pass this final stage are retained for our reasoning dataset; if a sample has more than one valid response at this point, we randomly select one response to include in the dataset.
The detailed statistical information of the data before and after filtering is presented in Tab. g Initially, generate 25,000 reasoning samples from 2,500 original cases. After applying three-stage data filtering process, the token length of the data is noticeably reduced, and the standard deviation also decreases significantly. This indicates that a substantial portion of excessively long short samples removed, resulting in a more complete coverage of the structured reasoning dimensions. We also present the detailed statistics of the selected "Golden" set. These samples are endorsed by human experts as having high content accuracy and logical consistency. In drug design scenarios, the difficulty of ligand generation can vary considerably across different targets; accordingly, the length of reasoning content should adapt to the complexity of the task. Therefore, the samples we select aim encompass broad range of token lengths within reasonable limits. The actual statistical results confirm this: while the standard deviation of the "Golden" set is relatively large, the mean length remains within a reasonable range, and all reasoning dimensions are fully represented.
Table 8: Statistical information the reasoning data before and after filtering. The mean and standard deviation are calculated based the token lengths.
Number
Mean
Standard deviation
Average number of dimensions
Before
25000
2361
483
4.2
After
2000
1787
347
5.0
"Golden" set
15
1694
835
5.0
C EXPERIMENTAL DETAILS
C.1 POCKET-LIGAND INTERACTION
C.1.1 EVALUATION METRICS
We conduct evaluations the CrossDocked2020 dataset, following its established split, which yields 100 complexes for testing. Another 100 complexes are randomly selected for validation, leaving 99,900 complexes for training. In the actual SFT training phase, the dataset construct, which includes reasoning trajectories, contains 2,000 samples.
Following Lin et al. (2024). the evaluation covers four dimensions: substructure, chemical property, geometry, and interaction. However, since our model does not perform direct 3D generation, our evaluation excludes the geometric dimension. The details of each dimension listed below.
Substructure analysis investigates the similarity between generated and reference molecules in terms of atom types, ring types, and functional groups. This evaluation considers how the statistical
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distributions of these features correspond across datasets. Two complementary metrics are applied: the Jensen-Shannon divergence (ISD), which measures the distance between probabilistic distributions over molecular substructures, and the mean absolute enror (MAE), which reflects differences in average occurrence frequencies (for example, the mean number of carbon atoms per molecule). The atom types in scope include C, N, O, F, P. S, and CI, while ring types range from three eight atoms. In addition, functional groups are identified and categorized into 25 classes following the EFG procedure (Salmina et al. 2013).
Chemical property evaluation draws upon established cheminformatics metrics, including QED (quantitative estimate of drug-likeness) (Bickerton al. 2012). SA (synthetic accessibility, nor- malized to the 0 I range) (Ertl & Schuffenhauer 2009), and LogP (octanol-watee partition coeffi- cient) (Ghose et al., 1999), with optimal values for drug-like candidates typically between 0.4 and 5.6. In addition, the LPSK metric (Lippinski's 's rule-of-five compliance) [Lipinski] (2004) calculates the proportion of outputs meeting the specified drug-likened criteria. Together, these measures quantity attributes such as bioavailability, ease of synthesis, lipophilicity, and compliance with widely accepted medicinal chemistry guidelines.
Interaction assessment covers both docking performance and specific interaction patterns. For docking evaluation, three operation modes from AutoDock Vina (Trott Olson 2010) are used: Score (direct computation of binding energy for the generated conformation). Min (pose optimization while holding internal coordinates fixed), and Dock (joint optimization of pose and internal structure). For each mode, E denotes the predicted binding energy, and IMP (%) quantifies the fraction of generated molecules with better Vina scores relative to the references. MPBG measures the mean percent binding gap between generated and reference complexes, and LBE expresses the per-atom ligand binding energy contribution. Interaction pattern evaluation employs the PLIP framework (Salmina et al.) 2015) to compare the distribution of seven interaction types between generated and reference molecules. Similarity is calculated using JSD and MAE metrics for probabilistic and mean value differences, respectively. Noted that since we does not perform direct 3D generation, we evaluate our method under the docking mode.
C.1.2 BASELINES
We conduct comparative analysis between DRUGTRAIL and both reasoning-enabled and non-reasoning large language models, including DeepSeck-R1 (Guo et al. 2025b), Qwen3-235B A22B YYng et al. 2025 QwQ-32B, Qwen3-4B-Thinkinn et al. 2024a), 235B--223--A2222 1022B-non-thiteee toinniinnkng YYang et 2025). ) and Llama-3.1 and further investigate the effectiveness of our proposed DTPO on Qwen3-1.7B. Qwen3-4B and Qwen3-8B.
(Yang et al. 2025), DeepSeek-V V3 (Liu
In addition, we compare our method with several established methods in the field of drug design, including 3DSBDD (Luo et al. 2021), GraphBP (Liu et al.)), DiffSBDD (Schneuing al., (024), FLAG (Zhang et al., 2023), D3FG (Lin et al. 2023). Pocket2Mol (Peng et al. 2022). and Target- Diff (Guan et al.J (202)). As shown in Tab. 9 our performance on the Vina Dock metric, which depends on firee-dimensione
conformations, is lower than that of models specifically optimized for 3D structural information (e.g., Pocket2Mol and TargetDiff). It is important to note that, unlike these methods, DRUGTRAIT does not directly use 3D structural data as input. Instead, it relies on the amino acid sequence of the protein pocket, leveraging the biomedical knowledge embedded in general-purposs large language models to perform reasoning and molecular generation. Furthermore, is worth emphasizing that DRUGTRAI consistently outperforms all baselines on key drug- drug-likeness metries (QED, SA), indicating that the molecules it generates possess superior synthetic feasibility, drug-like propertics, and physicochemical characteristics. This highlights the advantage of our method: achieving comparable binding potential while enhancing the practical prospects of drug development and the transparency of Dacision-making
C.2 MOLECULE EDITING
C.2.1 TASK SETTINGS
In the molecule editing task, adopt zero-shot evaluation setting. Specifically, the model trained on the interaction task using the CCIR and DTPO procedures is directly applied to the molecule editing task without any additional fine-tuning. The prompt used for generation is: Y am now
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Table Comparison of DRUGTRAIL with other methods on interaction and chemical property of ligands.
Method
Vina Score
QED 1
LogP
SA t
LPSK 1
3DSBDD
-6.45
0,48
0.47
0.63
4.72
GraphBP
-4.57
0.44
3.29
0.64
4.73
DiftSBDD
-5.53
0.49
-0.15
0.34
4.89
FLAG
-3.65
0.41
0.29
0.58
4.93
D3FG
-6.78
0.49
1.56
0.66
4.84
Pocket2Mol
-7.05
0.39
2.39
0.65
4.58
TargetDiff
-7.41
0,49
1.13
0.60
4.57
DRUGTRAIL -6.82
0.57
1.71
0.72
4.81
conducting molecule editing task. The given molecule is [SMILES). Please edit it achieve [task descriptions]: Following the protocol in Liang et al. (2023), we sample 200 molecules from the ZINC database as the evaluation set.
C.3 PROTEIN OPTIMIZATION
C.3.1 EVALUATION METRICS
Following Kirjner et al. (2023), we perform an evaluation on the Green Fluorescent Protein (GFP). We employed three metrics: fitness, diversity, and novelty. is important to note that higher diversity and novelty do not necessarily indicate superior performance; rather, they offer valuable perspectives on the balance between exploration and exploitation achieved by different approaches. For example. purely random algorithm would yield the highest possible diversity and novelty (Kirjner et al. 2023).
C.3.2 TASK SETTINGS
Following Kirjner et al. 2023). conduct evaluation medium and hard difficulty tasks. For the GFP benchmark, task difficulty is determined by two factors: the mutational gap to the optimal fitness and the fitness range of the starting set of sequences. The mutational gap is defined as the minimum number of mutations required to reach the set of optimal proteins, X 99th consisting of sequences in the 99th percentile of fitness across the dataset. The fitness range criterion is based on the percentage rank of fitness values in the starting set, with lower ranges presenting greater challenges by requiring methods to identify beneficial mutations from proteins with limited initial functionality. In our study, the medium difficulty setting corresponds to starting set whose fitness lies between the 20th and 40th percentiles and has mutational gap The hard difficulty setting uses starting set with fitness below the 30th percentile and a mutational gap of 7, making it harder for baseline methods to discover proteins with improved fitness.
C.4 DTPO TRAINING CONFIGURATIONS
In the DTPO/GRPO training configurations, we use the models (Qwen3-1.7B. Qwen3-4B, and Qwen3-8Bi after CCIR/SFT our base models. For each question, we generate 8 rollouts and restrict the model's maximum response length to 8k tokens. The batch size is set to 64 with one update per training step, which corresponds to an on-policy setting. We adopt a sequence-level mean loss aggregation method and set the KL divergence coefficient 3 0.001 to maintain training stability. The learning rate is set to 10 with a warm-up 10 steps.
Furthermore, sample and check the reasoning process during DTPO training. Specifically, sample a rollout result at each step from step to step 100 of the training process, then perform manual checks and LLM validation. We find that the randomly sampled samples all meet the requirements of consistency and correct, smooth logic. Below, due to the space limits, we randomly display one sampled result.
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sampled rollout result during DTPO training
<Characterization The proteis pocket sequence consists of 60 amino acids. Analysis of physicachemical property distribation shows: polar
acids (e.g.. serine. threonine. tyrasine glutamine)
account for approximately 90% (positions: Serl3, 3.....2...22 $,19,21 19. Glall,20. Asn42, 43, Gle22, Gly25, 27.28. Thr41 43,51), Torming a high-density polar region with extensive hydrugen bonding patential: hydrophobic amine acids (e.g., isoleucine. alanine valine, leucine methionine (rypiuphan) account for approximately 40% pposiiion: lee 166.17.3 .32 Alals 1es9, Me16. 30. Val4,
Cys14,46, Tyrl
45,50. 1y31 588 ,9 54 Pro60 52.55, Mer00 Trp3s. Tyr39). forming a hydrophubic patch especially glycine-rich regions (Gly25 Gly27 28) and proline lurns, Acidic residues (glutamic acid Glull 20 and aspartic residues (histidine Hist,2,47, lysine Lya31,58, 54.59
224,33.18. 19.35.36,44
Aspl2, 29.56..77) and basic present.
arginine Arg4) suggesting a local electrostatic putential gradient. The overall pocket is amphiphilic. suituble for binding ligands with mixture of pelar and nonpolar functional residues
</ Characterieation Sttabilitt >Key
pesitioned follows: hydrophebic/ / aromatiic residue clusser ( serve may participate in hydrogen bonding or SipiB-stacking): hydrophilie// polar residue group (Serl3, . Thi5 Asp29.36.55
the sequence
Trp38, , Trypiophan residue
SipiS-SipiS interaction site: Ilisl.2
Lys3t .7.58
.23 ,26 .2,4, etc.. can aet as hydrogen bond acceptors; 8.19,1, 24,,4,, Glull
Lys31 48.47 Glull As229, 56.556557 posilive charges: Hist 58,47, Arg4), Linker residues (Gly25 26 27.27 28, Pro55 .60) may enhance backbone flexibility. . These residues collectively indicate that the ligand should be designed to include: hydrophobic seaffulds bydrogen bunding networks and charge- complementary groups for simultancons filling and electrostatic balance </ </Sbbiiliyy seGuidance> Sequence features (such Cyslb, Cyεt9, Hiss 12 clastering and acidic residues concentrated pusitions 31 31-47: Lys3I Cps66-Aum39-Cysle -ysie- suggest that this pocket may belong to the hydrolase or transferase family (Cys/Hin His pattern is commonly found is protenses such as subtilisins). Prior chemical space coastraiats: The ligand should avoid strong metal coordination (due to infrequent metul-binding residues. only Cys16 may
form bydregen bond doners/ aacoss//cepoos));; charged residue regions (negative
nucleophilic site), but can integrate electrophilic groups le.g.. aldehyde. ketons] for reversible biading; molecular size is limited to small to medium (due to the lung sequences Gly25-28 containing glycine forming a flexible region), e.g., weight 200-400 Da: functional groups need to match the blocks and hydragen bead acceptors. while excluding large steric groups to prevent clashes. </Guidance ><Conservation No multiple sequence alignment data is available. but based on sequence internal pattera conservation. key sites are identified; Asp29 carrying negative charge (possibly catalytic) conserved for anion recognation; histidine claster (His2 .47) and Lys58 the region 1" JIGSNIPEY" (pusitions 40-46, Gludl) ma form a protective functios. Ligand design should prioritize targeting these sites: anion groups (e.g., sarboxylic acid) targeting Lys residues. hydrogen bond donors te.g.. primary amine) targeting
characteristies, i.e., prefer hydrophobic
Asp/AsTyyz, while using hydrophobic medules to match the polarity transition near positions 40-47, o/-47,s/Comm Conseervattioooo 2 Pfunctinna group layout: Core is hydrophobic skeleton (e.g.. aryl ring
alkyl chain) matching the W3-139 hydrophobic
groups /onor/acceppion targeting Tyr clusters (c.g., $13, 126. 134), amino groups (donor) targeting Gla/Asp aegative charge). and sarbony (acceptor) targeting Lys/His (positive charge); charge
interaction region; periphery arranged polar groups: hydrexyl/
complementurit design: pesitive groups (e.g.. quaternary ammonium) targeting Gla20/Asp29, aegative groups (e-g.. phosphorie aeid) targeting Lyd31. Spatial arrangement should avoid functionzl group clustering (e.g.. Sipiš systems near aromatic clusters like Y40, 847), instead have directional pocket curvature. </ Optimization >ennnerr
flexibility (e.g.. rotatable bends) optimization strategy: Based os pocket's 's amphiphilic distribution aptimize the ligand balance hydrophebic core (suitable for w3s. hydrophobic patch) and polar docking points (utilizing hydroges hand aetworks driven by S/T polarization and electrostatic complementarity Strengthen non-cove covalcn points
points aear positions 31-47). while designing molecular rigidity flexibility transitions (e.g., through ratatable bonds to adapt to Pro/Gly flexible regions), ) ensuring binding affinity and specifieity. W. boxed [Oxel[COclacee
interactions (e.g.. hydrophabic
positions 16-24, and charged
( Ansswers
D DETAILED RELATED WORK
In this part, we present detailed related work on the application of Large Language Models (LLMs) to drug discovery
extention of $ Specifically, existing approaches to adapt LLMs for scientific applications can be roughly grouped into three categories. First, a typical line of work trains models from scratch for specific domainssuch sequentialized proteins (Chen et al.
2024
Hayes al.)
2023 or scientific 2025| Zhu et al. 2025). DNA (Nguyen et al., 2024), small molecules (Liu et al.
corpora (Luo et al. 2022 Taylor al. 2022). They achieve accuracy within their respective domains but require massive amounts of in-domain data and lack flexibility for cross-domain understanding. Second, several studies have explored fine-tuning ceneral-purposss LLMs on molecular (Chaves et al.. 2024) or protein data (Lv et al., 2025). However, they still requires millions of training samples, resulting in high computational and resource costs. The third line of approaches integrate pretrained scientific encoders into LLMss through lightweight adapters, which leverages multi-modal information but faces challenges in aligning heterogeneous representations (Liang et al., 2023 Luo et al., 2023 Li et al. 2025). More recently, unified frameworks such as NatureLM (Xia et al. 2025) attempt to provide broad scientific coverage by pretraining on a sufficiently large domain corpora. While such models show strong potential, they remain heavily dependent on large-scale data and primarily focus on generating final predictions rather than offering transparent intermediate reasoning. Overall, despite notable progress, current I.L methods for drug discovery are still constrained
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by costly data requirements and lack of explicit reasoning processes, underscoring the need for approaches that combine interpretability with efficiency.
E DETAILED PROMPTS AND GENERATED EXAMPLES
Prompt for getting structured reasoning dimension
[Pocket-Ligand Interaction]
goal given sequence protein pecket to design candidate drug molecules capable hinding it and producing meaningful interactions. In this design process suppose you are acting as pharmacologistt What key and broadly applicable
currently working pocket-lizand interaction
should lake into consideration? Please outline several critieal
[Protein Editing]
I am currently working on proteia optimization task. where the objective is: given the sequence of protein and target property to be impraved, design new pretein sequences that meet the desired suppose
requirements for that property... In this design you take considcration identify several
biologist: What
key broadly applicable aspects critical and general factors.
Prompt for getting structured reasoning trajectory
[Pocket-Ligand Interaction]
goal perform pocket-ligand interaction task. give you sequence pucket. and you need to generate the corresponding ligand molecule SMILES that can bind to this pocket. will alse provide the ligand molecule SMILES that can bind to it which is the correct Your task is: Based the given directly calculated,
protein
information that following pocket analytical dimensions: In-depth physicochemical profiling, Maintenance of core structural and functional integrity Prior knowledge and chemical structural space guidance. Conservation analysis and identification of critical
the
given pecket Optimization and multi-attribste balancing, deduce ligand is the ligand
bind the
SMILES provided.
Daring the reasoning process: Du not use directly or indirectly. any information from the ligand molecule SMILES provided. Strictly follew guess any experimental
rule.
geometric/páysice quantities.
The amino acid position numbers in the given protein pocket sequence start from -strictly follow this rule.
Input:
Protein pocket sequenee: [protein]
Ligand moleculc SMILES; [ligand]
Output requirements:
Output five
sections
1-5. directly indireetly any informatios
ligand molecule SMILES provided.
At the end, outpur a general everall uptimization idea and then give the answer.
[Protein Editing]
The goal is perform proteis optimization lask. will give the protein sequence be optimized. types
properties the prateis by editing optimize the provide optimized protein sequeace (with stroager green fluorescence than the target sequence). which is the correct answer, Your task is: Based on the given protein
sequence.
and the sequence information that following fixed analytical dimensions:
directly caleulared. use the
In-depth physiccceemical profiling. Maintesance core strnetural functional integrity. Prior knowledge and chemical structural space guidance, Conservation anclysis and identification critical sites Optimizatoon and multi- balancing. derive optimized based
optimal proteis sequence
goal anhancing flaorescence properties
pravided.
Daring the reasoning process:
Do not directly or indirectly, any information from the aptimized pratein sequence 1 provided. Strietly fallew this rule.
not use or guess any experimental data or grometric/ physical quantities.
The amino seid position numbers in the gives protein packet sequence start from -atrietly follow this rule.
Input:
optimized: [pratein) Optimized protein sequence: (targel protein]
Pratein sequence
Output requirements:
Ourput five reasoning
directly indirectly any information
from the optimized protein sequence provided.
At the end, output general overall optimization idea and then give the answer.
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Please design ligand molecule capable of Published as a conference paper at ICLR 2026
example of explainable drug discovery process
[Question]
am currently conducting a pocket interaction task. The given pocket is I'A'
HwW.://wwww.O...OS/SS/SAAAAAAAA binding it.
[Answer]
<Characterization The pocket sequence contains high proportion uniform distribution of hydrophohic residues (I. I, V. F, M. A, G) suggesting the presence of sizable hydrophobic cavity. located at multiple pusitions and form SipiS-Sipis stacking with rings hydrophobic fragmenis in ligand. designing the ligand. incorporating stable hydrophobic aromatic core is recommended to provide both stacking interactions and spatial seaffold for orientation within the pocket. Chharacierizzaio ><Stability >uultiple polar residues (S. N. C) and charged residues (K. D. E, H) embedded in hydrophabic background indicate discrete polar
Arumatic residues (F. Y.
spots." ligand (e.g., carbunyl. amide bydrexyl. . or heteroaromatic nitrogen) at one end or a key position on the aromatic scaffold to aet as hydregen-hond hond donor/acespion acceptor. thus enabling directional H-hond contacts with those functional residues. S/Sbbillity GeGidannce Motif features nucleotide -binding related substrates. For such sites placing stable aromatic or heteroaromatic
these iniroducing
polar functional group
similarity heterosromatic
certain
which typically accommodate planar
care mimicking scaffold at the center of the ligand is beneficial accupying the binding plane while offering edge positions for substicuenis reach peripheral hydrophubic pular regions. Incorporating can forther eagage conserved polar residues through hydrogen conserved regions may include acidic residues (D/E) and
beteroatoms Conserrrtoorrr bunding. < slycine- -rich flexible motifs -uften critical for anchoring small polar moieties. To address these I--bon
acceptor groups ligand seaffold could be designed enabling compatibility with conserved aromatic nitrogens, amide carbonyls residue interaction patterns and improving binding affinity. </Comeervation pcptiiiatttiiiattioino 5Overall ligand layout could combine: A strong Bydrophobic/aroma aromatii core for stacking ia the cavity One or strategically positioned polar anchurs engage hydrogen-boni sites charged residues. A terminus
specific positions
(e.g., fluoroalkyl. cyclaslkyl substituted aryl). to fit into small bydrophebic subpeckets </Optimization Baed on the five -dimensional analysis. I have designed mulecule, results in hoxee//CCii00EE(004))0000OCAAAA AICE (ECCOCC)) Aaswer>
moderately bulky hydrophabic substituent
21


Reviews:
[{"review_id": "i0dUq8IOYt", "reviewer": "Reviewer_aDhV", "summary": "Summary:\nThe paper introduces DRUGTRIAL, an LLM framework for explainable drug discovery. It try to addresses the “black-box” nature of AI methods by introducing structured reasoning traces to explain how and why behind its conlcusion. The work also uses DTPO strategy to optimize not only for binding affinity but also balance multiple essential factors.", "questions": "See Weaknesses", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "Pros:\n- The paper introduces DRUGTRIAL, an LLM framework for explainable drug discovery.\n- Extensive experiments demonstrate the effectiveness of our approach and its generalizability to a wider range of biomolecular optimization domains.", "weaknesses": "Cons:\n- 3D structural analysis doesn’t seem to taken into account, which is the core for small molecule drug design from medicinal chemist viewpoint.\n- Explainability is a big claim; this method is clearly not explainable but only can provide interpretable insights. Phrasing and statements regarding this are encouraged to modified.\n- Vina is an old approach that is not considered very accurate. More advanced binidng affinity prediction or docking methods like Boltz2, and PSICHIC, can be considered.\n* hyperparameters for multiple reward functions may be difficult to tune.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "N4lI3qJX1n", "reviewer": "Reviewer_Pp95", "summary": "This paper presents DRUGTRAIL, an LLM framework for explainable drug discovery. It first uses Clinical Chemistry-Informed Reasoning (CCIR) to fine-tune a model on structured reasoning, then applies Druggability-Tailored Preference Optimization (DTPO) to optimize for multi-component rewards (ligand similarity, drug-likeness, and reasoning format) beyond simple binding affinity.", "questions": "1.There is an inconsistency in the description of the reasoning dimensions. Section 2.1.1 explicitly lists \"five core reasoning dimensions\". However, Section 2.1.3 states the SFT data conforms to \"six predefined reasoning dimensions\", apparently counting the final <Answer> block as the sixth. This is confusing.\n\n2.The SFT dataset generation (2.1.2) relies on several LLM-based filtering steps, such as Conflict Resolving and Domain Consistency which introduce unquantified biases. The \"Domain Consistency\" check, in particular, relies on a \"small set of 'golden' reasoning trajectories\" whose size and diversity are not specified.\n\n3.By pre-filtering the reference dataset with AutoDock Vina and then rewarding Tanimoto similarity, the model is effectively trained to optimize the Vina score, which is also the primary evaluation metric (Table 1). This risks overfitting to the Vina function.\n\n4.The \"Reasoning Quality Reward\" merely checks for the syntactic presence of formatting tags, not the semantic quality or logical accuracy of the reasoning content. Sometimes the model is not penalized for generating nonsensical reasoning as long as the format is correct.\n\n5.The paper claims its 1D SMILES generation method \"excludes the geometric dimension\". However, pocket-ligand binding is inherently a 3D problem. Furthermore, the model's inference (e.g.,π-π stacking) and key evaluation (Vina docking score) heavily rely on 3D geometry. It’s better that explain how the model learns this implicit 3D perception solely from 1D sequences.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "The work commendably tackles the critical challenges of interpretability and multi-objective optimization in drug discovery. Its core strength is the DTPO reward function, which explicitly moves beyond optimizing affinity scores by balancing structural similarity, rule-based drug-likeness, and reasoning coherence, addressing a key limitation of prior methods.", "weaknesses": "The \"Reasoning Quality Reward\" is purely syntactic, rewarding formatting tags rather than semantic accuracy, which undermines the interpretability claim. Furthermore, the evaluation risks circularity, as the model is rewarded for similarity to a Vina-filtered dataset and then primarily evaluated with Vina. Finally, it fails to resolve the 1D/3D contradiction: the model uses 1D sequences but makes 3D-dependent inferences (e.g., π-π stacking) and relies on 3D docking for evaluation.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "O8NhtNRhTr", "reviewer": "Reviewer_ru54", "summary": "DRUGTRAIL is an LLM-based framework for interpretable drug discovery that combines structured reasoning and reinforcement learning. It applies a Druggability-Tailored Preference Optimization (DTPO) scheme to balance binding affinity and drug-likeness. Trained mainly on CrossDocked2020, it outperforms baseline LLMs in docking and property metrics.", "questions": "see weakness", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. By enforcing explicit reasoning trajectories, DRUGTRAIL makes the model’s decisions transparent and closer to human medicinal-chemistry logic.\n2. Demonstrates consistent improvements across interaction, chemical, and structural metrics, and shows transferability to both small- and large-molecule tasks.\n3. The creation of a reasoning dataset with conflict resolution, domain consistency checks, and a thinking budget is novel and carefully designed.", "weaknesses": "1. Do the authors provide a rationale for designing the reasoning format with tags such as Characterization, Stability, etc? It is unclear why these particular dimensions were chosen.\n2. Only general LLMs (eg. Qwen) are used for comparison. There is no evaluation against established drug-design systems.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "zybhpjPQfg", "reviewer": "Reviewer_6Cmn", "summary": "The authors present DrugTrail, an LLM-based method for computational drug discovery. DrugTrail is a novel approach to an ongoing challenge in drug discovery: the de novo generation of drug-like molecules. Through a RL-based process to optimize several metrics (beyond binding affinity), DrugTrail can effectively generate small molecules/ ligands for binding proteins. This is an interesting, interpretable approach which has several steps to incorporate and align with biochemical domain knowledge.\n\nOverall, this is a nice paper, and I will recommend an acceptance. However, the authors should provide better explanations of some of the biochemical jargon (see below).", "questions": "- Please go through all the acronyms and ensure they are defined before being used.\n- In your paper, better define biochemical jargon. A few examples are included below, but this is not an exhaustive list.\n    - \"ligand\" - this is also used interchangeably with \"molecule\" or \"small molecule\" without an explanation that they are synonymous\n    - \"druggable\"\n    - \"binding pocket\"\n    - \"canonical SMILES\"\n    - \"backbone\"\n    - \"docking\"", "limitations": "", "rating": 8, "confidence": 3, "soundness": 4, "presentation": 4, "contribution": 4, "strengths": "**Strong points:**\n\n- Unique from other approaches\n- Great use of relevant scientific and medicinal information for model context.\n- The approach for tokenization is very sensible for the application.\n- The authors clearly thought-out every step of the pipeline. I appreciate that every step is justified appropriately.\n- Great figures.\n- The claims follow the results.\n- The background information motivates the need for DrugTrail well.", "weaknesses": "**Weak points:**\n- My main criticism is that some of the biochemical jargon needs to be better or earlier explained. I know this is difficult with limited space, but it is important for understanding the paper, especially for a computational conference. \n- The explanation of biochemically relevant acronyms, such as LPSK and QED, are in the supplementary, but this makes it difficult to understand what these are when they come up earlier in the main text and corresponding figures.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)