Computational Drug Discovery Methods

Latest papers

• Beyond Random Splits: Evaluating Drug-Target Affinity Models Under Chemically and Biologically Motivated Distribution Shifts Copy

  Minjae Chung, Clara Li, Malar Paavai Muthukumaran, Shaunna Wang, Aniket Ramkrishnan Iyer, Shaun Qien Yeau Tan, Harinishree Sathu, Micky C. Nnamdi, J. Ben Tamo, Benoit Louis Marteau, May Dongmei Wang · 5 October 2026

  Drug-target affinity (DTA) prediction is widely used to prioritize candidate compounds before costly experimental screening. DTA models are often compared under a single data split, even though deployment may require extrapolation to new chemical series, new protein targets, or both. We ask whether …

• DISSOLVR: An Interpretable and Fast Framework for Aqueous and Organic Solubility Prediction

  Vansh Ramani, Har Ashish Arora, Dhairya Kuchhal, Sayan Ranu, Tarak Karmakar · 5 October 2026

  High-fidelity solubility prediction is fundamental to pharmaceutical development and environmental partitioning, where accurate modeling must couple molecular structure with thermodynamic behavior across diverse chemical environments. However, recent advancements have been dominated by deep learning…

• Nearest-neighbour baselines for fingerprint prediction from MS/MS spectra under different assumptions

  Ling Min Serena Khoo · 5 October 2026

  It has recently been shown that nearest-neighbour retrieval provides a strong baseline for molecular fingerprint prediction from MS/MS spectra, with several variants matching or outperforming current deep learning models (Khoo and Barzilay, 2026; Liu et al., 2026; Gupta et al., 2026). Importantly, "…

• Higher-Order Molecular Grammars for Generative and Foundation Models in Chemistry

  Yiming Huang, Yujie Zeng, Vijay Prakash Dwivedi, Simone Foti, Jianmin Wang, Jure Leskovec, Tolga Birdal · 2 October 2026

  Molecular learning models are strongly shaped by their underlying representations. Yet standard sequential and graph formalisms struggle to explicitly encode higher-order topology, such as ring systems and recurring motifs. Existing higher-order representations can capture these structures directly,…

• R-GroundBench: A Diagnostic Benchmark for R-Group Groundingin Markush Molecular Editing

  Xin Wang, Zichuan Ying, Xinna Lin, Junqi Zhang, Hanyi Xiong, Tianyu Gao, Hairong Zhang, Qixiang Hua, Botian Shi, Zhenhailong Wang, Kaicheng Yu · 2 October 2026

  Recent advances in AI for scientific discovery enable molecular understandingand design, yet reasoning over incomplete chemical representations remainsunclear.Markush structures, which encode molecular families through variable R-groupplaceholders (\textit{R\textsubscript{1}}, \textit{R\textsubscrip…

• CalibHyper: Chance-Corrected Relational Hypergraphs for Few-Shot Molecular Property Prediction

  Linyu Li, Zhi Jin, Yuanpeng He, Dongming Jin, Huanyu Liu, Huanyao Zhang, Haoran Duan, Heng Tian, Gadeng Luosang, Nyima Tashi · 30 September 2026

  Molecular property prediction is central to drug development and materials discovery, but experiments are costly and labeled data are scarce. Context-aware methods use auxiliary assay labels to support few-shot prediction, and recent work supervises property relations with label agreement. However, …

• AssayRouter: Historical Utility Priors for Frozen Molecular Predictor Routing

  Dong Xu, Zhangfan Yang, Jiantao Wu, Shipeng Zhang, Zexuan Zhu, Jiangqiang Li, Jun Zhang, Junkai Ji · 30 September 2026

  Laboratories often face a new molecular assay with 16-64 labels and a bank of predictors whose training data and parameters are unavailable. The practical question is which frozen outputs to include in a small local model. AssayRouter treats completed assays as pseudo-targets and labels each candida…

• One Sequence, Many Decodings: CAGenMol-2 Recasts Drug Design as Masked Molecular Inference

  Yanting Li, Enyan Dai, Lei Wang, Wen-Cai Ye, Li Liu · 29 September 2026

  Drug design couples property evaluation, conditional generation, structure-based design, and local optimization, yet machine learning systems typically address these capabilities with separate task-specific models. We introduce CAGenMol-2, a masked diffusion molecular language model that represents …

• ViCoR: Reliable Molecular Structure Extraction via Spatially Aligned Verification and Executable Revision

  Yujian Yuan, Xin Cai, Yufan Chen, Jiaxin Xu, Mengdi Liu, Zhichao Tan, Long Chen, Hanyu Gao · 29 September 2026

  Reliable optical chemical structure recognition (OCSR) is essential for building high-quality chemical data from scientific literature, yet even small recognition errors can propagate into chemical databases and downstream models. In practice, recognized structures often require manual inspection an…

• BERT4DTI : BERT-based Model for Predicting Drug-Protein Interactions

  Thanina Hamitouch, Khadidja Henni, Abdelkrim Arie, Amina Selma Haichour, Neila Mezghani, Lina Abou-Abbas · 29 September 2026

  Understanding how drugs interact with protein targets is fundamental to drug discovery, drug repurposing and the early identification of promising therapeutic candidates before costly experimental testing. Sequence-based DTI models face three practical limitations: labelled interactions are scarce a…

• RIDE: Reference-Anchored Inference-Time Diffusion Editing for Scaffold Hopping

  Ruoxi Gao, Frazier N. Baker, Trieu Nguyen, Xia Ning · 29 September 2026

  Scaffold hopping is a critical task in drug discovery, which seeks to discover new, structurally distinct molecules that share key functional groups and similar 3D shape with a reference binding ligand. Existing diffusion-based scaffold hopping methods formulate the problem as conditional generation…

• MolLedger: An Additive Graph Neural Network with Chemically Grounded ADME Attributions

  Christina X. Ji · 28 September 2026

  Optimizing absorption, distribution, metabolism, and excretion (ADME) is an important part of small molecule drug discovery. Many machine learning models have been built to predict ADME properties to facilitate this optimization process, but explaining model predictions is challenging. We propose a …

• Budgeted Quotient-Residual Guidance for Frozen Pocket-Conditioned Molecular Diffusion

  Xinyu Wang, Jinbo Bi, Minghu Song · 28 September 2026

  Pocket-conditioned molecular diffusion updates ambient atom coordinates, but many lead-optimization objectives are expressed on quotient features such as distances, contacts, and anchored substructures. We introduce budgeted quotient-residual guidance (QRG), an inference-time correction that makes t…

• Interpretable-by-Design Descriptor Portfolios Match a 2048-Dimensional Foundation Embedding on Low-Data Molecular Assays

  Yiqi Yao, Miquel Duran-Frigola · 28 September 2026

  In low-data structure-activity prediction, the choice of molecular representation can matter more than the choice of predictor, and tabular foundation models sharpen that effect. We ask whether a portfolio of compact, semantically named descriptor blocks can reach the accuracy of a 2048-dimensional …

• Feature Space Selection and Heterogeneous Effect Estimation for Blood-Brain Barrier Permeability: A Random Forest to the Generalized Random Forest Pipeline

  Tshemollo Rapolai, Seite Makgai, Mohammad Arashi · 25 September 2026

  Predicting blood-brain barrier (BBB) permeability is critical for central nervous system drug discovery. Using the MoleculeNet BBBP dataset (n = 2039), this study systematically ablates molecular feature spaces to isolate featurisation from model architecture. We evaluate three feature families (Mor…

• OPDiv: Optimal Selection of Top-K High-Scoring, Diverse Compounds

  Miroslav L\v{z}i\v{c}a\v{r} (Deep MedChem) · 25 September 2026

  A virtual screening campaign may produce thousands of promising candidates, but only a small number can be purchased, synthesized, or tested. The practical question is how to select a set of compounds that both rank well and are diverse enough: this poses a genuine tradeoff, where selecting the high…

• Multimodal AI predicts clinical outcomes of drug combinations from preclinical data

  Yepeng Huang, Xiaorui Su, Varun Ullanat, Intae Moon, Ivy Liang, Lindsay Clegg, Damilola Olabode, Ruthie Johnson, Nicholas Ho, Megan Gibbs, Alexander Gusev, Bino John, Marinka Zitnik · 25 September 2026

  Predicting clinical outcomes from preclinical data is essential for selecting safe and effective drug combinations and for reducing late-stage failures. AI models use molecular structure and target annotations, and do not leverage the perturbation readouts that report how a compound acts in a cellul…

• TopU-LBVS: A Realistic Multi Target Benchmark for Ligand Based Virtual Screening

  Surbhi Kumar, Yuhe Zhou, Varun Shiralkar, Niu Huang, Baris Coskunuzer · 25 September 2026

  Ligand-based virtual screening (LBVS) is a practical first-pass tool in early-stage drug discovery, but existing benchmarks can overestimate performance through random negatives, easy decoys, limited target coverage, and non-standardized evaluation protocols. We introduce TopU-LBVS, a multi-target b…

• SMILESGNN: Interpretable Clinical Toxicity Prediction via SMILES-Graph Cross-Attention Fusion

  Quang Minh Nguyen, Thuy Quynh Nguyen, Duc Minh Le, Ho Nhat Minh Nguyen, Thanh Long Dai Doan, Trong Nghia Nguyen · 25 September 2026

  Drug toxicity prediction is critical for reducing late-stage attrition in drug discovery, yet remains challenging due to severe class imbalance, scaffold-based generalization, and the clinical need for interpretable predictions. Single-modality approaches-SMILES Transformers or graph neural networks…

• MolSC: Leveraging Substituent Contributions to Enhance Fine-grained Molecular Understanding in LLMs

  Hyuntae Park, Sooyeon Kim, Jiwon Park, SangKeun Lee · 22 September 2026

  Recent advances in natural language processing have led to molecular Large Language Models (LLMs) with strong performance across diverse chemistry tasks. However, they still struggle to capture fine-grained structure-property relationships, particularly how small, localized modifications alter a mol…

• CurvFlow-DTA: dual-graph discrete Ricci curvature flow for drug--target affinity prediction

  Jicheng Ma, Yunyan Yang, Juan Zhao, Liang Zhao · 22 September 2026

  Graph neural networks are widely used for drug--target affinity (DTA) prediction, and discrete Ricci curvature has recently been used to characterize molecular graph geometry. Existing curvature-aware DTA approaches mainly use static curvature on the drug graph while representing proteins primarily …

• DPTM-DT: Dual-Pretrained Transformer Multitask Representation Learning for Drug-Target Prediction

  Ge Kong · 22 September 2026

  Drug-target relation prediction supports candidate screening, drug repositioning, and mechanism analysis. Existing models often use incomplete drug or protein representations, model cross-modal interactions shallowly, or train affinity regression and interaction classification separately, although t…

• Role-Aware Morgan Fingerprints for Reaction Yield Prediction

  Chinmay Mirji, Prashant Shekhar, Foram Madiyar, Hao Peng · 22 September 2026

  Predicting reaction yield from molecular structure and reaction context can cut experimental trial-and-error and speed up condition screening in synthetic chemistry. Recent methods for this task use learned representations such as graph neural networks or Transformer encoders over reaction SMILES (S…

• EnSol: an environment-aware graph neural network for molecular solubility prediction

  Thao Nguyen, Saman Shafaei, Zhengyi Zhang, Huimin Zhao, Heng Ji · 21 September 2026

  Molecular solubility directly affects key aspects of molecular development such as reaction feasibility, formulation performance, separation efficiency, and solvent selection. However, experimental measurement across solutes, solvents, and temperatures remains costly and sparsely sampled. Existing c…

• SpecOpt: Contact-Diff Reasoning for Agentic Molecule Optimization Toward Binding Specificity

  Thao Nguyen, Heng Ji · 21 September 2026

  Off-target protein binding is a major source of adverse effects for small-molecule drugs, yet most structure-based molecular design methods focus on generating selective compounds de novo rather than improving the selectivity of existing, well- characterized drugs. We introduce specificity optimizat…