Computational Drug Discovery Methods

Derniers papiers

• 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 septembre 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 septembre 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 septembre 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 septembre 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 septembre 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 septembre 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 septembre 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 septembre 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 septembre 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 septembre 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 septembre 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 septembre 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 septembre 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 septembre 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 septembre 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 septembre 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 septembre 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 septembre 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 septembre 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 septembre 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…

• Procedural Pretraining for Molecular Property Prediction

  Moritz Friedemann, Zachary Shinnick, Philip Torr, Bruno Andreis · 17 septembre 2026

  Molecular property prediction is often limited by the small size of labeled downstream datasets, motivating pretraining on large corpora of unlabeled molecules. In this work, we ask whether useful inductive biases can instead be learned from abstract, procedurally generated data before a model sees …

• Multi-View Molecular Representation Learning with Hierarchical Graphs and Contextualized Fingerprints

  Gwang-Hyeon Yun, Jong-Hoon Park, Bing Hu, Helen Chen, Anita Layton, Young-Rae Cho · 16 septembre 2026

  Molecular property prediction requires representations that generalize from limited labeled data to structurally novel compounds. Existing molecular pretraining methods often rely on a single view: graph-based approaches model atom-bond topology but provide limited fragment-level supervision, wherea…

• Chemical and geometric representation fidelity improves drug--target affinity prediction

  Yixiao Li, Yining Qian, Yefan Chen, Zenghui Chen, Jiayue Sun, Yuhai Zhao, Cheng Tan, An-Yang Lu · 15 septembre 2026

  Predicting drug--target binding affinity (DTA) requires models to distinguish subtle chemical and structural determinants underlying molecular recognition. Although recent approaches increasingly incorporate richer drug and protein information, such information may be compressed, homogenized or disc…

• Fraglingo: Molecular Design via Attachment-Aware Autoregressive Fragment Generation

  Thao Nguyen, Jeonghwan Kim, Zhenhailong Wang, Heng Ji · 15 septembre 2026

  Molecular design is most effective when generation mirrors the edits chemists actually make: extending a scaffold, replacing a substituent, or decorating a scaffold at a specified attachment site while optimizing molecular properties. Fragment-based molecular design naturally supports this workflow,…

• Predicting Collision Cross Sections with GRACE: Geometric Residual Adduct Conditioning via Early-fusion

  Parthasarathy Suryanarayanan, Susanta Das, Shreyans Sethi, Kenneth M. Merz, Jr., Joseph A. Morrone · 14 septembre 2026

  Collision cross section (CCS), derived from ion mobility mass spectrometry, is a common descriptor for molecular annotation. Prediction is challenging for machine learning models because it reflects the size, shape, and ionization state of a gas-phase molecular ion. Most predictors either ignore exp…