Virtual Screening

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3 papers in the last four weeks, against 2 the four weeks before. 0.0% of all new papers.

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Latest papers 12

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TopU-LBVS: A Realistic Multi Target Benchmark for Ligand Based Virtual Screening
Sep 24, 2026Surbhi Kumar, Yuhe Zhou, Varun Shiralkar +2Benchmark Design Virtual Screening
UT Dallas, Mathematical Sciences Richardson, TX, USA
OPDiv: Optimal Selection of Top-K High-Scoring, Diverse Compounds
Sep 23, 2026Miroslav LžičařData Selection Virtual Screening
Deep MedChem
Adapting Boltz-2 with limited experimental activity data improves early enrichment in virtual screening
Sep 21, 2026Kairi Furui, Masahito OhueProtein-Ligand Binding Affinity Prediction Virtual Screening
Department of Computer Science, School of Computing, Institute of Science Tokyo, Yokohama 226-8501, Japan
Autonomous discovery of new structure-plausibility laws for explainable and rapid crystal diagnosis and screening
Sep 1, 2026Zhilong Song, Lixue ChengAI Agents for Scientific Discovery Crystal Structure Prediction
Department of Chemistry, Hong Kong University of Science and Technology, Kowloon, Hong Kong 999077, China. · IAS Center for AI for Scientific Discoveries, Hong Kong University of Science and Technology, Kowloon, Hong Kong 999077, China. · Department of Chemical and Biological Engineering, Hong Kong University of Science and Technology, Kowloon, Hong Kong 999077, China.
Deploying DeepSeek 175B Locally on a Single Consumer-Grade RTX 4060 Laptop with 32GB RAM for 200k-Scale Protein-Ligand Virtual Screening
Aug 31, 2026Rui Xiao, Yili XuProtein-Ligand Binding Affinity Prediction Virtual Screening
Hangzhou Tsingxin quantum Co., Ltd.
A Quiet Failure in Calibrated Virtual Screening: Marginal Conformal Prediction Under-Covers the Minority Class, and a Class-Conditional Fix Recovers It
Jul 7, 2026Muhammadjon Tursunbadalov, Mustafojon TursunbadalovClass-Imbalanced Learning Conformal Prediction
School of Science and Technology, Champions College Prep, United States
Target-Aware Bandit Allocation for Scalable Surrogate Optimization in Chemical Space
Jun 25, 2026Mohammad Haddadnia, Yuvan Chali, Abhilash Jayaraj +4Multi-Armed Bandits Molecular Optimization
Dana-Farber Cancer Institute, Boston, MA, USA · Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, MA, USA · Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA +4
Computational references are not experiments: pre-registered validation of machine-learned sodium-cathode voltages
Jun 19, 2026Krishna Teja VepaMaterials Property Prediction Virtual Screening
Independent Researcher†
Benchmarking Single-Pose Docking, Consensus Rescoring, and Supervised ML on the LIT-PCBA Library: A Critical Evaluation of DiffDock, AutoDock-GPU, GNINA, and DiffDock-NMDN
May 3, 2026Youssef Abo-Dahab, Xiaoiang Xiang, Joanne Chun +1Learning to Rank Virtual Screening
Department of Bioengineering and Therapeutic Sciences, Schools of Pharmacy and Medicine, University of California, San Francisco, San Francisco, CA, USA · UCSF–Stanford Center of Excellence in Regulatory Science and Innovation (CERSI), San Francisco, CA, USA
Structure-guided molecular design with contrastive 3D protein-ligand learning
Apr 21, 2026Carles Navarro, Philipp Tholke, Gianni de FabritiisContrastive Learning Virtual Screening
Acellera Labs, C/ Doctor Trueta 183, 08005 Barcelona, Spain · Computational Science Laboratory, Universitat Pompeu Fabra, PRBB, C/ Doctor Aiguader 88, 08003 Barcelona, Spain · Instituci´o Catalana de Recerca i Estudis Avan¸cats (ICREA), Passeig Llu´ıs Companys 23, 08010 Barcelona, Spain
APEX: Approximate-but-exhaustive search for ultra-large combinatorial synthesis libraries
Oct 28, 2025Aryan Pedawi, Jordi Silvestre-Ryan, Bradley Worley +5Surrogate-Assisted Optimization AI-Assisted Scientific Research
Work performed at Numerion Labs · NVIDIA
Why Pool When You Can Flow? Active Learning with GFlowNets
Aug 31, 2025Renfei Zhang, Mohit Pandey, Artem Cherkasov +1Active Learning Virtual Screening
School of Computer Science, Simon Fraser University, Burnaby, BC, Canada · Vancouver Prostate Centre, University of British Columbia, Vancouver, BC, Canada · Diagen AI +1

Sep 24, 2026, cs.LGJ/K move · Enter open · S save

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

Surbhi Kumar, Yuhe Zhou, Varun Shiralkar, Niu Huang+1

UT Dallas, Mathematical Sciences Richardson, TX, USA

Benchmark Design Virtual Screening Benchmark Validity Molecular Representation Learning Drug Discovery

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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 benchmark for LBVS under hard-negative screening conditions. Starting from curated ChEMBL~35 bioactivity data, TopU-LBVS covers 93 protein targets across 7 protein classes and constructs target-specific screening libraries with property-matched, structurally similar decoys at a fixed 1:40 active-to-decoy ratio. Libraries contain roughly 400 to 10,000 compounds and are designed to reduce simple physicochemical and nearest-neighbor fingerprint shortcuts. TopU-LBVS provides three fixed protocols. TopU-LBVS-full evaluates ChEMBL

^\ast \rightarrow

TopU generalization across all 93 targets. TopU-LBVS-low evaluates low-data TopU

\rightarrow

TopU learning within the hard-negative distribution. TopU-LBVS-mini provides a compact seven-target protocol with a paired random-decoy control that changes only the test decoys, enabling low-cost development and direct measurement of the gap between random ChEMBL

^\ast

and TopU decoys. Across ten reference baselines spanning fingerprint methods, molecular GNNs, fingerprint hybrids, and modern molecular models, performance under random-decoy evaluation degrades sharply under hard-negative screening. We release data, fixed splits, evaluation code, and baseline implementations for reproducible comparison of future LBVS and molecular representation learning methods. Code and data are available at https://github.com/topu-benchmark/topu-lbvs and https://huggingface.co/datasets/topu-benchmark/topu-lbvs.

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