ArticleNature computational science2025
Rapid traversal of vast chemical space using machine learning-guided docking screens.
Article in Nature computational science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers.
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Who cites it
37 citing papers in PubMed.
- Artificial intelligence and ultra-high performance computing methods and experiments for drug discovery: virtual screening, deep learning, molecular dynamics simulations, ADMET modelling, and experimental validation.Molecular biomedicine · 2026Review
- Pushing the boundaries of virtual screening scale of combinatorial spaces with the V-SYNTHES approach.npj drug discovery · 2026Article
- AI-driven drug design: a comprehensive review.Journal of computer-aided molecular design · 2026Review
- Recent Advances in the Design of Inhibitors Targeting the Viral Entry and Replication of the SARS-CoV-2 Virus, Driven by In Silico Approaches.Molecules (Basel, Switzerland) · 2026Review
- DeorphaNN: Virtual screening of GPCR peptide agonists using AlphaFold-predicted active-state complexes and deep learning embeddings.Molecular cell · 2026Article
- Automated Parallel Synthesis Accelerates Virtual Screening Hit Discovery.Journal of the American Chemical Society · 2026Article
- Rapid and energy-efficient ultra-large library screening for drug discovery on a SpiNNaker2 neuromorphic chip.Communications chemistry · 2026Article
- Distillation enables scalable high-fidelity virtual screening across ultra-large chemical libraries.bioRxiv : the preprint server for biology · 2026Article
- V-SYNTHES2-the next generation tool for structure-based virtual screening of giga-scale chemical spaces.npj drug discovery · 2026Article
- Multi-omics and artificial intelligence for precision drug discovery and potential clinical applications.Signal transduction and targeted therapy · 2026Review
- The Selectivity Implications of Docking Libraries with Greater and Lesser Similarities to Bio-like Molecules.Journal of medicinal chemistry · 2026Article
- Scaffold-based evaluation metrics for fair comparison of molecular generators.Journal of cheminformatics · 2026Article
- ChemBang: Expanding the Chemical Space Around Small Molecules.Molecular informatics · 2026Article
- Distribution-preserved sampling (DPS) for smarter machine learning assisted ultra-large-scale virtual screening.RSC advances · 2026Article
- Molecular Modeling of the Pathogenetic Mechanisms of Neuropsychiatric Disorders.International journal of molecular sciences · 2026Review
- Simulation-guided design of peptide-metal coordination interfaces for next-generation metallo-immunotherapy.Nano convergence · 2026Review
- Anatomically Guided Non-Viral CRISPR/Cas9 Delivery in the Eye: Overcoming Barriers for Precision Gene Therapy.Pharmaceutics · 2026Review
- AI-guided competitive docking for virtual screening and compound efficacy prediction.npj drug discovery · 2026Article
- AI accelerate the identification of druggable targets by 3D structures of proteins and compounds.NPJ precision oncology · 2026Review
- The selectivity implications of docking libraries with greater and lesser similarities to bio-like molecules.bioRxiv : the preprint server for biology · 2026Article
Corrections and comments
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Authors and funding
11 authors.
Funding
Abstract
The accelerating growth of make-on-demand chemical libraries provides unprecedented opportunities to identify starting points for drug discovery with virtual screening. However, these multi-billion-scale libraries are challenging to screen, even for the fastest structure-based docking methods. Here we explore a strategy that combines machine learning and molecular docking to enable rapid virtual screening of databases containing billions of compounds. In our workflow, a classification algorithm is trained to identify top-scoring compounds based on molecular docking of 1 million compounds to the target protein. The conformal prediction framework is then used to make selections from the multi-billion-scale library, reducing the number of compounds to be scored by docking. The CatBoost classifier showed an optimal balance between speed and accuracy and was used to adapt the workflow for screens of ultralarge libraries. Application to a library of 3.5 billion compounds demonstrated that our protocol can reduce the computational cost of structure-based virtual screening by more than 1,000-fold. Experimental testing of predictions identified ligands of G protein-coupled receptors and demonstrated that our approach enables discovery of compounds with multi-target activity tailored for therapeutic effect.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.