ReviewNature protocols2022
Artificial intelligence-enabled virtual screening of ultra-large chemical libraries with deep docking.
Review in Nature protocols, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 172 papers.
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The trial behind it
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Who cites it
172 citing papers in PubMed, 367 citations in OpenAlex.
- Degrading GPX4 via Biomimetic Nanoparticle-Mediated In Situ Synthesis of Deep-Learning-Designed Binder-Degron Chimeras for Prostate Cancer Therapy.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Docking-based virtual screening: Past, present, and future.Biophysical journal · 2026Review
- 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
- Review
- Chemical Distance-Based Acceleration of Large Library Docking with ChemSTEP.ACS central science · 2026Article
- Siteomix: a PyMOL plugin for the detection, alignment, and similarity analysis of protein‒ligand binding sites.Journal of computer-aided molecular design · 2026Article
- Targeting WEE1 kinase: an integrated machine learning-cheminformatics framework for ultra-large-scale virtual screening and novel inhibitor discovery.Journal of computer-aided molecular design · 2026Article
- AOP-Based Analysis of Curated Transcriptomic Data Reveals a Context-Dependent Core Mechanism of PFAS-Induced Liver Steatosis.Environmental science & technology · 2026Article
- Automated Parallel Synthesis Accelerates Virtual Screening Hit Discovery.Journal of the American Chemical Society · 2026Article
- AI-Driven discovery and experimental validation of covalent FGFR4 inhibitors for hepatocellular carcinoma.Journal of translational medicine · 2026Article
- Rapid and energy-efficient ultra-large library screening for drug discovery on a SpiNNaker2 neuromorphic chip.Communications chemistry · 2026Article
- Deep docking, part 2: an amplified DDU platform for ultra-large virtual screening.Chemical science · 2026Article
- Prioritising search for virtual screening via preliminary interpretable low-feature likelihood-based rankings of drug-target activity measures.BMC bioinformatics · 2026Article
- V-SYNTHES2-the next generation tool for structure-based virtual screening of giga-scale chemical spaces.npj drug discovery · 2026Article
- Distillation enables scalable high-fidelity virtual screening across ultra-large chemical libraries.bioRxiv : the preprint server for biology · 2026Article
- Deep3D-DTA: A Tri-Modal Deep Learning Framework for Binding Affinity Prediction Leveraging 3D Structural Representations of Drugs and Targets.Interdisciplinary sciences, computational life sciences · 2026Article
- The Tsetlin Machine: A "Third Way" in QSAR Modeling.Journal of chemical information and modeling · 2026Article
- Exploring the global mosaic of medicinal plant databases: unveiling nature's pharmacopoeia.Natural products and bioprospecting · 2026Review
- Phenotypic AI-based design of cell-specific small molecule cytotoxics.Communications chemistry · 2026Article
- AI-driven fungicide design: From target identification to field application.Plant communications · 2026Review
112 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
8 authors at 2 institutions in 3 countries.
Funding
Abstract
With the recent explosion of chemical libraries beyond a billion molecules, more efficient virtual screening approaches are needed. The Deep Docking (DD) platform enables up to 100-fold acceleration of structure-based virtual screening by docking only a subset of a chemical library, iteratively synchronized with a ligand-based prediction of the remaining docking scores. This method results in hundreds- to thousands-fold virtual hit enrichment (without significant loss of potential drug candidates) and hence enables the screening of billion molecule-sized chemical libraries without using extraordinary computational resources. Herein, we present and discuss the generalized DD protocol that has been proven successful in various computer-aided drug discovery (CADD) campaigns and can be applied in conjunction with any conventional docking program. The protocol encompasses eight consecutive stages: molecular library preparation, receptor preparation, random sampling of a library, ligand preparation, molecular docking, model training, model inference and the residual docking. The standard DD workflow enables iterative application of stages 3-7 with continuous augmentation of the training set, and the number of such iterations can be adjusted by the user. A predefined recall value allows for control of the percentage of top-scoring molecules that are retained by DD and can be adjusted to control the library size reduction. The procedure takes 1-2 weeks (depending on the available resources) and can be completely automated on computing clusters managed by job schedulers. This open-source protocol, at https://github.com/jamesgleave/DD_protocol , can be readily deployed by CADD researchers and can significantly accelerate the effective exploration of ultra-large portions of a chemical space.
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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.