ArticleJournal of chemical information and modeling2023
Machine Learning-Boosted Docking Enables the Efficient Structure-Based Virtual Screening of Giga-Scale Enumerated Chemical Libraries.
Article in Journal of chemical information and modeling, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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Who cites it
26 citing papers in PubMed, 34 citations in OpenAlex.
- DockM8: an all-in-one open-source platform for consensus virtual screening in drug design.Journal of cheminformatics · 2026Article
- 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
- Chemical Distance-Based Acceleration of Large Library Docking with ChemSTEP.ACS central science · 2026Article
- Deep docking, part 2: an amplified DDU platform for ultra-large virtual screening.Chemical science · 2026Article
- V-SYNTHES2-the next generation tool for structure-based virtual screening of giga-scale chemical spaces.npj drug discovery · 2026Article
- A Reproducible Hierarchical Virtual Screening Framework Integrating Scaffold-Aware Machine Learning, Ensemble Docking, and Molecular Dynamics: Application to IDO1.Journal of chemical information and modeling · 2026Article
- Phenotypic AI-based design of cell-specific small molecule cytotoxics.Communications chemistry · 2026Article
- Constructing and characterizing trillion-scale combinatorial chemical library.Journal of cheminformatics · 2026Article
- Equivariant graph neural network-based accurate and ultra-fast virtual screening of small molecules targeting miRNA-protein complex.Journal of pharmaceutical analysis · 2026Article
- Solvent-Site Prediction for Fragment Docking and Its Implication on Fragment-Based Drug Discovery.Journal of chemical information and modeling · 2025Article
- Ultra-large library screening with an evolutionary algorithm in Rosetta (REvoLd).Communications chemistry · 2025Article
- Molecular Docking as a Key Driver of Biocontrol for Agri-Food Security.Biotech (Basel (Switzerland)) · 2025Review
- V-synthes2 - the Next Generation Tool for Structure-based Virtual Screening of Giga-scale Chemical Spaces.Research square · 2025Article
- Enhanced Thompson sampling by roulette wheel selection for screening ultralarge combinatorial libraries.Journal of cheminformatics · 2025Article
- A bottom-up approach to find lead compounds in expansive chemical spaces.Communications chemistry · 2025Article
- Identification of nanomolar adenosine ANature communications · 2025Article
- A Database for Large-Scale Docking and Experimental Results.Journal of chemical information and modeling · 2025Article
- Assessing the Robustness and Scalability of Machine Learning Methods to Accelerate Ultralarge High-Throughput Docking Campaigns.ACS omega · 2025Article
- The Need for Continuing Blinded Pose- and Activity Prediction Benchmarks.Journal of chemical information and modeling · 2025Review
- Active learning driven prioritisation of compounds from on-demand libraries targeting the SARS-CoV-2 main protease.Digital discovery · 2025Article
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Authors and funding
6 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The emergence of ultra-large screening libraries, filled to the brim with billions of readily available compounds, poses a growing challenge for docking-based virtual screening. Machine learning (ML)-boosted strategies like the tool HASTEN combine rapid ML prediction with the brute-force docking of small fractions of such libraries to increase screening throughput and take on giga-scale libraries. In our case study of an anti-bacterial chaperone and an anti-viral kinase, we first generated a brute-force docking baseline for 1.56 billion compounds in the Enamine REAL lead-like library with the fast Glide high-throughput virtual screening protocol. With HASTEN, we observed robust recall of 90% of the true 1000 top-scoring virtual hits in both targets when docking only 1% of the entire library. This reduction of the required docking experiments by 99% significantly shortens the screening time. In the kinase target, the employment of a hydrogen bonding constraint resulted in a major proportion of unsuccessful docking attempts and hampered ML predictions. We demonstrate the optimization potential in the treatment of failed compounds when performing ML-boosted screening and benchmark and showcase HASTEN as a fast and robust tool in a growing arsenal of approaches to unlock the chemical space covered by giga-scale screening libraries for everyday drug discovery campaigns.
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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.