ArticleJournal of chemical information and modeling2024
Streamlining Large Chemical Library Docking with Artificial Intelligence: the PyRMD2Dock Approach.
Article in Journal of chemical information and modeling, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- AI-Enforced Ultra-Large Virtual Screening Discovers Potent CD28 Binders.Journal of chemical information and modeling · 2026Article
- AI-driven computational drug design: tools, workflow and challenges.RSC advances · 2026Review
- PyRMD Studio: A Unified Suite for Next-Generation, AI-Powered Virtual Screening.Journal of chemical information and modeling · 2026Article
- Structure-based screening and a conformational biosensor identify a GPR183 inverse agonist and an activation switch.Nature communications · 2026Article
- GNN-MA: Soft Molecular Alignment with Cross-Graph Attention for Ligand-Based Virtual Screening.Molecules (Basel, Switzerland) · 2026Article
- Article
- Developmental neurotoxicity of an anatoxin-a-producing cyanobacteria (Frontiers in microbiology · 2025Article
- Protein Structure-Based Organic Chemistry-Driven Ligand Design from Ultralarge Chemical Spaces.ACS central science · 2024Article
- Large language models facilitating modern molecular biology and novel drug development.Frontiers in pharmacology · 2024Review
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The present contribution introduces a novel computational protocol called PyRMD2Dock, which combines the Ligand-Based Virtual Screening (LBVS) tool PyRMD with the popular docking software AutoDock-GPU (AD4-GPU) to enhance the throughput of virtual screening campaigns for drug discovery. By implementing PyRMD2Dock, we demonstrate that it is possible to rapidly screen massive chemical databases and identify those with the highest predicted binding affinity to a target protein. Our benchmarking and screening experiments illustrate the predictive power and speed of PyRMD2Dock and highlight its potential to accelerate the discovery of novel drug candidates. Overall, this study showcases the value of combining AI-powered LBVS tools with docking software to enable effective and high-throughput virtual screening of ultralarge molecular databases in drug discovery. PyRMD and the PyRMD2Dock protocol are freely available on GitHub (https://github.com/cosconatilab/PyRMD) as an open-source tool.
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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.