ArticleJournal of chemical theory and computation2024
Machine Learning Guided AQFEP: A Fast and Efficient Absolute Free Energy Perturbation Solution for Virtual Screening.
Article in Journal of chemical theory and computation, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- A simple compound prioritization method for drug discovery considering multi-target binding.Digital discovery · 2026Article
- Predicting the Impact of Drug Resistance Mutations on Inhibitor Potency with Molecular Dynamics and Machine Learning.The journal of physical chemistry. B · 2025Article
- Navigating chemical space: multi-level Bayesian optimization with hierarchical coarse-graining.Chemical science · 2025Article
- Guided multi-objective generative AI to enhance structure-based drug design.Chemical science · 2025Article
- Uncertainty quantification with graph neural networks for efficient molecular design.Nature communications · 2025Article
- How does machine learning augment alchemical binding free energy calculations?Future medicinal chemistry · 2025Article
- Optimal Molecular Design: Generative Active Learning Combining REINVENT with Precise Binding Free Energy Ranking Simulations.Journal of chemical theory and computation · 2024Article
- Machine Learning Guided AQFEP: A Fast and Efficient Absolute Free Energy Perturbation Solution for Virtual Screening.Journal of chemical theory and computation · 2024Article
Corrections and comments
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Authors and funding
9 authors.
Funding
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Abstract
Structure-based methods in drug discovery have become an integral part of the modern drug discovery process. The power of virtual screening lies in its ability to rapidly and cost-effectively explore enormous chemical spaces to select promising ligands for further experimental investigation. Relative free energy perturbation (RFEP) and similar methods are the gold standard for binding affinity prediction in drug discovery hit-to-lead and lead optimization phases, but have high computational cost and the requirement of a structural analog with a known activity. Without a reference molecule requirement, absolute FEP (AFEP) has, in theory, better accuracy for hit ID, but in practice, the slow throughput is not compatible with VS, where fast docking and unreliable scoring functions are still the standard. Here, we present an integrated workflow to virtually screen large and diverse chemical libraries efficiently, combining active learning with a physics-based scoring function based on a fast absolute free energy perturbation method. We validated the performance of the approach in the ranking of structurally related ligands, virtual screening hit rate enrichment, and active learning chemical space exploration; disclosing the largest reported collection of free energy simulations to date.
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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.