ArticleJournal of chemical theory and computation2024
Calculating Protein-Ligand Residence Times through State Predictive Information Bottleneck Based Enhanced Sampling.
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 12 papers.
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12 citing papers in PubMed.
- Computational discovery of emodin-based anthraquinones as PARP-1 inhibitors with relevance to ovarian and prostate cancer.Scientific reports · 2026Article
- MAHLER: Integrating Metadynamics and Inverse Folding to Predict Antibody-Antigen Kinetics.bioRxiv : the preprint server for biology · 2026Article
- Altered residence time as a cause of drug resistance.Current opinion in structural biology · 2026Review
- Latent thermodynamic flows: unified representation learning and generative modeling of temperature-dependent behaviors from limited data.Chemical science · 2026Article
- Early-Enrichment Hit Discovery via Reversible-Work c(t) Estimation in Metadynamics (CTMD).bioRxiv : the preprint server for biology · 2026Article
- Using Time Dependent Rate Analysis to Evaluate the Quality of Machine Learned Reaction Coordinates for Biasing and Computing Kinetics.The journal of physical chemistry. B · 2025Article
- Residence time in drug discovery: current insights and future perspectives.Pharmacological reports : PR · 2025Review
- Augmenting Human Expertise in Weighted Ensemble Simulations through Deep Learning-Based Information Bottleneck.Journal of chemical theory and computation · 2024Article
- Advances and Challenges in Milestoning Simulations for Drug-Target Kinetics.Journal of chemical theory and computation · 2024Article
- Article
- Information Bottleneck Approach for Markov Model Construction.Journal of chemical theory and computation · 2024Article
- Therapeutic Potential and Mechanistic Insights of a Novel Synthetic α-Lactalbumin-Derived Peptide for the Treatment of Liver Fibrosis.Journal of clinical and experimental hepatologyArticle
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4 authors.
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Abstract
Understanding drug residence times in target proteins is key to improving drug efficacy and understanding target recognition in biochemistry. While drug residence time is just as important as binding affinity, atomic-level understanding of drug residence times through molecular dynamics (MD) simulations has been difficult primarily due to the extremely long time scales. Recent advances in rare event sampling have allowed us to reach these time scales, yet predicting protein-ligand residence times remains a significant challenge. Here we present a semi-automated protocol to calculate the ligand residence times across 12 orders of magnitude of time scales. In our proposed framework, we integrate a deep learning-based method, the state predictive information bottleneck (SPIB), to learn an approximate reaction coordinate (RC) and use it to guide the enhanced sampling method metadynamics. We demonstrate the performance of our algorithm by applying it to six different protein-ligand complexes with available benchmark residence times, including the dissociation of the widely studied anticancer drug Imatinib (Gleevec) from both wild-type Abl kinase and drug-resistant mutants. We show how our protocol can recover quantitatively accurate residence times, potentially opening avenues for deeper insights into drug development possibilities and ligand recognition mechanisms.
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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.