ArticleThe Journal of chemical physics2020
A combination of machine learning and infrequent metadynamics to efficiently predict kinetic rates, transition states, and molecular determinants of drug dissociation from G protein-coupled receptors.
Article in The Journal of chemical physics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed, 50 citations in OpenAlex.
- Enhanced Sampling in the Age of Machine Learning: Algorithms and Applications.Chemical reviews · 2026Review
- Structural Determinants of Buprenorphine Partial Agonism at the μ-Opioid Receptor.Journal of chemical information and modeling · 2025Article
- Molecular Glues: A New Approach to Modulating GPCR Signaling Bias.Biochemistry · 2025Review
- Alchemical Free-Energy Calculations at Quantum-Chemical Precision.The journal of physical chemistry letters · 2025Article
- Advances and Challenges in Milestoning Simulations for Drug-Target Kinetics.Journal of chemical theory and computation · 2024Article
- Incorporating Prior Knowledge in the Seeds of Adaptive Sampling Molecular Dynamics Simulations of Ligand Transport in Enzymes with Buried Active Sites.Journal of chemical theory and computation · 2024Article
- Understanding the impact of binding free energy and kinetics calculations in modern drug discovery.Expert opinion on drug discovery · 2024Review
- How Robust Is the Ligand Binding Transition State?Journal of the American Chemical Society · 2023Article
- Perspectives on Ligand/Protein Binding Kinetics Simulations: Force Fields, Machine Learning, Sampling, and User-Friendliness.Journal of chemical theory and computation · 2023Review
- Predicting Biomolecular Binding Kinetics: A Review.Journal of chemical theory and computation · 2023Review
- Structure-Kinetics Relationships of Opioids from Metadynamics and Machine Learning Analysis.Journal of chemical information and modeling · 2023Article
- Structure-Kinetics Relationships of Opioids from Metadynamics and Machine Learning.bioRxiv : the preprint server for biology · 2023Article
- Protein Function Analysis through Machine Learning.Biomolecules · 2022Review
- GLOW: A Workflow Integrating Gaussian-Accelerated Molecular Dynamics and Deep Learning for Free Energy Profiling.Journal of chemical theory and computation · 2022Article
- Metadynamics simulations leveraged by statistical analyses and artificial intelligence-based tools to inform the discovery of G protein-coupled receptor ligands.Frontiers in endocrinology · 2022Review
- Recent Molecular Insights into Agonist-specific Binding to the Mu-Opioid Receptor.Frontiers in molecular biosciences · 2022Review
- The Impact of the Secondary Binding Pocket on the Pharmacology of Class A GPCRs.Frontiers in pharmacology · 2022Review
- Article
- Pepsin-like aspartic proteases (PAPs) as model systems for combining biomolecular simulation with biophysical experiments.RSC advances · 2021Review
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
Determining the drug-target residence time (RT) is of major interest in drug discovery given that this kinetic parameter often represents a better indicator of in vivo drug efficacy than binding affinity. However, obtaining drug-target unbinding rates poses significant challenges, both computationally and experimentally. This is particularly palpable for complex systems like G Protein-Coupled Receptors (GPCRs) whose ligand unbinding typically requires very long timescales oftentimes inaccessible by standard molecular dynamics simulations. Enhanced sampling methods offer a useful alternative, and their efficiency can be further improved by using machine learning tools to identify optimal reaction coordinates. Here, we test the combination of two machine learning techniques, automatic mutual information noise omission and reweighted autoencoded variational Bayes for enhanced sampling, with infrequent metadynamics to efficiently study the unbinding kinetics of two classical drugs with different RTs in a prototypic GPCR, the μ-opioid receptor. Dissociation rates derived from these computations are within one order of magnitude from experimental values. We also use the simulation data to uncover the dissociation mechanisms of these drugs, shedding light on the structures of rate-limiting transition states, which, alongside metastable poses, are difficult to obtain experimentally but important to visualize when designing drugs with a desired kinetic profile.
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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.