The Physics-AI Dialogue in Drug Design

RSC Medicinal Chemistry, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 1, 2025

A long path has led from the determination of first protein structure in 1960 to recent breakthroughs science. Protein prediction and design methodologies based on machine learning (ML) have been recognized with 2024 Nobel prize Chemistry, but they would not possible without previous work input many domain scientists. Challenges remain application ML tools for structural ensembles their usage within software pipelines by crystallography or cryogenic electron microscopy. In drug discovery workflow, techniques are being used diverse areas such as scoring docked poses, generation molecular descriptors. As become more widespread, novel applications emerge which can profit large amounts data available. Nevertheless, it is essential balance potential advantages against environmental costs deployment decide if when best apply it. For hit lead optimization efficiently interpolate between compounds chemical series free energy calculations dynamics simulations seem be superior designing derivatives. Importantly, complementarity and/or synergism physics-based methods (e.g., force field-based simulation models) data-hungry growing strongly. Current evolved decades research. It now necessary biologists, physicists, computer scientists fully understand limitations ensure that exploited design.

Language: Английский

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A thermodynamic cycle to predict the competitive inhibition outcomes of an evolving enzyme

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 7, 2025

Abstract Understanding competitive inhibition at the molecular level is essential for unraveling dynamics of enzyme-inhibitor interactions and predicting evolutionary outcomes resistance mutations. In this study, we present a framework linking to alchemical free energy perturbation (FEP) calculations, focusing on E. coli dihydrofolate reductase (DHFR) its by trimethoprim (TMP). Using thermodynamic cycles, relate experimentally measured binding constants ( K i m ) differences associated with wild-type mutant forms DHFR mean error 0.9 kcal/mol, providing insights into underpinnings TMP resistance. Our findings highlight importance local conformational in inhibition. Mutations affect substrate inhibitor affinities differently, influencing fitness landscape under selective pressure from TMP. FEP simulations reveal that mutations stabilize inhibitor-bound or substrate-bound states through specific structural and/or dynamical effects. The interplay these effects showcases significant epistasis certain cases. ability separately assess provides valuable insights, allowing more precise interpretation mutation epistatic interactions. Furthermore, identify key challenges simulations, including convergence issues arising charge-changing long-range allosteric By integrating computational experimental data, provide an effective approach functional impact their contributions landscapes. These pave way constructing robust mutational scanning protocols designing therapeutic strategies against resistant bacterial strains.

Language: Английский

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Covalent-Allosteric Inhibitors: Do We Get the Best of Both Worlds?

Journal of Medicinal Chemistry, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 12, 2025

Covalent-allosteric inhibitors (CAIs) may achieve the best of both worlds: increased potency, long-lasting effects, and reduced drug resistance typical covalent ligands, along with enhanced specificity decreased toxicity inherent in allosteric modulators. Therefore, CAIs can be an effective strategy to transform many undruggable targets into druggable ones. However, are challenging design. In this perspective, we analyze discovery known targeting three protein families: phosphatases, kinases, GTPases. We also discuss how computational methods tools play a role addressing practical challenges rational CAI

Language: Английский

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Discovery of Novel Pyridin-2-yl Urea Inhibitors Targeting ASK1 Kinase and Its Binding Mode by Absolute Protein–Ligand Binding Free Energy Calculations

International Journal of Molecular Sciences, Journal Year: 2025, Volume and Issue: 26(4), P. 1527 - 1527

Published: Feb. 12, 2025

Apoptosis signal-regulating kinase 1 (ASK1), a key component of the mitogen-activated protein (MAPK) cascades, has been identified as promising therapeutic target owing to its critical role in signal transduction pathways. In this study, we proposed novel pyridin-2-yl urea inhibitors exhibiting favorable physicochemical properties. The potency these compounds was validated through vitro bioassays. inhibition (IC50) compound 2 1.55 ± 0.27 nM, which comparable known clinical inhibitor, Selonsertib. To further optimize hit compounds, two possible binding modes were initially predicted by molecular docking. Absolute free energy (BFE) calculations based on dynamics simulations discriminated modes, presenting good tendency with bioassay results. This strategy, underpinned BFE calculations, great potential expedite drug discovery process targeting ASK1 kinase.

Language: Английский

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FEP-SPell-ABFE: An Open-Source Automated Alchemical Absolute Binding Free-Energy Calculation Workflow for Drug Discovery

Journal of Chemical Information and Modeling, Journal Year: 2025, Volume and Issue: unknown

Published: March 3, 2025

The binding affinity between a drug molecule and its target, measured by the absolute free energy (ABFE), is crucial factor in lead discovery phase of development. Recent research has highlighted potential silico ABFE predictions to directly aid development allowing for ranking prioritization promising candidates. This work introduces an open-source Python workflow called FEP-SPell-ABFE, designed automate calculations with minimal user involvement. requires only three key inputs: receptor protein structure PDB format, candidate ligands SDF configuration file (config.yaml) that governs both molecular dynamics simulation parameters. It produces ranked list along their energies comma-separated values (CSV) format. leverages SLURM (Simple Linux Utility Resource Management) automating task execution resource allocation across modules. A usage example several benchmark systems validation are provided. FEP-SPell-ABFE workflow, practical example, publicly accessible on GitHub at https://github.com/freeenergylab/FEP-SPell-ABFE, distributed under MIT License.

Language: Английский

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QuantumBind-RBFE: Accurate Relative Binding Free Energy Calculations Using Neural Network Potentials

Journal of Chemical Information and Modeling, Journal Year: 2025, Volume and Issue: unknown

Published: April 8, 2025

Accurate prediction of protein-ligand binding affinities is crucial in drug discovery, particularly during hit-to-lead and lead optimization phases, however, limitations ligand force fields continue to impact accuracy. In this work, we validate relative free energy (RBFE) accuracy using neural network potentials (NNPs) for the ligands. We utilize a novel NNP model, AceFF 1.0, based on TensorNet architecture small molecules that broadens applicability diverse drug-like compounds, including all important chemical elements supporting charged molecules. Using established benchmarks, show overall improved correlation affinity predictions compared with GAFF2 molecular mechanics ANI2-x NNPs. Slightly less but comparable correlations OPLS4. also can run simulations at 2 fs time step, least two times larger than previous models, providing significant speed gains. The results promise further evolutions calculations NNPs while demonstrating its practical use already current generation. code model are publicly available research use.

Language: Английский

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Enhancing the understandings on SARS-CoV-2 main protease (Mpro) mutants from molecular dynamics and machine learning

International Journal of Biological Macromolecules, Journal Year: 2025, Volume and Issue: unknown, P. 143076 - 143076

Published: April 1, 2025

Language: Английский

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A Thermodynamic Cycle to Predict the Competitive Inhibition Outcomes of an Evolving Enzyme

Journal of Chemical Theory and Computation, Journal Year: 2025, Volume and Issue: unknown

Published: April 23, 2025

Understanding competitive inhibition at the molecular level is essential for unraveling dynamics of enzyme-inhibitor interactions and predicting evolutionary outcomes resistance mutations. In this study, we present a framework linking to alchemical free energy perturbation (FEP) calculations, focusing on Escherichia coli dihydrofolate reductase (DHFR) its by trimethoprim (TMP). Using thermodynamic cycles, relate experimentally measured binding constants (Ki Km) differences associated with wild-type mutant forms DHFR mean error 0.9 kcal/mol, providing insight into underpinnings TMP resistance. Our findings highlight importance local conformational in inhibition. Mutations affect substrate inhibitor affinities differently, influencing fitness landscape under selective pressure from TMP. FEP simulations reveal that mutations stabilize inhibitor-bound or substrate-bound states through specific structural and/or dynamical effects. The interplay these effects showcases significant molecular-level epistasis certain cases. ability separately assess provides valuable insights, allowing more precise interpretation mutation epistatic interactions. Furthermore, identify key challenges simulations, including convergence issues arising charge-changing long-range allosteric By integrating computational experimental data, provide an effective approach functional impact their contributions landscapes. These insights pave way constructing robust mutational scanning protocols designing therapeutic strategies against resistant bacterial strains.

Language: Английский

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