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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GC–MS analysis and in silico approaches to Stichopus hermanii as anti-inflammatory through PKC-β inhibition

Results in Chemistry, Journal Year: 2025, Volume and Issue: 14, P. 102086 - 102086

Published: Feb. 1, 2025

Language: Английский

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Ligand-based cheminformatics and free energy-inspired molecular simulations for prioritizing and optimizing G-protein coupled receptor kinase-6 (GRK6) inhibitors in multiple myeloma treatment

Computational Biology and Chemistry, Journal Year: 2025, Volume and Issue: 115, P. 108347 - 108347

Published: Jan. 13, 2025

Language: Английский

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In Silico Screening of Therapeutic Targets as a Tool to Optimize the Development of Drugs and Nutraceuticals in the Treatment of Diabetes mellitus: A Systematic Review

International Journal of Molecular Sciences, Journal Year: 2024, Volume and Issue: 25(17), P. 9213 - 9213

Published: Aug. 25, 2024

The Target-Based Virtual Screening approach is widely employed in drug development, with docking or molecular dynamics techniques commonly utilized for this purpose. This systematic review (SR) aimed to identify silico therapeutic targets treating Diabetes mellitus (DM) and answer the question: What have been used analyses treatment of DM? SR was developed following guidelines Preferred Reporting Items Checklist Systematic Review Meta-Analysis, accordance protocol registered PROSPERO (CRD42022353808). Studies that met PECo strategy (Problem, Exposure, Context) were included using databases: Medline (PubMed), Web Science, Scopus, Embase, ScienceDirect, Health Library. A total 20 articles included, which not only identified but also conducted vivo validate obtained results. most frequently indicated studies GLUT4, DPP-IV, PPARγ. In conclusion, a diversity DM verified through both reassessment. contributes discovery potential new allies DM.

Language: Английский

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Harnessing free energy calculations to achieve kinome-wide selectivity in drug discovery campaigns: Wee1 case study

Published: Aug. 20, 2024

Free energy calculations are revolutionizing early-stage drug-discovery campaigns. Robust free methods can rapidly provide accurate on-target and off-target potency predictions to identify promising chemical matter for synthesis, thus, inspiring further rounds of ideation optimization. Here, we present a framework efficiently achieving kinome-wide selectivity that led the discovery novel selective Wee1 kinase inhibitors. With ligand-based relative binding calculations, multiple scaffolds were identified. protein residue mutation perturbed gatekeeper residue, liabilities across kinome these series reduced. judicious identification handle, applying this computational strategy could effectively streamline optimization profiles, thereby accelerating drug timelines decreasing unanticipated toxicities.

Language: Английский

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Design, synthesis and FXR partial agonistic activity of anthranilic acid derivatives bearing aryloxy moiety as therapeutic agents for metabolic dysfunction-associated steatohepatitis

Bioorganic Chemistry, Journal Year: 2024, Volume and Issue: 153, P. 107940 - 107940

Published: Nov. 3, 2024

Language: Английский

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In Silico and In Vivo Studies of β-Sitosterol Nanoparticles as a Potential Therapy for Isoprenaline-Induced Cognitive Impairment in Myocardial Infarction, Targeting Myeloperoxidase

Pharmaceuticals, Journal Year: 2024, Volume and Issue: 17(8), P. 1093 - 1093

Published: Aug. 21, 2024

Objective: This study aimed to compare the effects of β-sitosterol nanoparticles (BETNs) and (BET) on cognitive impairment, oxidative stress, inflammation in a myocardial infarction (MI) rat model using silico vivo methods. Methods: β-Sitosterol myeloperoxidase (MPO) ligand-receptor binding affinities were evaluated Autodock Vina for docking Gromacs dynamics simulations. BET nanoparticles, prepared via solvent evaporation, had their size confirmed by nanoparticle analyzer. ISO-induced impairment rats was assessed through Morris water maze Cook’s pole climbing tests. Oxidative inflammation, cardiac injury measuring GSH, SOD, MDA, MPO, CkMB, LDH, lipid profiles, ECGs. Histopathology CA1 hippocampus tissue performed H&E staining. Results: In analyses revealed strong between suggesting BET’s potential anti-inflammatory effect. BETN (119.6 ± 42.6 nm; PDI: 0.809) significantly improved MI-induced dysfunction (p < 0.001 ***), increased hippocampal GSH 0.01 **) SOD levels, decreased MDA 0.05 *) MPO levels **). BETNs also elevated reduced **), CkMB LDH levels. It restored normalized ECG patterns, histology region myocardium. Conclusions: Compared with treatment, more effective improving damage, MI rats, its treating associated pathological changes MI.

Language: Английский

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Computational identification and analysis of CNP0269688 as a natural product inhibitor disrupting the interaction between the HIV matrix domain and tRNA

Frontiers in Chemistry, Journal Year: 2024, Volume and Issue: 12

Published: Sept. 2, 2024

Our research is dedicated to combating HIV by targeting its Matrix (MA) domain, which crucial for viral assembly and replication. This strategy specifically aims interrupt early-stage infection deter drug resistance focusing on this essential domain. Due the MA domain’s conservation across different strains, our approach promises broad-spectrum efficacy, particularly in regions marked significant genetic diversity issues. In study, we introduce CNP0269688, a natural product that exhibits high affinity HIV-1 Matrix. Through detailed molecular dynamics simulations, have assessed compound’s structural stability interaction dynamics, potential hinder Protein-tRNA interactions. analysis lays groundwork future experimental investigations. efforts are steps toward enhancing treatment, reducing transmission, curbing resistance, with ultimate aim of controlling eradicating pandemic, thereby contributing significantly public health scientific advancement.

Language: Английский

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Stereo-selectivity of enantiomeric inhibitors to ubiquitin-specific protease 7 (USP7) dissected by molecular docking, molecular dynamics simulations, and binding free energy calculations

Molecular Diversity, Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 19, 2024

Language: Английский

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