A Comprehensive Survey of Prospective Structure-Based Virtual Screening for Early Drug Discovery in the Past Fifteen Years

International Journal of Molecular Sciences, Journal Year: 2022, Volume and Issue: 23(24), P. 15961 - 15961

Published: Dec. 15, 2022

Structure-based virtual screening (SBVS), also known as molecular docking, has been increasingly applied to discover small-molecule ligands based on the protein structures in early stage of drug discovery. In this review, we comprehensively surveyed prospective applications docking judged by solid experimental validations literature over past fifteen years. Herein, systematically analyzed novelty targets and hits, practical protocols screening, following validations. Among 419 case studies reviewed, most screenings were carried out widely studied targets, only 22% less-explored new targets. Regarding software, GLIDE is popular one used while DOCK 3 series showed a strong capacity for large-scale screening. Besides, majority identified hits are promising structural one-quarter better potency than 1 μM, indicating that primary advantage SBVS chemotypes rather highly potent compounds. Furthermore, studies, vitro bioassays validate which might limit further characterization development active Finally, several successful stories with extensive have highlighted, provide unique insights into future discovery campaigns.

Language: Английский

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Large library docking for novel SARS‐CoV‐2 main protease non‐covalent and covalent inhibitors

Protein Science, Journal Year: 2023, Volume and Issue: 32(8)

Published: June 25, 2023

Abstract Antiviral therapeutics to treat SARS‐CoV‐2 are needed diminish the morbidity of ongoing COVID‐19 pandemic. A well‐precedented drug target is main viral protease (M Pro ), which targeted by an approved and several investigational drugs. Emerging resistance has made new inhibitor chemotypes more pressing. Adopting a structure‐based approach, we docked 1.2 billion non‐covalent lead‐like molecules library 6.5 million electrophiles against enzyme structure. From these, 29 11 covalent inhibitors were identified in 37 series, most potent having IC 50 20 μM, respectively. Several series optimized, resulting low micromolar inhibitors. Subsequent crystallography confirmed docking predicted binding modes may template further optimization. While aid optimization M for SARS‐CoV‐2, modest success rate also reveals weaknesses our approach challenging targets like versus other where it been successful, techniques itself.

Language: Английский

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Structure-Based Discovery of Inhibitors of the SARS-CoV-2 Nsp14 N7-Methyltransferase

Journal of Medicinal Chemistry, Journal Year: 2023, Volume and Issue: 66(12), P. 7785 - 7803

Published: June 9, 2023

An under-explored target for SARS-CoV-2 is the S-adenosyl methionine (SAM)-dependent methyltransferase Nsp14, which methylates N7-guanosine of viral RNA at 5′-end, allowing virus to evade host immune response. We sought new Nsp14 inhibitors with three large library docking strategies. First, up 1.1 billion lead-like molecules were docked against enzyme's SAM site, leading IC50 values from 6 50 μM. Second, a 16 million fragments revealed 9 12 341 Third, 25 electrophiles covalently modify Cys387 7 3.5 39 Overall, 32 encompassing 11 chemotypes had < μM and 5 in 4 10 These are among first non-SAM-like providing starting points future optimization.

Language: Английский

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Phytochemicals in Drug Discovery—A Confluence of Tradition and Innovation

International Journal of Molecular Sciences, Journal Year: 2024, Volume and Issue: 25(16), P. 8792 - 8792

Published: Aug. 13, 2024

Phytochemicals have a long and successful history in drug discovery. With recent advancements analytical techniques methodologies, discovering bioactive leads from natural compounds has become easier. Computational like molecular docking, QSAR modelling machine learning, network pharmacology are among the most promising new tools that allow researchers to make predictions concerning products’ potential targets, thereby guiding experimental validation efforts. Additionally, approaches LC-MS or LC-NMR speed up compound identification by streamlining processes. Integrating structural computational biology aids lead identification, thus providing invaluable information understand how phytochemicals interact with targets body. An emerging approach is learning involving deep neural networks interrelate phytochemical properties diverse physiological activities such as antimicrobial anticancer effects.

Language: Английский

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Rapid traversal of vast chemical space using machine learning-guided docking screens

Nature Computational Science, Journal Year: 2025, Volume and Issue: unknown

Published: March 13, 2025

Abstract The accelerating growth of make-on-demand chemical libraries provides unprecedented opportunities to identify starting points for drug discovery with virtual screening. However, these multi-billion-scale are challenging screen, even the fastest structure-based docking methods. Here we explore a strategy that combines machine learning and molecular enable rapid screening databases containing billions compounds. In our workflow, classification algorithm is trained top-scoring compounds based on 1 million target protein. conformal prediction framework then used make selections from library, reducing number be scored by docking. CatBoost classifier showed an optimal balance between speed accuracy was adapt workflow screens ultralarge libraries. Application library 3.5 billion demonstrated protocol can reduce computational cost more than 1,000-fold. Experimental testing predictions identified ligands G protein-coupled receptors approach enables multi-target activity tailored therapeutic effect.

Language: Английский

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On the origins of SARS-CoV-2 main protease inhibitors

RSC Medicinal Chemistry, Journal Year: 2023, Volume and Issue: 15(1), P. 81 - 118

Published: Oct. 13, 2023

In order to address the world-wide health challenge caused by COVID-19 pandemic, 3CL protease/SARS-CoV-2 main protease (SARS-CoV-2-M

Language: Английский

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Covalent small-molecule inhibitors of SARS-CoV-2 Mpro: Insights into their design, classification, biological activity, and binding interactions

European Journal of Medicinal Chemistry, Journal Year: 2024, Volume and Issue: 277, P. 116704 - 116704

Published: Aug. 8, 2024

Language: Английский

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Regression-Based Active Learning for Accessible Acceleration of Ultra-Large Library Docking

Journal of Chemical Information and Modeling, Journal Year: 2023, Volume and Issue: 64(7), P. 2612 - 2623

Published: Dec. 29, 2023

Structure-based drug discovery is a process for both hit finding and optimization that relies on validated three-dimensional model of target biomolecule, used to rationalize the structure–function relationship this particular target. An ultralarge virtual screening approach has emerged recently rapid high-affinity compounds, but it requires substantial computational resources. This study shows active learning with simple linear regression models can accelerate screening, retrieving up 90% top-1% docking list after just 10% ligands. The results demonstrate unnecessary use complex models, such as deep approaches, predict imprecise ligand low sampling depth. Furthermore, we explore meta-parameters find constant batch size ensembling method provide best retrieval rate. Finally, our data set, 70% top-0.05% ligands only 2% library. Altogether, work provides computationally accessible accelerated serve blueprint future design low-compute agents exploration chemical space via large-scale docking. With recent breakthroughs in protein structure prediction, significantly increase accessibility academic community aid compounds various targets.

Language: Английский

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Identifying Artifacts from Large Library Docking

Journal of Medicinal Chemistry, Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 10, 2024

While large library docking has discovered potent ligands for multiple targets, as the libraries have grown hit lists can become dominated by rare artifacts that cheat our scoring functions. Here, we investigate rescoring top-ranked docked molecules with orthogonal methods to identify these artifacts, exploring implicit solvent models and absolute binding free energy perturbation cross-filters. In retrospective studies, this approach deprioritized high-ranking nonbinders nine targets while leaving true relatively unaffected. We tested method

Language: Английский

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Structure-based discovery of highly bioavailable, covalent, broad-spectrum coronavirus M ^Pro inhibitors with potent in vivo efficacy

Science Advances, Journal Year: 2025, Volume and Issue: 11(17)

Published: April 23, 2025

The main protease (M Pro ) of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a validated drug target. Starting with lead-like dihydrouracil chemotype identified in large-library docking campaign, we improved M inhibition >1000-fold by engaging additional subsites and using latent electrophile to engage Cys 145 . Advanced leads from this series show pan-coronavirus antiviral activity, low clearance mice, for AVI-4773 , rapid reduction viral titers >1,000,000 after just three doses. Both compounds are well distributed mouse tissues, including brain, where concentrations >1000× the 90% effective concentration observed 8 hours oral dosing AVI-4516 shows minimal major cytochrome P450s human proteases. also exhibits synergy RNA-dependent RNA polymerase inhibitor, molnupiravir, cellular infection models. Related analogs strongly inhibit nirmatrelvir-resistant mutant virus. properties differentiated existing clinical preclinical inhibitors will advance therapeutic development against emerging SARS-CoV-2 variants other coronaviruses.

Language: Английский

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