Enhancing the Reliability of Integrated Consensus Strategies to Boost Docking-Based Screening Campaigns Using Publicly Available Docking Programs.

IF 2.8 4区医学 Q3 CHEMISTRY, MEDICINAL

Molecular Informatics Pub Date : 2025-06-01 DOI:10.1002/minf.2445

Valeria Scardino, M Justina Galarce, M Emilia Mignone, Claudio N Cavasotto

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Abstract

The use of docking-based virtual screening is today an established critical component within the drug discovery pipeline. In the context where the performance of molecular docking has been found to depend on the protein target and the program, consensus docking has been found to be a valuable approach to enhance the performance of high-throughput docking (HTD). We present and evaluate an integrated pose and ranking consensus approach that combines the advantages of pose consensus and the exponential consensus ranking (ECR) approach, using only publicly available docking programs (rDock, DOCK 6, Auto Dock 4, PLANTS, and Vina). Based on a thorough analysis performed to assess the optimal combination of matching poses and ECR thresholds, using a benchmarking set of 50 protein targets of diverse families and different property-matched ligand/decoy libraries, this enhanced pose/ranking consensus approach displayed a notably superior performance than the individual docking programs, and the ECR. This approach was also evaluated in HTD campaigns using larger libraries (∼1.1 million molecules) on six targets, thus obtaining an average improvement of the ECR of about 40%. We thus may say that this pose/ranking consensus methodology can be confidently used in prospective HTD campaigns using free-available docking programs.

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提高综合共识策略的可靠性，以促进基于对接的筛查活动，使用公开可用的对接计划。

使用基于对接的虚拟筛选是当今药物发现管道中建立的关键组成部分。在发现分子对接的性能依赖于蛋白靶点和程序的情况下，共识对接被认为是提高高通量对接（HTD）性能的一种有价值的方法。我们提出并评估了一种综合姿态和排名共识方法，该方法结合了姿态共识和指数共识排名（ECR）方法的优点，仅使用公开可用的对接程序（rDock, DOCK 6, Auto DOCK 4， PLANTS和Vina）。基于对不同家族和不同属性匹配配体/诱饵库的50个蛋白质靶标的基准集进行的全面分析，以评估匹配姿态和ECR阈值的最佳组合，这种增强的姿态/排名共识方法显示出明显优于单个对接方案和ECR的性能。该方法也在HTD活动中进行了评估，在六个靶标上使用更大的文库（约110万个分子），从而获得了约40%的ECR平均改善。因此，我们可以说，这种姿态/排名共识方法可以自信地用于使用免费对接程序的未来HTD活动。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Molecular Informatics CHEMISTRY, MEDICINAL-MATHEMATICAL & COMPUTATIONAL BIOLOGY

CiteScore

7.30

自引率

2.80%

发文量

审稿时长

3 months

期刊介绍： Molecular Informatics is a peer-reviewed, international forum for publication of high-quality, interdisciplinary research on all molecular aspects of bio/cheminformatics and computer-assisted molecular design. Molecular Informatics succeeded QSAR & Combinatorial Science in 2010. Molecular Informatics presents methodological innovations that will lead to a deeper understanding of ligand-receptor interactions, macromolecular complexes, molecular networks, design concepts and processes that demonstrate how ideas and design concepts lead to molecules with a desired structure or function, preferably including experimental validation. The journal''s scope includes but is not limited to the fields of drug discovery and chemical biology, protein and nucleic acid engineering and design, the design of nanomolecular structures, strategies for modeling of macromolecular assemblies, molecular networks and systems, pharmaco- and chemogenomics, computer-assisted screening strategies, as well as novel technologies for the de novo design of biologically active molecules. As a unique feature Molecular Informatics publishes so-called "Methods Corner" review-type articles which feature important technological concepts and advances within the scope of the journal.