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Quantitative Polypharmacology Profiling Based on a Multifingerprint Similarity Predictive Approach
Journal of Chemical Information and Modeling ( IF 5.6 ) Pub Date : 2021-09-27 , DOI: 10.1021/acs.jcim.1c00498
Fulvio Ciriaco 1 , Nicola Gambacorta 2 , Domenico Alberga 2 , Orazio Nicolotti 2
Affiliation  

We present a new quantitative ligand-based bioactivity prediction approach employing a multifingerprint similarity search algorithm, enabling the polypharmacological profiling of small molecules. Quantitative bioactivity predictions are made on the basis of the statistical distributions of multiple Tanimoto similarity θ values, calculated through 13 different molecular fingerprints, and of the variation of the measured biological activity, reported as ΔpIC50, for all of the ligands sharing a given protein drug target. The application data set comprises as much as 4241 protein drug targets as well as 418 485 ligands selected from ChEMBL (release 25) by employing a set of well-defined filtering rules. Several large internal and external validation studies were carried out to demonstrate the robustness and the predictive potential of the herein proposed method. Additional comparative studies, carried out on two freely available and well-known ligand–target prediction platforms, demonstrated the reliability of our proposed approach for accurate ligand–target matching. Moreover, two applicative cases were also discussed to practically describe how to use our predictive algorithm, which is freely available as a user-friendly web platform. The user can screen single or multiple queries at a time and retrieve the output as a terse html table or as a json file including all of the information concerning the explored similarities to obtain a deeper understanding of the results. High-throughput virtual reverse screening campaigns, allowing for a given query compound the quick detection of the potential drug target from a large collection of them, can be carried out in batch on demand.

中文翻译:

基于多指纹相似性预测方法的定量多药理学分析

我们提出了一种新的基于配体的定量生物活性预测方法,该方法采用多指纹相似性搜索算法,能够对小分子进行多药理学分析。基于通过 13 个不同分子指纹计算的多个 Tanimoto 相似性 θ 值的统计分布以及测量的生物活性的变化(报告为 ΔpIC 50),进行定量生物活性预测,对于共享给定蛋白质药物靶标的所有配体。应用数据集包含多达 4241 个蛋白质药物靶点以及 418485 个配体,这些配体通过采用一组明确定义的过滤规则从 ChEMBL(第 25 版)中选出。进行了几项大型内部和外部验证研究,以证明本文提出的方法的稳健性和预测潜力。在两个免费提供且众所周知的配体-靶标预测平台上进行的其他比较研究证明了我们提出的准确配体-靶标匹配方法的可靠性。此外,还讨论了两个应用案例,以实际描述如何使用我们的预测算法,该算法作为用户友好的网络平台免费提供。用户可以一次筛选单个或多个查询,并以简洁的 html 表或 json 文件的形式检索输出,其中包含有关探索的相似性的所有信息,以获得对结果的更深入理解。高通量虚拟反向筛选活动允许对给定的查询化合物从大量集合中快速检测潜在的药物目标,可以按需分批进行。
更新日期:2021-10-25
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