Rethinking the 'best method' paradigm: The effectiveness of hybrid and multidisciplinary approaches in chemoinformatics

José L. Medina-Franco , Johny R. Rodríguez-Pérez , Héctor F. Cortés-Hernández , Edgar López-López
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Abstract

In Chemoinformatics, as in many other computational-related disciplines, it is a common practice to identify the “single best” approach or methodology, for instance, identify the best fingerprint representation, the best single virtual screening approach or protocol, the optimal representation of the chemical space, the best predictive model, to name a few. In molecular modeling, a typical example is finding the best docking program. However, it is also known that each approach has its advantages and limitations. There are examples of benchmark studies comparing different approaches to find the most appropriate solution, and it is common to find that there are no single best programs in such studies. Yet, searching for the “best” methods is still common. The main goal of this work is to survey hybrid methodologies recently developed in Chemoinformatics. The list of approaches is not exhaustive, but it aims to cover several representative applications. One of the major outcomes of the survey is that, for various purposes, individual methods do not perform as well as the combination of approaches because single methods have inherent limitations with advantages and disadvantages.

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重新思考“最佳方法”范式:化学信息学中混合和多学科方法的有效性
在化学信息学中,与许多其他与计算相关的学科一样,确定“单一最佳”方法或方法是一种常见的做法,例如,确定最佳指纹表示,最佳单一虚拟筛选方法或协议,化学空间的最佳表示,最佳预测模型,等等。在分子建模中,寻找最佳对接方案是一个典型的例子。然而,众所周知,每种方法都有其优点和局限性。有一些比较不同方法以找到最合适的解决方案的基准研究的例子,并且通常发现在此类研究中没有单一的最佳方案。然而,寻找“最佳”方法仍然很常见。这项工作的主要目的是调查混合方法最近发展在化学信息学。方法列表并不详尽,但它旨在涵盖几个具有代表性的应用程序。调查的主要结果之一是,对于各种目的,单个方法不如方法组合的效果好,因为单个方法具有固有的优点和缺点的局限性。
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来源期刊
Artificial intelligence in the life sciences
Artificial intelligence in the life sciences Pharmacology, Biochemistry, Genetics and Molecular Biology (General), Computer Science Applications, Health Informatics, Drug Discovery, Veterinary Science and Veterinary Medicine (General)
CiteScore
5.00
自引率
0.00%
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0
审稿时长
15 days
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