计算机科学
领域(数学)
班级(哲学)
分类方案
人工智能
机器学习
数学
纯数学
摘要
Scoring functions are a class of computational methods widely applied in structure-based drug design for evaluating protein–ligand interactions. Dozens of scoring functions have been published since the early 1990s. In literature, scoring functions are typically classified as force-field-based, empirical, and knowledge-based. This classification scheme has been quoted for more than a decade and is still repeatedly quoted by some recent publications. Unfortunately, it does not reflect the recent progress in this field. Besides, the naming convention used for describing different types of scoring functions has been somewhat jumbled in literature, which could be confusing for newcomers to this field. Here, we express our viewpoint on an up-to-date classification scheme and appropriate naming convention for current scoring functions. We propose that they can be classified into physics-based methods, empirical scoring functions, knowledge-based potentials, and descriptor-based scoring functions. We also outline the major difference and connections between different categories of scoring functions.
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