Empirical Scoring Functions for Structure-Based Virtual Screening: Applications, Critical Aspects, and Challenges.
Empirical Scoring Functions for Structure-Based Virtual Screening: Applications, Critical Aspects, and Challenges.
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DOI：
10.3389/fphar.2018.01089
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发表时间：
2018
影响因子：
5.6
通讯作者：
Dardenne LE
中科院分区：
文献类型：
--
作者：
Guedes IA;Pereira FSS;Dardenne LE
基于结构的虚拟筛选（VS）是一种广泛使用的方法，其在从大规模分子对接实验设计新的先导化合物中采用感兴趣的靶标的三维结构的知识。通过预测目标靶点结合位点内小分子的结合模式和亲和力，可以了解与结合过程相关的重要性质。经验评分函数被广泛用于姿势和亲和力预测。虽然姿态预测具有令人满意的准确性，但结合亲和力的正确预测仍然是一项具有挑战性的任务，并且对于基于结构的VS实验的成功至关重要。在不同的前沿有几项努力，以开发更复杂和准确的模型，用于过滤和排名大型化合物库。本文将涵盖一些最近的成功应用和方法的进展，包括探索配体熵和溶剂效应的策略，复杂的机器学习技术的训练，以及量子力学的使用。特别强调的是，将给予关键方面的讨论和更准确的经验评分功能的发展进一步的方向。
Structure-based virtual screening (VS) is a widely used approach that employs the knowledge of the three-dimensional structure of the target of interest in the design of new lead compounds from large-scale molecular docking experiments. Through the prediction of the binding mode and affinity of a small molecule within the binding site of the target of interest, it is possible to understand important properties related to the binding process. Empirical scoring functions are widely used for pose and affinity prediction. Although pose prediction is performed with satisfactory accuracy, the correct prediction of binding affinity is still a challenging task and crucial for the success of structure-based VS experiments. There are several efforts in distinct fronts to develop even more sophisticated and accurate models for filtering and ranking large libraries of compounds. This paper will cover some recent successful applications and methodological advances, including strategies to explore the ligand entropy and solvent effects, training with sophisticated machine-learning techniques, and the use of quantum mechanics. Particular emphasis will be given to the discussion of critical aspects and further directions for the development of more accurate empirical scoring functions.
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