论文标题
SHREC 2022：蛋白质 - 配体结合位点识别
SHREC 2022: Protein-ligand binding site recognition
论文作者
论文摘要
本文介绍了参加了SHREC 2022蛋白质结合位点识别竞赛的方法。蛋白质 - 配体结合区域的预测是计算生物物理学和结构生物学中的活跃研究领域，并且在分子对接和药物设计中起着相关的作用。比赛的目的是评估计算方法在基于其几何结构基于蛋白质中识别配体结合位点的有效性。根据两个评估分数分析了分割算法的性能，这些评估得分描述了推定口袋接触配体并查明正确结合区域的能力。尽管有一些方法表现出色，但我们表明，与数据驱动算法相对于数据驱动的算法，简单的非计算学习方法仍然非常有竞争力。通常，口袋检测的任务仍然是一个具有挑战性的学习问题，由于缺乏负面示例（数据不平衡问题），这遇到了内在困难。
This paper presents the methods that have participated in the SHREC 2022 contest on protein-ligand binding site recognition. The prediction of protein-ligand binding regions is an active research domain in computational biophysics and structural biology and plays a relevant role for molecular docking and drug design. The goal of the contest is to assess the effectiveness of computational methods in recognizing ligand binding sites in a protein based on its geometrical structure. Performances of the segmentation algorithms are analyzed according to two evaluation scores describing the capacity of a putative pocket to contact a ligand and to pinpoint the correct binding region. Despite some methods perform remarkably, we show that simple non-machine-learning approaches remain very competitive against data-driven algorithms. In general, the task of pocket detection remains a challenging learning problem which suffers of intrinsic difficulties due to the lack of negative examples (data imbalance problem).
