Abstract：
Computational target fishing plays an important role in target identification, particularly in drug discovery campaigns utilizing phenotypic screening. Numerous approaches exist to predict potential targets for a given ligand, but true targets may be inconsistently ranked. More advanced simulation methods may provide benefit in such cases by reranking these initial predictions. We evaluated the ability of binding pose metadynamics to improve the predicted rankings for three diverse ligands and their six true targets. Initial predictions using pharmacophore mapping showed no true targets ranked in the top 50 and two targets each ranked within the 50-100, 100-150, and 250-300 ranges respectively. Following binding pose metadynamics, ranking of true targets improved for four out of the six targets and included the highest ranked predictions overall, while rankings deteriorated for two targets. The revised rankings predicted two true targets ranked within the top 50, and one target each within the 50-100, 100-150, 150-200, and 200-250 ranges respectively. The findings of this study demonstrate that binding pose metadynamics may be of benefit in refining initial predictions from structure-based target fishing algorithms, thereby improving the efficiency of the target identification process in drug discovery efforts.
摘要：
计算目标捕捞在目标识别中起着重要作用,特别是在利用表型筛查的药物发现活动中。存在许多方法来预测给定配体的潜在靶标，但是真正的目标可能排名不一致。更先进的模拟方法可以通过重新排序这些初始预测来在这种情况下提供益处。我们评估了结合位元动力学的能力，以提高三个不同配体及其六个真实目标的预测排名。使用药效基团作图的初始预测显示没有排名在前50的真实靶标,并且两个靶标各自分别在50-100、100-150和250-300范围内排名。在绑定姿势元动力学之后，六个目标中的四个目标的真实目标排名得到了提高，并包括了整体排名最高的预测，而两个目标的排名下降。修订后的排名预测了排名前50名的两个真实目标，以及分别在50-100、100-150、150-200和200-250范围内的一个目标。这项研究的结果表明，约束位元动力学可能有助于从基于结构的目标钓鱼算法中完善初始预测，从而提高了药物发现工作中靶标识别过程的效率。
