Department of Pharmaceutical Chemistry, University of California, San Francisco, California 94158, United States.
Schrödinger, Inc., 9868 Scranton Road, San Diego, California 92121, United States.
J Med Chem. 2024 Sep 26;67(18):16796-16806. doi: 10.1021/acs.jmedchem.4c01632. Epub 2024 Sep 10.
While large library docking has discovered potent ligands for multiple targets, as the libraries have grown the hit lists can become dominated by rare artifacts that cheat our scoring functions. Here, we investigate rescoring top-ranked docked molecules with orthogonal methods to identify these artifacts, exploring implicit solvent models and absolute binding free energy perturbation as cross-filters. In retrospective studies, this approach deprioritized high-ranking nonbinders for nine targets while leaving true ligands relatively unaffected. We tested the method against hits from docking against AmpC β-lactamase. We prioritized 128 high-ranking molecules for synthesis and testing, a mixture of 39 molecules flagged as likely cheaters and 89 that were plausible inhibitors. None of the predicted cheating compounds inhibited AmpC detectably, while 57% of the 89 plausible compounds did so. As our libraries continue to grow, deprioritizing docking artifacts by rescoring with orthogonal methods may find wide use.
虽然大型文库对接已经发现了多种靶标的有效配体，但随着文库的增长，命中列表可能会被欺骗我们评分函数的稀有伪影所主导。在这里，我们研究了用正交方法重新评分排名靠前的对接分子，以识别这些伪影，探索隐溶剂模型和绝对结合自由能扰动作为交叉筛选器。在回顾性研究中，这种方法将九个靶标中排名靠前的非结合物的优先级降低，而对真正的配体的影响相对较小。我们用对接 AmpCβ-内酰胺酶的方法来测试该方法。我们优先选择了 128 种高排名的分子进行合成和测试，其中包括 39 种可能是骗子的分子和 89 种可能是抑制剂的分子。预测的作弊化合物中没有一种能显著抑制 AmpC，而 89 种可能的化合物中有 57%能显著抑制 AmpC。随着我们的文库不断增长，通过用正交方法重新评分来优先考虑对接伪影可能会得到广泛应用。
