Shanmugasundaram Veerabahu, Zhang Liying, Kayastha Shilva, de la Vega de León Antonio, Dimova Dilyana, Bajorath Jürgen
Center of Chemistry Innovation & Excellence, WorldWide Medicinal Chemistry, Pfizer PharmaTherapeutics Research & Development , Eastern Point Road, Groton, Connecticut 06340, United States.
Computational Sciences CoE, WorldWide Medicinal Chemistry, Pfizer PharmaTherapeutics Research & Development , 610 Main Street, Cambridge, Massachusetts 06340, United States.
J Med Chem. 2016 May 12;59(9):4235-44. doi: 10.1021/acs.jmedchem.5b01428. Epub 2015 Nov 16.
Lead optimization (LO) in medicinal chemistry is largely driven by hypotheses and depends on the ingenuity, experience, and intuition of medicinal chemists, focusing on the key question of which compound should be made next. It is essentially impossible to predict whether an LO project might ultimately be successful, and it is also very difficult to estimate when a sufficient number of compounds has been evaluated to judge the odds of a project. Given the subjective nature of LO decisions and the inherent optimism of project teams, very few attempts have been made to systematically evaluate project progression. Herein, we introduce a computational framework to follow the evolution of structure-activity relationship (SAR) information over a time course. The approach is based on the use of SAR matrix data structures as a diagnostic tool and enables graphical analysis of SAR redundancy and project progression. This framework should help the process of making decisions in close-in analogue work.
药物化学中的先导优化（LO）很大程度上由假设驱动，并且依赖于药物化学家的独创性、经验和直觉，其关注的关键问题是接下来应该合成哪种化合物。预测一个LO项目最终是否会成功基本上是不可能的，而且也很难估计何时已经评估了足够数量的化合物来判断一个项目成功的可能性。鉴于LO决策的主观性以及项目团队固有的乐观态度，很少有人尝试系统地评估项目进展。在此，我们引入一个计算框架，以跟踪结构-活性关系（SAR）信息在一段时间内的演变。该方法基于使用SAR矩阵数据结构作为诊断工具，并能够对SAR冗余和项目进展进行图形分析。这个框架应该有助于在类似物研究工作中做出决策的过程。
