Monitoring the Progression of Structure–Activity Relationship Information during Lead Optimization

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• 作者：Veerabahu Shanmugasundaram ; Liying Zhang ; Shilva Kayastha ; Antonio de la Vega de León ; Dilyana Dimova ; Jürgen Bajorath
• 刊名：Journal of Medicinal Chemistry
• 出版年：2016
• 出版时间：May 12, 2016
• 年：2016
• 卷：59
• 期：9
• 页码：4235-4244
• 全文大小：530K
• 年卷期：0
• ISSN：1520-4804

文摘

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.