Abstract
In a previous Method Article, we have presented the 'Structure-Activity Relationship (SAR) Matrix' (SARM) approach. The SARM methodology is designed to systematically extract structurally related compound series from screening or chemical optimization data and organize these series and associated SAR information in matrices reminiscent of R-group tables. SARM calculations also yield many virtual candidate compounds that form a "chemical space envelope" around related series. To further extend the SARM approach, different methods are developed to predict the activity of virtual compounds. In this follow-up contribution, we describe an activity prediction method that derives conditional probabilities of activity from SARMs and report representative results of first prospective applications of this approach.
Highlights
In recent years, graphical methods have substantially expanded the medicinal chemistry repertoire for analyzing Structure-Activity Relationships (SARs)[1,2]
While we are currently unable to disclose the structures of active compounds, the prediction statistics and exemplary results we report for an actual drug discovery project should be helpful to put SAR Matrix (SARM)-based predictions into perspective, beyond computational benchmarking, and might spark the interest of practitioners in this field
Since details of the SARM methodology and matrix-based Quantitative SAR (QSAR) modeling have been presented in the accompanying article[6], we initially provide only brief summaries of these methods, followed by a detailed description of the conditional probability approach
Summary
Matrix’ method and matrix-derived conditional probabilities of activity [version 2; peer review: 4 approved]. Disha Gupta-Ostermann[1], Yoichiro Hirose[2], Takenao Odagami[2], Hiroyuki Kouji[2], Jürgen Bajorath 1.
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