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Follow-up: Prospective compound design using the ‘SAR Matrix’ method and matrix-derived conditional probabilities of activity

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 SA...

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Detalles Bibliográficos
Autores principales: Gupta-Ostermann, Disha, Hirose, Yoichiro, Odagami, Takenao, Kouji, Hiroyuki, Bajorath, Jürgen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: F1000Research 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4406192/
https://www.ncbi.nlm.nih.gov/pubmed/25949808
http://dx.doi.org/10.12688/f1000research.6271.2
Descripción
Sumario: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.