IPSJ Transactions on Bioinformatics
Online ISSN : 1882-6679
ISSN-L : 1882-6679
Predicting Strategies for Lead Optimization via Learning to Rank
Nobuaki YasuoKeisuke WatanabeHideto HaraKentaro RikimaruMasakazu Sekijima
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2018 Volume 11 Pages 41-47


Lead optimization is an essential step in drug discovery in which the chemical structures of compounds are modified to improve characteristics such as binding affinity, target selectivity, physicochemical properties, and toxicity. We present a concept for a computational compound optimization system that outputs optimized compounds from hit compounds by using previous lead optimization data from a pharmaceutical company. In this study, to predict the drug-likeness of compounds in the evaluation function of this system, we evaluated and compared the ability to correctly predict lead optimization strategies through learning to rank methods.

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© 2018 by the Information Processing Society of Japan
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