Predicting Strategies for Lead Optimization via Learning to Rank

Nobuaki Yasuo, Keisuke Watanabe, Hideto Hara, Kentaro Rikimaru, Masakazu Sekijima
2018 IPSJ Transactions on Bioinformatics  
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
more » ... function of this system, we evaluated and compared the ability to correctly predict lead optimization strategies through learning to rank methods.
doi:10.2197/ipsjtbio.11.41 fatcat:v3zysdnnzfhsbox6flczofdjaa