Design, synthesis and experimental validation of novel potential chemopreventive agents using random forest and support vector machine binary classifiers.

ABSTRACT: Compared to the current knowledge on cancer chemotherapeutic agents, only limited information is available on the ability of organic compounds, such as drugs and/or natural products, to prevent or delay the onset of cancer. In order to evaluate chemical chemopreventive potentials and design novel chemopreventive agents with low to no toxicity, we developed predictive computational models for chemopreventive agents in this study. First, we curated a database containing over 400 organic compounds with known chemoprevention activities. Based on this database, various random forest and support vector machine binary classifiers were developed. All of the resulting models were validated by cross validation procedures. Then, the validated models were applied to virtually screen a chemical library

SUBMITTER: Sprague B 

PROVIDER: S-EPMC5600879 | biostudies-literature | 2014 Jun

REPOSITORIES: biostudies-literature