Design, synthesis and experimental validation of novel potential chemopreventive agents using random forest and support vector machine binary classifiers.
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 containing around 23,000 natural products and derivatives. We selected a list of 148 novel chemopreventive compounds based on the consensus prediction of all validated models. We further analyzed the predicted active compounds by their ease of organic synthesis. Finally, 18 compounds were synthesized and experimentally validated for their chemopreventive activity. The experimental validation results paralleled the cross validation results, demonstrating the utility of the developed models. The predictive models developed in this study can be applied to virtually screen other chemical libraries to identify novel lead compounds for the chemoprevention of cancers.
Department of Chemistry, Rutgers University, 315 Penn St., Camden, NJ, 08102, USA.
Brienne
B
Sprague
Qian
Q
Shi
Marlene T
MT
Kim
Liying
L
Zhang
Alexander
A
Sedykh
Eiichiro
E
Ichiishi
Harukuni
H
Tokuda
Kuo-Hsiung
KH
Lee
Hao
H
Zhu
D016588:Anticarcinogenic Agents / Q000138:chemical synthesis / Q000737:chemistry* / Q000494:pharmacology*; D001688:Biological Products / Q000138:chemical synthesis / Q000737:chemistry / Q000494:pharmacology; D017076:Computer-Aided Design; D062313:Databases, Pharmaceutical; D015195:Drug Design; D006801:Humans; D008954:Models, Biological; D009369:Neoplasms / Q000188:drug therapy; D021281:Quantitative Structure-Activity Relationship; D060388:Support Vector Machine
D016588:Anticarcinogenic Agents; D001688:Biological Products