Design synthesis and experimental validation of n

Authors: Brienne Sprague Qian Shi Marlene T Kim Liying Zhang Alexander Sedykh Eiichiro Ichiishi Harukuni Tokuda KuoHsiung Lee Hao Zhu

Publish Date: 2014/05/20

Volume: 28, Issue: 6, Pages: 631-646

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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 containing around 23000 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

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