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 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 chemo-prevention of cancers.

Big Data and Artificial Intelligence Modeling for Drug Discovery

“…Instead of using all available descriptors, descriptor selection was integrated into the modeling procedure, e.g., the genetic algorithm (74,75) and simulated annealing (76). Instead of using linear regression, new machine learning approaches, which were developed based on nonlinear modeling algorithms such as k-nearest neighbors (77), support vector machines (78), and random forest (79,80), were used frequently in modeling studies from the 1990s to the 2000s. In the same period, model validation was emphasized and treated as a must-have component of modeling (81).…”

Multiple Machine Learning Comparisons of HIV Cell-based and Reverse Transcriptase Data Sets

“…A consensus model can be created by leveraging the outputs of several individual QSAR models into one prediction. Previous work has demonstrated these models to have comparable or superior predictive capabilities compared to their individual components. − In this work, a consensus model prediction was created by averaging the predicted classification of compound across all machine learning algorithms tested. Therefore, predictions by the consensus model were based on agreement of each algorithm’s predicted classification and ranged from 0 to 1.…”

Prediction Model of Clearance by a Novel Quantitative Structure–Activity Relationship Approach, Combination DeepSnap-Deep Learning and Conventional Machine Learning

“…Combi QSAR is a method for constructing models by combining molecular descriptors in multiple commercial software and multiple algorithms ( k -nearest neighbor, support vector machine, decision trees, and random forest). 25 − 28 It was reported that high prediction accuracy and stable results were obtained by using these methods. 25 − 28 Furthermore, Brownfield et al .…”

“… 25 − 28 It was reported that high prediction accuracy and stable results were obtained by using these methods. 25 − 28 Furthermore, Brownfield et al . proposed a prediction method that combined three class systems by a fusion process as a consensus classification.…”