Prediction of toxicity and synthesizability

One of the increasingly important component of modern drug discovery is the prediction of the toxicity of drug candidates. In collaboration with Supratik Mukhopadhyay and Hsiao-Chun Wu, we developed eToxPred, a new approach to reliably estimate the toxicity and synthetic accessibility of small organic compounds. eToxPred employs machine learning algorithms trained on molecular fingerprints to evaluate drug candidates. Encouragingly, it predicts the synthetic accessibility with the mean square error of only 4% and the toxicity with the accuracy of as high as 72%. eToxPred is a valuable tool that can be employed at the outset of drug discovery to filter out those drug candidates that are potentially toxic or would be difficult to synthesize.