Abstract
BackgroundThe efficiency of drug development defined as a number of successfully launched new pharmaceuticals normalized by financial investments has significantly declined. Nonetheless, recent advances in high-throughput experimental techniques and computational modeling promise reductions in the costs and development times required to bring new drugs to market. The prediction of toxicity of drug candidates is one of the important components of modern drug discovery.ResultsIn this work, we describe 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. The performance is assessed against multiple datasets containing known drugs, potentially hazardous chemicals, natural products, and synthetic bioactive compounds. Encouragingly, eToxPred predicts the synthetic accessibility with the mean square error of only 4% and the toxicity with the accuracy of as high as 72%.ConclusionseToxPred can be incorporated into protocols to construct custom libraries for virtual screening in order to filter out those drug candidates that are potentially toxic or would be difficult to synthesize. It is freely available as a stand-alone software at https://github.com/pulimeng/etoxpred.
Highlights
The efficiency of drug development defined as a number of successfully launched new pharmaceuticals normalized by financial investments has significantly declined
The datasets used to train and validate the SAscore predictor, including Food and Drug Administration (FDA)-approved, DUD-E-active, Nuclei of Bioassays (NuBBE), and Universal Natural Products Database (UNPD) datasets, are highly skewed, i.e., SAscore values are non-uniformly distributed over the 1–10 range
A pre-processing is needed to balance the dataset for a better performance of the SAscore predictor
Summary
We describe 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. We describe 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. EToxPred predicts the synthetic accessibility with the mean square error of only 4% and the toxicity with the accuracy of as high as 72%. Conclusions: eToxPred can be incorporated into protocols to construct custom libraries for virtual screening in order to filter out those drug candidates that are potentially toxic or would be difficult to synthesize. It is freely available as a stand-alone software at https://github.com/pulimeng/etoxpred
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