Hao Zhu

TL;DR: The history of machine learning is summarized and insight into recently developed deep learning approaches and their applications in rational drug discovery are provided to help guide early-stage drug design and discovery in the current big data era.

TL;DR: It is shown that incorrect validation of a model may result in the wrong estimation of its performance and suggested how this problem could be circumvented and the distance to model metric could also be used to augment mechanistic QSAR models by estimating their prediction errors.

TL;DR: An international virtual collaboratory consisting of six independent groups with shared interests in computational chemical toxicology develops 15 different types of QSAR models of aquatic toxicity and finds that consensus models afford higher prediction accuracy for the external validation data sets with the highest space coverage as compared to individual constituent models.

TL;DR: The results of this study indicate that models based on rational division methods generate better statistical results for the test sets than modelsbased on random division, but the predictive power of both types of models are comparable.

TL;DR: The validated consensus LD(50) models developed in this study can be used as reliable computational predictors of in vivo acute toxicity in rats caused by oral exposure to chemicals.