Supplementary data are available at Bioinformatics online.

Chemical absorption, distribution, metabolism, excretion, and toxicity (ADMET), play key roles in drug discovery and development.

BACKGROUND: Endocrine disrupting chemicals (EDCs) are xenobiotics that mimic the interaction of natural hormones and alter synthesis, transport, or metabolic pathways. The prospect of EDCs causing adverse health effects in humans and wildlife has led to the development of scientific and regulatory approaches for evaluating bioactivity. This need is being addressed using high-throughput screening (HTS) in vitro approaches and computational modeling. OBJECTIVES: In support of the Endocrine Disruptor Screening Program, the U.S. Environmental Protection Agency (EPA) led two worldwide consortiums to virtually screen chemicals for their potential estrogenic and androgenic activities. Here, we describe the Collaborative Modeling Project for Androgen Receptor Activity (CoMPARA) efforts, which follows the steps of the Collaborative Estrogen Receptor Activity Prediction Project (CERAPP).

During drug development, safety is always the most important issue, including a variety of toxicities and adverse drug effects, which should be evaluated in preclinical and clinical trial phases. This review article at first simply introduced the computational methods used in prediction of chemical toxicity for drug design, including machine learning methods and structural alerts. Machine learning methods have been widely applied in qualitative classification and quantitative regression studies, while structural alerts can be regarded as a complementary tool for lead optimization. The emphasis of this article was put on the recent progress of predictive models built for various toxicities. Available databases and web servers were also provided. Though the methods and models are very helpful for drug design, there are still some challenges and limitations to be improved for drug safety assessment in the future.

Plasma protein binding (PPB) is a significant pharmacokinetic property of compounds in drug discovery and design. Due to the high cost and time-consuming nature of experimental assays, in silico approaches have been developed to assess the binding profiles of chemicals. However, because of unambiguity and the lack of uniform experimental data, most available predictive models are far from satisfactory. In this study, an elaborately curated training set containing 967 diverse pharmaceuticals with plasma-protein-bound fractions (f ) was used to construct quantitative structure-activity relationship (QSAR) models by six machine learning algorithms with 26 molecular descriptors. Furthermore, we combined all of the individual learners to yield consensus prediction, marginally improving the accuracy of the consensus model. The model performance was estimated by tenfold cross validation and three external validation sets comprising 242 pharmaceutical, 397 industrial, and 231 newly designed chemicals, respectively. The models showed excellent performance for the entire test set, with mean absolute error (MAE) ranging from 0.126 to 0.178, demonstrating that our models could be used by a chemist when drawing a molecular structure from scratch. Meanwhile, structural descriptors contributing significantly to the predictive power of the models were related to the binding mechanisms, and the trend in terms of their effects on PPB can serve as guidance for the structural modification of chemicals. The applicability domain was also defined to distinguish favorable predictions from unfavorable predictions.