Multiple approaches to quantitative structure-activity relationship (QSAR) modeling using various statistical or machine learning techniques and different types of chemical descriptors have been developed over the years. Oftentimes models are used in consensus to make more accurate predictions at the expense of model interpretation. We propose a simple, fast, and reliable method termed Multi-Descriptor Read Across (MuDRA) for developing both accurate and interpretable models. The method is conceptually related to the well-known kNN approach but uses different types of chemical descriptors simultaneously for similarity assessment. To benchmark the new method, we have built MuDRA models for six different end points (Ames mutagenicity, aquatic toxicity, hepatotoxicity, hERG liability, skin sensitization, and endocrine disruption) and compared the results with those generated with conventional consensus QSAR modeling. We find that models built with MuDRA show consistently high external accuracy similar to that of conventional QSAR models. However, MuDRA models excel in terms of transparency, interpretability, and computational efficiency. We posit that due to its methodological simplicity and reliable predictive accuracy, MuDRA provides a powerful alternative to a much more complex consensus QSAR modeling. MuDRA is implemented and freely available at the Chembench web portal ( https://chembench.mml.unc.edu/mudra ).

Elucidation of the mechanistic relationships between drugs, their targets, and diseases is at the core of modern drug discovery research. Thousands of studies relevant to the drug-target-disease (DTD) triangle have been published and annotated in the Medline/PubMed database. Mining this database affords rapid identification of all published studies that confirm connections between vertices of this triangle or enable new inferences of such connections. To this end, we describe the development of Chemotext, a publicly-available Web server that mines the entire compendium of published literature in PubMed annotated by Medline Subject Heading (MeSH) terms. The goal of Chemotext is to identify all known drug-target-disease relationships and infer missing links between vertices of the DTD triangle. As a proof-of-concept, we show that Chemotext could be instrumental in generating new drug repurposing hypotheses or annotating clinical outcomes pathways for known drugs. The Chemotext Web server is freely-available at http://chemotext.mml.unc.edu.