S. Joshua Swamidass

TL;DR: It is found that deep learning has yet to revolutionize biomedicine or definitively resolve any of the most pressing challenges in the field, but promising advances have been made on the prior state of the art.

TL;DR: This review of recent advancements in the critical areas of computational drug repositioning from multiple aspects shows potential opportunities and use-cases, including a few target areas such as cancers.

TL;DR: The results reveal the immense potential for translating the dispersed expertise in biological assays involving human pathogens into drug discovery starting points, by providing open access to new families of molecules, and emphasize how a small additional investment made to help acquire and distribute compounds, and sharing the data, can catalyze drug discovery for dozens of different indications.

TL;DR: The Medicines for Malaria Venture Malaria Box as mentioned in this paper is a collection of over 400 compounds representing families of structures identified in phenotypic screens of pharmaceutical and academic libraries against the Plasmodium falciparum malaria parasite.

TL;DR: Several new classes of kernels for small molecules using their 1D, 2D and 3D representations are developed and applied to problems of classification and prediction of mutagenicity, toxicity and anti-cancer activity on three publicly available datasets.