Graph neural processes for molecules: an evaluation on docking scores and strategies to improve generalization.

Neural processes (NPs) are models for meta-learning which output uncertainty estimates. So far, most studies of NPs have focused on low-dimensional datasets of highly-correlated tasks. While these homogeneous datasets are useful for benchmarking, they may not be representative of realistic transfer learning.

Insights into Drug Cardiotoxicity from Biological and Chemical Data: The First Public Classifiers for FDA DICTrank.

Drug-induced cardiotoxicity (DICT) is a major concern in drug development, accounting for 10-14% of postmarket withdrawals. In this study, we explored the capabilities of various chemical and biological data to predict cardiotoxicity, using the recently released Drug-Induced Cardiotoxicity Rank (DICTrank) dataset from the United States FDA. We analyzed a diverse set of data sources, including physicochemical properties, annotated mechanisms of action (MOA), Cell Painting, Gene Expression, and more, to identify indications of cardiotoxicity.

DOCKSTRING: Easy Molecular Docking Yields Better Benchmarks for Ligand Design.

The field of machine learning for drug discovery is witnessing an explosion of novel methods. These methods are often benchmarked on simple physicochemical properties such as solubility or general druglikeness, which can be readily computed. However, these properties are poor representatives of objective functions in drug design, mainly because they do not depend on the candidate compound's interaction with the target.

Applications of Artificial Intelligence in Drug Design: Opportunities and Challenges.

Artificial intelligence (AI) has undergone rapid development in recent years and has been successfully applied to real-world problems such as drug design. In this chapter, we review recent applications of AI to problems in drug design including virtual screening, computer-aided synthesis planning, and de novo molecule generation, with a focus on the limitations of the application of AI therein and opportunities for improvement. Furthermore, we discuss the broader challenges imposed by AI in translating theoretical practice to real-world drug design; including quantifying prediction uncertainty and explaining model behavior.

Characterization of Atg38 and NRBF2, a fifth subunit of the autophagic Vps34/PIK3C3 complex.

The phosphatidylinositol 3-kinase Vps34 is part of several protein complexes. The structural organization of heterotetrameric complexes is starting to emerge, but little is known about organization of additional accessory subunits that interact with these assemblies. Combining hydrogen-deuterium exchange mass spectrometry (HDX-MS), X-ray crystallography and electron microscopy (EM), we have characterized Atg38 and its human ortholog NRBF2, accessory components of complex I consisting of Vps15-Vps34-Vps30/Atg6-Atg14 (yeast) and PIK3R4/VPS15-PIK3C3/VPS34-BECN1/Beclin 1-ATG14 (human).