Comprehensive detection and characterization of human druggable pockets through binding site descriptors.

Druggable pockets are protein regions that have the ability to bind organic small molecules, and their characterization is essential in target-based drug discovery. However, deriving pocket descriptors is challenging and existing strategies are often limited in applicability. We introduce PocketVec, an approach to generate pocket descriptors via inverse virtual screening of lead-like molecules.

Stereochemically-aware bioactivity descriptors for uncharacterized chemical compounds.

Stereochemistry plays a fundamental role in pharmacology. Here, we systematically investigate the relationship between stereoisomerism and bioactivity on over 1 M compounds, finding that a very significant fraction (~ 40%) of spatial isomer pairs show, to some extent, distinct bioactivities. We then use the 3D representation of these molecules to train a collection of deep neural networks (Signaturizers3D) to generate bioactivity descriptors associated to small molecules, that capture their effects at increasing levels of biological complexity (i.

Global analysis of suppressor mutations that rescue human genetic defects.

Background: Genetic suppression occurs when the deleterious effects of a primary "query" mutation, such as a disease-causing mutation, are rescued by a suppressor mutation elsewhere in the genome.

Methods: To capture existing knowledge on suppression relationships between human genes, we examined 2,400 published papers for potential interactions identified through either genetic modification of cultured human cells or through association studies in patients.

Results: The resulting network encompassed 476 unique suppression interactions covering a wide spectrum of diseases and biological functions.