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.

Connecting chemistry and biology through molecular descriptors.

Through the representation of small molecule structures as numerical descriptors and the exploitation of the similarity principle, chemoinformatics has made paramount contributions to drug discovery, from unveiling mechanisms of action and repurposing approved drugs to de novo crafting of molecules with desired properties and tailored targets. Yet, the inherent complexity of biological systems has fostered the implementation of large-scale experimental screenings seeking a deeper understanding of the targeted proteins, the disrupted biological processes and the systemic responses of cells to chemical perturbations. After this wealth of data, a new generation of data-driven descriptors has arisen providing a rich portrait of small molecule characteristics that goes beyond chemical properties.