A mutual information-based activity labeling and scoring approach to reverse fingerprint (RF) analysis is introduced [1]. Starting with a series of ligands, only described in 2D, and each with known biological activity, the RF approach allows for the elucidation of important molecular structural motifs (as related to activity) along with the determination of a consensus pharmacophore; the consensus pharmacophore includes both atom-centered and projected features.<br/><br/>It is possible to combine this information, along with ligand shape and excluded volume features (derived from conformational sampling), to derive a "virtual pocket". This virtual pocket represents a hypothesis for how the ligands may orient in a bound state, and for what the binding pocket might look like around the bound ligands. This virtual pocket can be used for both retrospective analysis (to rationalize known structure-activity relationships), or converted to a 3D pharmacophore query that can be used to search databases for new active molecules in a vHTS context.<br/><br/>Following a concise description of the virtual pockets methodology described above, a series of illustrative examples will be presented, describing the performance of the vHTS experiments against a variety of pharmaceutically relevant targets.

Reverse fingerprints, virtual pockets, and high-throughput virtual screening. Oh my!

Fall 2022 abstracts, posters, and presentations will launch in mid-September.

ACS Fall 2022

The official archive and repository for all ACS Meetings & Expositions abstracts and select posters or presentations.