Computer-aided Pattern Scoring (C@PS): A Novel Cheminformatic Workflow to Predict Ligands with Rare Modes-of-action

Abstract: The identification, establishment, and exploration of potential pharmacological drug targets are major steps of the drug development pipeline. Target validation requires diverse chemical tools that come with a spectrum of functionality, e.g., inhibitors, activators, and other modulators. Particularly tools with rare modes-of-action allow for a proper kinetic and functional characterization of the targets-of-interest (e.g., channels, enzymes, receptors, or transporters). Despite, functional innovation is a prime criterion for patentability and commercial exploitation, which may lead to therapeutic benefit. Unfortunately, data on new, and thus, undruggable or barely druggable targets are scarce and mostly available for mainstream modes-of-action only (e.g., inhibition). Here we present a novel cheminformatic workflow – computer-aided pattern scoring (C@PS) – which was specifically designed to project its prediction capabilities into an uncharted domain of applicability. Scientific Contribution: The presented workflow tackles, for the first time, the challenge of data scarcity particularly focusing rare modes-of-action. In addition, the workflow and associated dataset provide new standards in the definition and application of criteria to rationalize drug candidate selection addressing an important gap in cheminformatics and computational chemistry