Ligandcentric

Ligand-centric is a term used in pharmacology, chemoinformatics, and drug discovery to describe approaches that place ligands (typically small molecules) at the center of the analysis of biological activity and target interactions. In ligand-centric frameworks, known ligand–target associations are used to infer new interactions, prioritize compounds, or explore polypharmacology, rather than starting from a predefined list of targets and screening for ligands against each target. This contrasts with target-centric approaches, which begin with a protein or receptor as the primary unit of analysis.

Common ligand-centric methods include ligand-based virtual screening (LBVS), quantitative structure–activity relationship (QSAR) modeling, and chemical similarity searching. These rely on chemical information and existing activity data rather than requiring experimental structures of all targets. Chemogenomics and ligand–target networks are used to propagate knowledge from known ligands to related targets and to map potential polypharmacology.

Applications include drug repurposing, identification of off-target effects, and prioritization of compounds for experimental testing. Ligand-centric analyses support clustering of ligands by activity profiles across multiple targets, which can suggest new therapeutic hypotheses.

Limitations include dependence on existing, high-quality ligand–target data and biases toward well-studied molecules. Predictions for novel chemotypes may be less reliable, and the inferred networks can be difficult to interpret. Data integration challenges and inconsistent assay terminology can affect accuracy.