Ligand-agnostic off-target site prediction for early toxicity screening by leveraging point cloud-based protein cavity analysis.

Adverse drug reactions caused by molecules binding unintended targets are a major concern in drug discovery. Early identification of such interactions during drug design and development minimizes risks and enhances therapeutic efficacy. While experimental approaches are time-consuming and resource-intensive, in silico virtual screening offers a faster, cost-effective strategy to anticipate off-target effects early in drug design. Here, we present a point cloud-based virtual screening method, designed to perform off-target identification based solely on the chemical composition of the primary drug binding site. By screening multidimensional point clouds representing all potential human binding sites, this approach identifies alternative targets based on shape and physicochemical properties. Notably, it operates independently of the protein's overall structure or sequence. This focus on the binding site broadens the search space to structurally unrelated proteins and enables screening without requiring lead molecule information. Using an experimentally validated test set, we demonstrated the method's ability to identify alternative targets across protein families and predict drug promiscuity, achieving a Top-10 recall of 24% for validated, strongly modulated targets. While ligand-agnostic sequence- (BLASTP) and structure-based (Foldseek) methods show higher overall recall, point cloud-based screening uniquely recovers 13.9% (79.4% of its total recovered hits) of known off-targets with <30% sequence identity at Top-100, which are missed by both BLASTP and Foldseek. Beyond known targets, we found several high-ranking candidates not yet annotated as drug-targets but showing notable cavity similarity despite being structurally unrelated, which we present as testable hypotheses for follow-up.