Pharmacoinformatics has become increasingly effective at identifying statistical relationships among molecular structure, biological measurements, and pharmaceutical outcomes, yet prediction alone does not establish what would happen if a compound, target, dose, pathway, or treatment policy were deliberately changed. This article develops an original, non-empirical causal-modelling framework for moving from molecular correlation toward mechanistic intervention in drug discovery, safety assessment, and therapeutic translation. The framework begins with explicit specification of variables, feasible interventions, comparators, outcomes, target systems, time horizons, and causal estimands. It then links causal-graph construction, molecular representation, biological context, identification analysis, counterfactual estimation, mechanistic interpretation, and claim-bounded validation. QSAR, network models, omics representations, and mechanistic models are positioned as complementary analytical modules rather than substitutes for causal identification. Perturbational experiments, external validation, temporal and chemical-space stress tests, negative controls, mediation analysis, and transport assessment are treated as distinct evidentiary requirements aligned with the intended claim. The proposed architecture also separates predictive uncertainty from uncertainty about causal structure, unmeasured confounding, intervention consistency, positivity, measurement error, and model transportability. Its central contribution is a gated causal-pharmacoinformatics logic in which no single predictive, explanatory, mechanistic, or validation component is sufficient by itself. The framework is intended to organize hypothesis formation, model design, evidence integration, and decision boundaries. It does not constitute an empirically validated model, clinical recommendation, regulatory standard, or deployment-ready system and requires domain-specific computational, experimental, translational, and implementation evaluation.
IJPRAS
