Potency Is Only One Objective in the Computational Design of Medicines with Viable Pharmaceutical Futures Download PDF

Computational molecular design increasingly enables the generation, ranking, and optimization of chemical structures against predicted biological and physicochemical properties. However, many design systems continue to privilege potency or a closely related target-activity score as the dominant measure of molecular quality. This emphasis creates a conceptual mismatch between optimization success and pharmaceutical viability because a highly potent structure may remain unsuitable owing to inadequate exposure, undesirable target interactions, safety liabilities, synthetic difficulty, formulation constraints, or weak alignment with the intended therapeutic context. This Original Multiobjective Design Theory Article develops a proposed account of medicine design in which computational optimization is directed toward viable pharmaceutical futures rather than isolated property maxima. The article conceptualizes candidate quality as a position within a multidimensional objective space comprising efficacy, selectivity, exposure, safety, and manufacturability, with each domain represented by imperfect evidence and subject to programme-specific constraints. It further distinguishes compensatory trade-offs from non-compensatory failures, proposes a viable pharmaceutical region within which Pareto reasoning becomes scientifically meaningful, and argues that preferences should change as evidence accumulates across discovery stages. The central contribution is therefore not a universal scoring formula but a theory for organizing objectives, constraints, uncertainty, and preference specification around pharmaceutical decisions. The proposed account remains conceptual: it does not establish validated thresholds, universally correct objective weights, prospective workflow superiority, clinical utility, regulatory acceptability, or deployment readiness. Its principal implication is that computational medicine design should be evaluated by the quality and transparency of the candidate sets and decisions it supports, rather than by potency improvement or aggregate benchmark performance alone.