Drug discovery is a molecular search task with a complex objective: modify the function of a complex biological system to interrupt disease processes. Conventionally, it is a costly, high failure-rate process – with molecular candidates clearing preclinical safety and efficacy hurdles only to fail upon delivery to humans. This happens partly because early screens in the discovery pipeline fall short of capturing the ultimate therapeutic value of new molecular candidates. For a molecule to become a successful drug, it should: (1) bind to a desired target protein; (2) be deliverable from a desired site of administration (oral, intravenous, etc.) to the physiological site of activity, with sufficient concentration for a sufficient duration of time; and (3) promote the desired pharmacological effect without causing unwanted toxicity. The chemical space that meets one of these objectives likely requires compromises in another, as binding, delivery, and activity depend on coupled and dynamic biophysical and biochemical interactions. To help improve the success rate of drug discovery, we should ideally look at design through the lens of human physiology. Quantitative systems pharmacology models could offer the molecule-to-therapeutic-outcome mapping required to inform AI-driven drug design. However, these models present multiple challenges – from the complexity of the biological pathways driving disease processes to the knowledge gaps limiting model construction and parameterization, to the challenges presented by data limitations and the relative computational expense of mechanistic systems models. Through this collaborative research effort, we aim to enable physiology-informed, AI-driven design of new therapeutics.