Machine learning has become a central tool in molecular design, enabling the generation and optimization of candidate therapeutics across vast chemical spaces; however, real-world drug development is governed by multiple practical constraints that are rarely addressed jointly in computational models. Beyond optimizing chemical and pharmacological properties, viable molecules must avoid intellectual property conflicts, exhibit tissue-specific biological activity, and remain relevant when translating from preclinical animal models to humans—constraints that, if ignored, often yield candidates that are computationally promising yet infeasible in practice.
In this work, we develop machine learning frameworks that explicitly incorporate these legal, biological, and translational constraints into molecular design and evaluation, demonstrating that constraint-aware approaches produce candidates that are more realistic and better aligned with the requirements of real-world drug discovery.
