Bioisosteric replacement is a fundamental design principle in medicinal chemistry, yet identifying substituents that preserve biological activity remains largely empirical. A model-oriented approach frames this challenge as a paired molecular learning problem rather than a simple similarity search. Existing computational methods often rely on physicochemical similarity, historical replacement tables, or local transformation statistics, which may fail to fully leverage paired activity evidence from matched molecular pairs and binding assays. To address this, a Siamese neural network can be defined to learn from matched molecular pairs annotated with activity data, predicting whether a structural modification could serve as a bioisosteric replacement that maintains target affinity. In this twin-network architecture, shared weights encode both the original and modified molecules into comparable latent representations, with a contrastive or classification loss applied to the joint representation based on curated assay-derived activity labels. Conceptually, the model would distinguish activity-preserving substitutions from detrimental modifications while accommodating molecular graph or fingerprint inputs, and it could also highlight molecular features that inform replacement decisions. By providing a data-driven and interpretable framework, the Siamese approach has the potential to accelerate lead optimization by prioritizing high-probability substitutions for chemical synthesis and biological testing.