Numerous generative models have been developed to aid in the design of small molecule drugs. However, these models typically generate thousands of suggestions, leaving it unclear how to select which molecules to synthesize next. My research focuses on bridging the gap between generative models and chemists; I aim to enhance the usability and applicability of these models in drug discovery pipelines. To achieve this, I will explore hypothesis generation, hypothesis summarization, and model interpretability, combining my expertise in medicinal chemistry with machine learning techniques.