Abstract
This study investigates the impact of incorporating stereochemical information, a crucial aspect of computational drug discovery and materials design, in molecular generative modeling. We present a detailed comparison of stereochemistry-aware and conventionally stereochemistry-unaware string-based generative approaches, utilizing both genetic algorithms and reinforcement learning-based techniques. To evaluate these models, we introduce novel benchmarks specifically designed to assess the importance of stereochemistry-aware generative modeling. Our results demonstrate that stereochemistry-aware models generally perform on par with or surpass conventional algorithms across various stereochemistry-sensitive tasks. However, we also observe that in scenarios where stereochemistry plays a less critical role, stereochemistry-aware models may face challenges due to the increased complexity of the chemical space they must navigate. This work provides insights into the trade-offs involved in incorporating stereochemical information in molecular generative models and offers guidance for selecting appropriate approaches based on specific application requirements.