Stereochemistry-aware string-based molecular generation

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.

Keywords: drug design; generative modeling; machine learning; molecular generation; stereochemistry.

Fig. 1.

Example of isomeric molecule encoded with SMILES, SELFIES, and GroupSELFIES.

Fig. 2.

Optimization traces for rediscovery tasks. The cumulative top-1 similarity score to the target molecule as a function of generation of optimization. Shaded regions indicate the 95% CI. The dashed line is the best score found in the starting dataset.

Fig. 3.

Structures of proteins with native ligands. The structures are from the Protein Data Bank (73). The native ligand in the binding pocket is shown inside a bounding box.

Fig. 4.

Optimization traces for docking tasks. The cumulative top-1 docking score for protein targets as a function of generation of optimization. Shaded regions indicate the 95% CI. The dashed line is the best score found in the starting dataset.

Fig. 5.

Optimization traces for CD task. The cumulative top-1 CD peak score as a function of generation of optimization. Shaded regions indicate the 95% CI. The dashed line is the best score found in the starting dataset.