The discovery of new drugs has important implications for human health. Traditional
methods for drug discovery rely on experiments to optimize the structure of lead molecules …

Computational techniques, including virtual screening, de novo design, and generative
models, play an increasing role in expediting DMTA cycles for modern molecular discovery …

Designing ligand molecules that bind to specific protein binding sites is a fundamental
problem in structure-based drug design. Although deep generative models and geometric …

Deep generative modeling has a strong potential to accelerate drug design. However,
existing generative models often face challenges in generalization due to limited data …

Computer-driven molecular design combines the principles of chemistry, physics, and
artificial intelligence to identify chemical compounds with tailored properties. While quantum …

Traditional molecular string representations, such as SMILES, often pose challenges for AI-
driven molecular design due to their non-sequential depiction of molecular substructures. To …

Despite the significant potential of generative models, low synthesizability of many
generated molecules limits their real-world applications. In response to this issue, we …

Generative models in drug discovery have recently gained attention as efficient alternatives
to brute-force virtual screening. However, most existing models do not account for …

Photoswitchable molecules display two or more isomeric forms that may be accessed using
light. Separating the electronic absorption bands of these isomers is key to selectively …

Constrained synthesizability is an unaddressed challenge in generative molecular design.
In particular, designing molecules satisfying multi-parameter optimization objectives, while …