Efficient and valid large molecule generation via self-supervised generative models.

The realm of molecular design, particularly for large molecules, presents unique challenges and opportunities in drug discovery and materials science. Large molecule design is inherently more complex and less explored compared to designing small molecules, adding significant difficulty in generative modeling. We aim to establish strong baselines for better scalability, efficiency, and generative performance in this domain. We evaluate the scalability and performance of generative AI models, initially effective for small molecule design, in generating large molecules for potential drugs in gene-based therapies, immunotherapies, hormonal regulators, and targeted cancer therapies. Our findings indicate that computational strategies and model architectures designed for small molecules may not readily extend to large molecular structures. To address these limitations, we explore masked language modeling strategies alongside advanced tokenization methods, including Atom-Pair Encoding (APE), to enhance generative AI models. We probe how incorporating such strategies, particularly the APE tokenization method that explicitly captures structural and chemical characteristics, can significantly improve design capabilities for complex molecular structures. Overall, our results demonstrate both the potential and challenges of deep generative modeling for large molecules and how the proposed enhancements may bridge the gap in generating large molecules when novel discovery is the ultimate goal.