Generative modeling has emerged as a powerful tool for molecular design and structure prediction, offering the ability for molecular discovery. However, challenges such as synthetic feasibility, novelty, diversity of generated molecules, and generalization remain critical for real-world applications, particularly in drug discovery. In this presentation, we provide a comprehensive overview of state-of-the-art generative models, including graph-based methods, generative flow networks, and diffusion methods, all aimed at addressing these challenges. First, we focus on strategies that improve molecular structural optimzation using geometric deep learning methods. Second, we show how generative modeling can be applied to design novel molecules with desired properties such as drug potency, binding affinities to a specific target protein. Third, we will consider synthesizability of generated molecules by incorporating chemical reaction templates, enabling the generation of novel compounds that are not only drug-like but also synthetically accessible. Moreover, advanced sampling techniques and adaptive learning allow these models to explore diverse molecular structures, including those composed of previously unseen building blocks, while optimizing for key properties such as binding affinity and drug-likeness. Through case studies in drug design and broader molecular applications, we demonstrate how these generative modeling can help accelerate molecular discovery, offering a pathway to more practical and innovative solutions across diverse chemistry domains.
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