Unraveling the Potential of Diffusion Models in Small Molecule Generation
By: Peining Zhang , Daniel Baker , Minghu Song and more
Potential Business Impact:
Helps invent new medicines faster.
Generative AI presents chemists with novel ideas for drug design and facilitates the exploration of vast chemical spaces. Diffusion models (DMs), an emerging tool, have recently attracted great attention in drug R\&D. This paper comprehensively reviews the latest advancements and applications of DMs in molecular generation. It begins by introducing the theoretical principles of DMs. Subsequently, it categorizes various DM-based molecular generation methods according to their mathematical and chemical applications. The review further examines the performance of these models on benchmark datasets, with a particular focus on comparing the generation performance of existing 3D methods. Finally, it concludes by emphasizing current challenges and suggesting future research directions to fully exploit the potential of DMs in drug discovery.
Similar Papers
Diffusion Models for Molecules: A Survey of Methods and Tasks
Machine Learning (CS)
Helps invent new medicines and materials faster.
From thermodynamics to protein design: Diffusion models for biomolecule generation towards autonomous protein engineering
Quantitative Methods
Designs new proteins that work like real ones.
Diffusion Models for Future Networks and Communications: A Comprehensive Survey
Machine Learning (CS)
Makes wireless networks smarter and faster