Guided Diffusion for Inverse Molecular Design

05 April 2023, Version 1
This content is a preprint and has not undergone peer review at the time of posting.


The holy grail of materials science is de novo molecular design -- i.e., the ability to engineer molecules with desired characteristics. Recently, this goal has become increasingly achievable thanks to developments such as equivariant graph neural networks that can better predict molecular properties, and to the improved performance of generation tasks, in particular of conditional generation, in text-to-image generators and large language models. Herein, we introduce GaUDI, a guided diffusion model for inverse molecular design, which combines these advances and can generate novel molecules with desired properties. GaUDI decouples the generator and the property-predicting models and can be guided using both point-wise targets and open-ended targets (e.g., minimum/maximum). We demonstrate GaUDI’s effectiveness using single- and multiple-objective tasks applied to newly-generated data sets of polycyclic aromatic systems, achieving nearly 100% validity of generated molecules. Further, for some tasks, GaUDI discovers better molecules than those present in our data set of 475k molecules.


inverse design
diffusion model
polycyclic aromatic systems
guided generation
molecular design

Supplementary materials

SI for "Guided Diffusion for Inverse Molecular Design"
Additional details for diffusion model, data, and representation development.

Supplementary weblinks


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