A Match Made in Drug Discovery: Marrying Geometric and Diffusion Models

Arun Raja, Meilina Reksoprodjo

Keywords: diffusion, geometric deep learning, molecular confirmation, deep generative models

Abstract: The chemical space of molecular candidates is vast, and interesting and powerful drugs are waiting to be found within this space. The use of machine learning has shown to be an effective method to speed up the process of discovering novel compounds, especially using (deep) generative models. The recent surge in graph generative models has opened up new avenues for exploring the chemical space of molecular candidates, enabling a more efficient and systematic exploration of the chemical space and increasing the chances of finding novel and potent molecules. One of the recent breakthroughs includes diffusion models, which have proven to yield superior performance in molecular conformation tasks, among others. In this blog post, we aim to highlight one of them, the “GeoDiff: A Geometric Diffusion Model for Molecular Conformation Generation” paper by Xu et al. (2022). We aim to distil the paper to provide researchers and practitioners with a deeper understanding of the (i) methodology and results and (ii) (societal) implications of this breakthrough in the field of drug discovery, and (iii) discuss future applications in the field of (bio)medicine and healthcare.

Blogpost Url: https://iclr-blogposts.github.io/staging/blog/2023/diffusion-is-all-you-need/

ICLR Papers: https://arxiv.org/abs/2203.02923

ID Of The Authors Of The ICLR Paper: ~Minkai_Xu1

Conflict Of Interest: No