Lift Your Molecules: Molecular Graph Generation in Latent Euclidean Space

TL;DR

This paper presents a novel framework for 3D molecular graph generation using Euclidean space embeddings, simplifying the process and achieving state-of-the-art results in molecular distribution learning.

Contribution

The introduction of the SyCo framework that maps molecular graphs to Euclidean point clouds and leverages E(n)-Equivariant GNNs for improved molecular generation.

Findings

Achieves over 30% better performance than previous methods on ZINC250K.

Outperforms existing non-autoregressive models on large datasets.

Enhances conditional molecular generation by up to 3.9 times.

Abstract

We introduce a new framework for molecular graph generation with 3D molecular generative models. Our Synthetic Coordinate Embedding (SyCo) framework maps molecular graphs to Euclidean point clouds via synthetic conformer coordinates and learns the inverse map using an E(n)-Equivariant Graph Neural Network (EGNN). The induced point cloud-structured latent space is well-suited to apply existing 3D molecular generative models. This approach simplifies the graph generation problem - without relying on molecular fragments nor autoregressive decoding - into a point cloud generation problem followed by node and edge classification tasks. Further, we propose a novel similarity-constrained optimization scheme for 3D diffusion models based on inpainting and guidance. As a concrete implementation of our framework, we develop EDM-SyCo based on the E(3) Equivariant Diffusion Model (EDM). EDM-SyCo achieves state-of-the-art performance in distribution learning of molecular graphs, outperforming the best non-autoregressive methods by more than 30% on ZINC250K and 16% on the large-scale GuacaMol dataset while improving conditional generation by up to 3.9 times.