Pre-training via Denoising for Molecular Property Prediction

Sheheryar Zaidi, Michael Schaarschmidt, James Martens, Hyunjik Kim, Yee Whye Teh, Alvaro Sanchez-Gonzalez, Peter Battaglia, Razvan Pascanu, Jonathan Godwin

Keywords: Molecular Property Prediction, Pre-training, Graph Neural Networks, Denoising, Molecules

TL;DR: We describe a technique for pre-training models for molecular property prediction from 3D structures based on denoising and show that it achieves SOTA results for various tasks.

Abstract: Many important problems involving molecular property prediction from 3D structures have limited data, posing a generalization challenge for neural networks. In this paper, we describe a pre-training technique based on denoising that achieves a new state-of-the-art in molecular property prediction by utilizing large datasets of 3D molecular structures at equilibrium to learn meaningful representations for downstream tasks. Relying on the well-known link between denoising autoencoders and score-matching, we show that the denoising objective corresponds to learning a molecular force field -- arising from approximating the Boltzmann distribution with a mixture of Gaussians -- directly from equilibrium structures. Our experiments demonstrate that using this pre-training objective significantly improves performance on multiple benchmarks, achieving a new state-of-the-art on the majority of targets in the widely used QM9 dataset. Our analysis then provides practical insights into the effects of different factors -- dataset sizes, model size and architecture, and the choice of upstream/downstream datasets -- on pre-training.

Community Implementations: [ 2 code implementations](https://www.catalyzex.com/paper/arxiv:2206.00133/code)