Deep learning methods have been considered promising for accelerating molecular screening in drug discovery and material design. Due to the limited availability of labelled data, various self-supervised molecular pre-training methods have been presented. Although many existing methods utilize common pre-training tasks in computer vision and natural language processing, they often overlook the fundamental physical principles governing molecules. In contrast, applying denoising in pre-training can be interpreted as an equivalent force learning, but the limited noise distribution introduces bias into the molecular distribution. To address this issue, we introduce a molecular pre-training framework called fractional denoising, which decouples noise design from the constraints imposed by force learning equivalence. In this way, the noise becomes customizable, allowing for incorporating chemical priors to substantially improve the molecular distribution modelling. Experiments demonstrate that our framework consistently outperforms existing methods, establishing state-of-the-art results across force prediction, quantum chemical properties and binding affinity tasks. The refined noise design enhances force accuracy and sampling coverage, which contribute to the creation of physically consistent molecular representations, ultimately leading to superior predictive performance.

Publication:

NATURE MACHINE INTELLIGENCE

http://dx.doi.org/10.1038/s42256-024-00900-z

Author:

Yuyan Ni

Institute for AI Industry Research (AIR), Tsinghua University, Beijing, China

Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China

University of Chinese Academy of Sciences, Beijing, China

Institute for AI Industry Research (AIR), Tsinghua University, Beijing, China.

These authors contributed equally: Yuyan Ni

e-mail: lanyanyan@air.tsinghua.edu.cn

Shikun Feng

Institute for AI Industry Research (AIR), Tsinghua University, Beijing, China

These authors contributed equally: Shikun Feng

Xin Hong

Institute for AI Industry Research (AIR), Tsinghua University, Beijing, China

Yuancheng Sun

University of Chinese Academy of Sciences, Beijing, China

Institute of Automation, Chinese Academy of Sciences, Beijing, China

Beijing Academy of Artificial Intelligence, Beijing, China

Wei-Ying Ma

Institute for AI Industry Research (AIR), Tsinghua University, Beijing, China

Zhi-Ming Ma

Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China

University of Chinese Academy of Sciences, Beijing, China

Qiwei Ye

Institute of Automation, Chinese Academy of Sciences, Beijing, China

Yanyan Lan

Institute for AI Industry Research (AIR), Tsinghua University, Beijing, China

Beijing Academy of Artificial Intelligence, Beijing, China