Self-supervised molecular pretraining strategy for reaction prediction in low-resource scenarios

15 July 2021, Version 1
This content is a preprint and has not undergone peer review at the time of posting.

Abstract

In the face of low-resource reaction training samples, we construct a chemical platform for addressing small-scale reaction prediction problem. By using a self-supervised molecular pretraining strategy, the chemical information from 1 billion molecules can be delivered to small-scale reaction prediction. To demonstrate the broad applicability of our approach, we apply our model to three different name reaction prediction tasks. In the Baeyer-Villiger, Heck and Sharpless asymmetric epoxidation reactions, the accuracies increase by 5.7%, 10.8%, 4.8% on average, respectively.

Keywords

Deep Learning
Self-supervised pretraining
Organic Chemistry
Reaction Prediction
Molecules
MASS
Transformer
SMILES string representation

Supplementary materials

Title
Description
Actions
Title
Self-supervised molecular pretraining strategy for reaction prediction in low-resource scenarios
Description
Supplementary Materials for Self-supervised molecular pretraining strategy for reaction prediction in low-resource scenarios
Actions

Comments

Comments are not moderated before they are posted, but they can be removed by the site moderators if they are found to be in contravention of our Commenting Policy [opens in a new tab] - please read this policy before you post. Comments should be used for scholarly discussion of the content in question. You can find more information about how to use the commenting feature here [opens in a new tab] .
This site is protected by reCAPTCHA and the Google Privacy Policy [opens in a new tab] and Terms of Service [opens in a new tab] apply.