Permutationally Invariant Deep Learning Approach to Molecular Fingerprinting with Application to Compound Mixtures

Recent advancements in deep learning have led to widespread applications of its algorithms to synthetic planning and reaction predictions in the field of chemistry. One major area, known as supervised learning, is being explored for predicting certain properties such as reaction yields and types. Ma...

Full description

Saved in:
Bibliographic Details
Published in:Journal of chemical information and modeling 2021-02, Vol.61 (2), p.631-640
Main Authors: Buin, Andrei, Chiang, Hung Yi, Gadsden, S. Andrew, Alderson, Faraz A
Format: Article
Language:English
Subjects:
Algorithms
Chemical fingerprinting
Deep learning
Hydrogen peroxide
Invariance
Machine learning
Machine Learning and Deep Learning
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Recent advancements in deep learning have led to widespread applications of its algorithms to synthetic planning and reaction predictions in the field of chemistry. One major area, known as supervised learning, is being explored for predicting certain properties such as reaction yields and types. Many chemical descriptors known as fingerprints are being explored as potential candidates for reaction properties prediction. However, there are few studies that describe the permutational invariance of chemical fingerprints, which are concatenated at some stage before being fed to deep learning architecture. In this work, we show that by utilizing permutational invariance, we consistently see improved results in terms of accuracy relative to previously published studies. Furthermore, we are able to accurately predict hydrogen peroxide loss with our own dataset, which consists of more than 20 ingredients in each chemical formulation.
ISSN:1549-9596
1549-960X
DOI:10.1021/acs.jcim.0c01097