Open AccessExtracting Predictive Representations from Hundreds of Millions of Molecules
Author(s) -
Dong Chen,
Jiaxin Zheng,
Guo-Wei Wei,
Feng Pan
Publication year - 2021
Publication title -
the journal of physical chemistry letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.563
H-Index - 203
ISSN - 1948-7185
DOI - 10.1021/acs.jpclett.1c03058
Subject(s) - computer science , task (project management) , machine learning , artificial intelligence , process (computing) , virtual screening , labeled data , supervised learning , data mining , drug discovery , bioinformatics , artificial neural network , management , economics , biology , operating system
The construction of appropriate representations remains essential for molecular predictions due to intricate molecular complexity. Additionally, it is often expensive and ethically constrained to generate labeled data for supervised learning in molecular sciences, leading to challenging small and diverse data sets. In this work, we develop a self-supervised learning approach to pretrain models from over 700 million unlabeled molecules in multiple databases. The intrinsic chemical logic learned from this approach enables the extraction of predictive representations from task-specific molecular sequences in a fine-tuned process. To understand the importance of self-supervised learning from unlabeled molecules, we assemble three models with different combinations of databases. Moreover, we propose a protocol based on data traits to automatically select the optimal model for a specific task. To validate the proposed method, we consider 10 benchmarks and 38 virtual screening data sets. Extensive validation indicates that the proposed method shows superb performance.