DeepReac+: deep active learning for quantitative modeling of organic chemical reactions. Issue 43 (21st October 2021)

• Record Type: Journal Article
• Authors: Gong, Yukang; Xue, Dongyu; Chuai, Guohui; Yu, Jing; Liu, Qi
• Is Part Of: Chemical science. Volume 12:Issue 43(2021)
• Page Start: 14459
• Page End: 14472
• Publication Date: 2021-10-21
• Subjects: Chemistry -- Periodicals; 540.5
• DOI: 10.1039/d1sc02087k
• Languages: English
• ISSNs: 2041-6520

Abstract

Abstract : Based on GNNs and active learning, DeepReac+ is designed as a universal framework for quantitative modeling of chemical reactions. It takes molecular structures as inputs directly and adapts to various prediction tasks with fewer training data. Abstract : Various computational methods have been developed for quantitative modeling of organic chemical reactions; however, the lack of universality as well as the requirement of large amounts of experimental data limit their broad applications. Here, we present DeepReac+, an efficient and universal computational framework for prediction of chemical reaction outcomes and identification of optimal reaction conditions based on deep active learning. Under this framework, DeepReac is designed as a graph-neural-network-based model, which directly takes 2D molecular structures as inputs and automatically adapts to different prediction tasks. In addition, carefully-designed active learning strategies are incorporated to substantially reduce the number of necessary experiments for model training. We demonstrate the universality and high efficiency of DeepReac+ by achieving the state-of-the-art results with a minimum of labeled data on three diverse chemical reaction datasets in several scenarios. Collectively, DeepReac+ has great potential and utility in the development of AI-aided chemical synthesis. DeepReac+ is freely accessible at https://github.com/bm2-lab/DeepReac .