Uncertainty-aware prediction of chemical reaction yields with graph neural networks

Youngchun Kwon,Seokho Kang,Youn-Suk Choi,Dongseon Lee

doi:10.1186/s13321-021-00579-z

Youngchun Kwon, Seokho Kang + Show 2 more

Open Access

https://doi.org/10.1186/s13321-021-00579-z

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Abstract

In this paper, we present a data-driven method for the uncertainty-aware prediction of chemical reaction yields. The reactants and products in a chemical reaction are represented as a set of molecular graphs. The predictive distribution of the yield is modeled as a graph neural network that directly processes a set of graphs with permutation invariance. Uncertainty-aware learning and inference are applied to the model to make accurate predictions and to evaluate their uncertainty. We demonstrate the effectiveness of the proposed method on benchmark datasets with various settings. Compared to the existing methods, the proposed method improves the prediction and uncertainty quantification performance in most settings.

Highlights

IntroductionThe prediction of chemical reaction yields is an important research topic in chemical synthesis planning [1, 2]
In organic chemistry, the prediction of chemical reaction yields is an important research topic in chemical synthesis planning [1, 2]
The main concept is to construct a prediction model that predicts the yield of a chemical reaction by learning from previously accumulated data comprising a number of chemical reactions annotated with their experimentally measured yields

Summary

Introduction

The prediction of chemical reaction yields is an important research topic in chemical synthesis planning [1, 2]. This enables the estimation of the overall yield of a complex synthetic pathway and the detection of low-yield reactions that negatively affect the overall yield. It provides clues for designing new reactions that provide higher yields to save on the time and cost required for experimental syntheses. Machine learning has achieved remarkable success in the data-driven prediction of chemical reaction yields [1, 3–7]. The successful application of a prediction model enables fast and efficient estimation of chemical reaction yields without

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Conclusion