Uncertainty-Aware Yield Prediction with Multimodal Molecular Features

Authors

  • Jiayuan Chen The Ohio State University
  • Kehan Guo University of Notre Dame
  • Zhen Liu Carnegie Mellon University
  • Olexandr Isayev Carnegie Mellon University
  • Xiangliang Zhang University of Notre Dame

DOI:

https://doi.org/10.1609/aaai.v38i8.28668

Keywords:

DMKM: Mining of Visual, Multimedia & Multimodal Data, APP: Natural Sciences

Abstract

Predicting chemical reaction yields is pivotal for efficient chemical synthesis, an area that focuses on the creation of novel compounds for diverse uses. Yield prediction demands accurate representations of reactions for forecasting practical transformation rates. Yet, the uncertainty issues broadcasting in real-world situations prohibit current models to excel in this task owing to the high sensitivity of yield activities and the uncertainty in yield measurements. Existing models often utilize single-modal feature representations, such as molecular fingerprints, SMILES sequences, or molecular graphs, which is not sufficient to capture the complex interactions and dynamic behavior of molecules in reactions. In this paper, we present an advanced Uncertainty-Aware Multimodal model (UAM) to tackle these challenges. Our approach seamlessly integrates data sources from multiple modalities by encompassing sequence representations, molecular graphs, and expert-defined chemical reaction features for a comprehensive representation of reactions. Additionally, we address both the model and data-based uncertainty, refining the model's predictive capability. Extensive experiments on three datasets, including two high throughput experiment (HTE) datasets and one chemist-constructed Amide coupling reaction dataset, demonstrate that UAM outperforms the state-of-the-art methods. The code and used datasets are available at https://github.com/jychen229/Multimodal-reaction-yield-prediction.

Published

2024-03-24

How to Cite

Chen, J., Guo, K. ., Liu, Z., Isayev, O., & Zhang, X. (2024). Uncertainty-Aware Yield Prediction with Multimodal Molecular Features. Proceedings of the AAAI Conference on Artificial Intelligence, 38(8), 8274-8282. https://doi.org/10.1609/aaai.v38i8.28668

Issue

Section

AAAI Technical Track on Data Mining & Knowledge Management