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Advanced graph and sequence neural networks for molecular property prediction and drug discovery
Journal article   Open access   Peer reviewed

Advanced graph and sequence neural networks for molecular property prediction and drug discovery

Zhengyang Wang, Meng Liu, Youzhi Luo, Zhao Xu, Yaochen Xie, Limei Wang, Lei Cai, Qi Qi, Zhuoning Yuan, Tianbao Yang, …
BIOINFORMATICS, Vol.38(9), pp.2579-2586
04/28/2022
DOI: 10.1093/bioinformatics/btac112
PMID: 35179547
url
https://doi.org/10.1093/bioinformatics/btac112View
Published (Version of record) Open Access

Abstract

Motivation: Properties of molecules are indicative of their functions and thus are useful in many applications. With the advances of deep-learning methods, computational approaches for predicting molecular properties are gaining increasing momentum. However, there lacks customized and advanced methods and comprehensive tools for this task currently. Results: Here, we develop a suite of comprehensive machine-learning methods and tools spanning different computational models, molecular representations and loss functions for molecular property prediction and drug discovery. Specifically, we represent molecules as both graphs and sequences. Built on these representations, we develop novel deep models for learning from molecular graphs and sequences. In order to learn effectively from highly imbalanced datasets, we develop advanced loss functions that optimize areas under precision-recall curves (PRCs) and receiver operating characteristic (ROC) curves. Altogether, our work not only serves as a comprehensive tool, but also contributes toward developing novel and advanced graph and sequence-learning methodologies. Results on both online and offline antibiotics discovery and molecular property prediction tasks show that our methods achieve consistent improvements over prior methods. In particular, our methods achieve #1 ranking in terms of both ROC-AUC (area under curve) and PRC-AUC on the AI Cures open challenge for drug discovery related to COVID-19.
 Computer Science Mathematics Physical Sciences Technology COVID-19

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