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Open Access

Impact of Domain Knowledge and Multi-Modality on Intelligent Molecular Property Prediction: A Systematic Survey

Peng Cheng National Laboratory, Shenzhen 518000, China, and also with School of Future Technology, South China University of Technology, Guangzhou 511442, China
Peng Cheng National Laboratory, Shenzhen 518000, China, and also with School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou 510006, China
Peng Cheng National Laboratory, Shenzhen 518000, China
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Abstract

The precise prediction of molecular properties is essential for advancements in drug development, particularly in virtual screening and compound optimization. The recent introduction of numerous deep learningbased methods has shown remarkable potential in enhancing Molecular Property Prediction (MPP), especially improving accuracy and insights into molecular structures. Yet, two critical questions arise: does the integration of domain knowledge augment the accuracy of molecular property prediction and does employing multi-modal data fusion yield more precise results than unique data source methods? To explore these matters, we comprehensively review and quantitatively analyze recent deep learning methods based on various benchmarks. We discover that integrating molecular information significantly improves Molecular Property Prediction (MPP) for both regression and classification tasks. Specifically, regression improvements, measured by reductions in Root Mean Square Error (RMSE), are up to 4.0%, while classification enhancements, measured by the area under the receiver operating characteristic curve (ROC-AUC), are up to 1.7%. Additionally, we discover that, as measured by ROC-AUC, augmenting 2D graphs with 3D information improves performance for classification tasks by up to 13.2% and enriching 2D graphs with 1D SMILES boosts multi-modal learning performance for regression tasks by up to 9.1%. The two consolidated insights offer crucial guidance for future advancements in drug discovery.

Keywords

Molecular Property Prediction (MPP)Deep Learning (DL)domain knowledgemulti-modalitydrug discovery

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Big Data Mining and Analytics
Volume 7 Issue 3,
September 2024
Pages 858-888
DOI: 10.26599/BDMA.2024.9020028
Cite this article:
Kuang T, Liu P, Ren Z. Impact of Domain Knowledge and Multi-Modality on Intelligent Molecular Property Prediction: A Systematic Survey. Big Data Mining and Analytics, 2024, 7(3): 858-888. https://doi.org/10.26599/BDMA.2024.9020028

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Received: 18 February 2024
Revised: 10 April 2024
Accepted: 15 April 2024
Published: 28 August 2024
© The author(s) 2024.

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
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