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Consistent semantic representation learning for out-of-distribution molecular property prediction
Xinlong Wen1, Hao Liu1, Wenhan Long1
1College of Informatics, Huazhong Agricultural University, No.1 Shizishan Street, Hongshan District, Wuhan, 430070, Hubei, People's Republic of China.
Briefings in Bioinformatics
|April 10, 2025
Summary
This study introduces a Consistent Semantic Representation Learning (CSRL) framework to improve out-of-distribution molecular property prediction. CSRL enhances model performance by ensuring consistent semantic understanding across different molecular representations, boosting accuracy.
Area of Science:
- * Computational chemistry and cheminformatics.
- * Machine learning for molecular property prediction.
Background:
- * Invariant molecular representation models aim for accurate out-of-distribution (OOD) predictions by identifying stable molecular substructures.
- * Challenges include complex functional group entanglement and activity cliffs, leading to inconsistent semantic representations and inaccurate predictions.
- * Existing methods struggle with semantic mapping across different molecular representations, causing performance degradation.
Purpose of the Study:
- * To explore the correlation between consistent semantic representations and molecular property prediction accuracy.
- * To propose a novel framework, Consistent Semantic Representation Learning (CSRL), to enhance OOD molecular property prediction.
- * To address the issue of inconsistent semantic mapping in different molecular representation forms.
Main Methods:
- * Development of the Consistent Semantic Representation Learning (CSRL) framework, comprising a Semantic Uni-code (SUC) module and a Consistent Semantic Extractor (CSE).
- * The SUC module corrects inaccurate embeddings across different molecular representation forms to ensure consistent semantic mapping.
- * The CSE module utilizes non-semantic information as training labels to guide learning and reduce reliance on specific molecular representations.
Main Results:
- * Extensive experiments demonstrate that consistent semantic representations significantly improve model performance.
- * The CSRL framework enhances the average Receiver Operating Characteristic - Area Under the Curve (ROC-AUC) by 6.43% compared to 11 state-of-the-art models.
- * The proposed method shows robust performance across 12 diverse datasets.
Conclusions:
- * Consistent semantic representation is crucial for guaranteeing reliable molecular property prediction, especially under distribution shifts.
- * The CSRL framework effectively overcomes the limitations of existing models in handling semantic inconsistencies.
- * CSRL offers a promising approach to improve the accuracy and robustness of OOD molecular property prediction models.
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