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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.

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Summary
This summary is machine-generated.

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.

Keywords:
consistent semanticmolecular property predictionout-of-distributionrepresentation learning

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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.