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Related Experiment Video

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Retention time prediction for small samples based on integrating molecular representations and adaptive network.

Xiaoxiao Wang1, Fujian Zheng2, Meizhen Sheng1

  • 1School of Computer Science and Technology, Dalian University of Technology, Dalian 116024, Liaoning, China.

Journal of Chromatography. B, Analytical Technologies in the Biomedical and Life Sciences
|February 13, 2023
PubMed
Summary

This study introduces a novel method for predicting retention times (RTs) in chromatography. By combining multiple molecular representations and adaptive neural networks (MDC-ANN), it improves compound identification accuracy, even with limited data.

Keywords:
Compound AnnotationMulti-Data CombinationsNeural NetworkRetention Time PredictionTransfer Learning

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Area of Science:

  • Analytical Chemistry
  • Computational Chemistry
  • Machine Learning

Background:

  • Retention time (RT) is crucial for compound identification in chromatography, offering orthogonal information to mass spectrometry.
  • Existing machine learning methods for RT prediction often struggle with small training datasets common in chromatography.
  • Accurate RT prediction is vital for enhancing compound annotation and data analysis in various chemical analyses.

Purpose of the Study:

  • To develop an enhanced retention time prediction method (MDC-ANN) that overcomes limitations of small training datasets.
  • To improve the accuracy and reliability of compound identification through superior RT prediction.
  • To automatically optimize molecular representation combinations and neural network architectures for specific chromatographic systems.

Main Methods:

  • Proposed a Multi-Data Combinations and Adaptive Neural Network (MDC-ANN) approach for retention time prediction.
  • Utilized transfer learning and a pre-trained deep learning model on a large dataset to establish the base model.
  • Implemented automated selection of optimal molecular representation combinations and neural network structures tailored to the target system.

Main Results:

  • MDC-ANN demonstrated superior performance in Mean Absolute Error (MAE), Median Absolute Error (MedAE), Mean Relative Error (MRE), and R-squared (R²) compared to existing methods across 14 small datasets.
  • Integrating multiple molecular representations significantly improved RT prediction performance and provided richer information for compound annotation.
  • The optimal combination of molecular representations varied across different chromatographic systems, highlighting the need for adaptive approaches.

Conclusions:

  • The MDC-ANN method effectively enhances retention time prediction accuracy, particularly in scenarios with limited training data.
  • Automated selection of molecular representations and network architecture by MDC-ANN offers a promising solution for accurate RT prediction in real-world applications.
  • This approach contributes to more reliable compound identification and annotation in chromatographic analyses.