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Prediction and design of cyclodextrin inclusion complexes formation via machine learning-based strategies

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posted on 2022-10-18, 15:30 authored by Yiming Ma, Yue Niu, Huaiyu YangHuaiyu Yang, Jiayu Dai, Jiawei Lin, Huiqi Wang, Songgu Wu, Qiuxiang Yin, Ling Zhou, Junbo Gong
This study reports a machine-learning (ML) method to develop multi-purpose prediction strategies for the formation of cyclodextrin inclusion complexes (ICs) in aqueous solutions. A balanced dataset of pharmaceutically relevant molecules was constructed using experimental verification. Three ML models (artificial neural network, support vector machine, and logistic regression) were established and optimized to predict IC formation. To provide more reliable approaches for different prediction requirements, ML-based linear, recall-first, and precision-first strategies were further established based on the ML models for the maximum recall or precision values. The proposed recall-first strategy identified all positive samples to avoid missing data in the prediction, and the precision-first strategy accurately identified positive samples to reduce the number of validation experiments. The ML-based prediction strategies for IC formation were first established and showed high accuracy and reliability. These strategies provide higher efficiency and lower processing cost solutions for IC screening.

Funding

China Scholarship Council

National Natural Science Foundation of China (No. NNSFC 22111530115)

Tianjin Municipal Natural Science Foundation (No.21JCYBJC00600)

History

School

  • Aeronautical, Automotive, Chemical and Materials Engineering

Department

  • Chemical Engineering

Published in

Chemical Engineering Science

Volume

261

Publisher

Elsevier

Version

  • AM (Accepted Manuscript)

Rights holder

© Elsevier

Publisher statement

This paper was accepted for publication in the journal Chemical Engineering Science and the definitive published version is available at https://doi.org/10.1016/j.ces.2022.117946

Acceptance date

2022-07-19

Publication date

2022-07-23

Copyright date

2022

ISSN

0009-2509

eISSN

1873-4405

Language

  • en

Depositor

Dr Huaiyu Yang. Deposit date: 17 October 2022

Article number

117946

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