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A strategy of artificial intelligence with chemical fingerprinting to predict drug phase behaviors in complex systems.
  • Siqi Wang,
  • Yuanhui Ji
Siqi Wang
Southeast University
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Yuanhui Ji
Southeast University

Corresponding Author:[email protected]

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Abstract

With the large-scale development of drugs, understanding the drug phase behaviors in complex systems become increasingly important. Among them, the solubility of drugs in biorelevant media needs to be urgently understood. To address this challenge, new strategies based on machine learning models are proposed. First, the strategy trains five machine learning models based on fifteen molecular descriptors of the drug molecular properties. The XGboost model was identified as the best predictive model for predicting drug solubility performance in various solvents. Next, the input feature vectors were expanded for machine learning using the MACCS chemical fingerprint coupled with the XGboost model. The MACCS chemical fingerprint coupled with the XGboost model has significantly improved the prediction accuracy of drug solubility. This finding demonstrates that the proposed strategy has solubility prediction capability, which is expected to provide valid information for drug development and drug solvent screening.
16 Apr 2023Submitted to AIChE Journal
16 Apr 2023Review(s) Completed, Editorial Evaluation Pending
16 Apr 2023Submission Checks Completed
16 Apr 2023Assigned to Editor
24 Apr 2023Reviewer(s) Assigned
17 Oct 2023Editorial Decision: Revise Major
10 Nov 20231st Revision Received
12 Nov 2023Submission Checks Completed
12 Nov 2023Assigned to Editor
12 Nov 2023Review(s) Completed, Editorial Evaluation Pending