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DeepDelta: predicting ADMET improvements of molecular derivatives with deep learning

Journal of cheminformatics, 2023-10, Vol.15 (1), p.101-101, Article 101 [Peer Reviewed Journal]

The Author(s) 2023 ;COPYRIGHT 2023 BioMed Central Ltd. ;The Author(s) 2023. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 1758-2946 ;EISSN: 1758-2946 ;DOI: 10.1186/s13321-023-00769-x ;PMID: 37885017

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  • Title:
    DeepDelta: predicting ADMET improvements of molecular derivatives with deep learning
  • Author: Fralish, Zachary ; Chen, Ashley ; Skaluba, Paul ; Reker, Daniel
  • Subjects: ADMET ; Algorithms ; Benchmarks ; Biological properties ; Chemistry ; Chemistry and Materials Science ; Computational Biology/Bioinformatics ; Computer Applications in Chemistry ; Data mining ; Datasets ; Deep learning ; Documentation and Information in Chemistry ; Drug design ; Drug development ; Evaluation ; Learning algorithms ; Machine learning ; Mathematical models ; Message passing ; Molecular machines ; Molecular optimization ; Molecular properties ; Neural network ; Neural networks ; Optimization ; Physical properties ; Theoretical and Computational Chemistry
  • Is Part Of: Journal of cheminformatics, 2023-10, Vol.15 (1), p.101-101, Article 101
  • Description: Established molecular machine learning models process individual molecules as inputs to predict their biological, chemical, or physical properties. However, such algorithms require large datasets and have not been optimized to predict property differences between molecules, limiting their ability to learn from smaller datasets and to directly compare the anticipated properties of two molecules. Many drug and material development tasks would benefit from an algorithm that can directly compare two molecules to guide molecular optimization and prioritization, especially for tasks with limited available data. Here, we develop DeepDelta, a pairwise deep learning approach that processes two molecules simultaneously and learns to predict property differences between two molecules from small datasets. On 10 ADMET benchmark tasks, our DeepDelta approach significantly outperforms two established molecular machine learning algorithms, the directed message passing neural network (D-MPNN) ChemProp and Random Forest using radial fingerprints, for 70% of benchmarks in terms of Pearson’s r, 60% of benchmarks in terms of mean absolute error (MAE), and all external test sets for both Pearson’s r and MAE. We further analyze our performance and find that DeepDelta is particularly outperforming established approaches at predicting large differences in molecular properties and can perform scaffold hopping. Furthermore, we derive mathematically fundamental computational tests of our models based on mathematical invariants and show that compliance to these tests correlates with overall model performance — providing an innovative, unsupervised, and easily computable measure of expected model performance and applicability. Taken together, DeepDelta provides an accurate approach to predict molecular property differences by directly training on molecular pairs and their property differences to further support fidelity and transparency in molecular optimization for drug development and the chemical sciences.
  • Publisher: Cham: Springer International Publishing
  • Language: English
  • Identifier: ISSN: 1758-2946
    EISSN: 1758-2946
    DOI: 10.1186/s13321-023-00769-x
    PMID: 37885017
  • Source: Geneva Foundation Free Medical Journals at publisher websites
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    ProQuest Central
    DOAJ Directory of Open Access Journals
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