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Link to original content: https://doi.org/10.1007/s10822-023-00497-2
GPCRLigNet: rapid screening for GPCR active ligands using machine learning | Journal of Computer-Aided Molecular Design Skip to main content

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GPCRLigNet: rapid screening for GPCR active ligands using machine learning

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Abstract

Molecules with bioactivity towards G protein-coupled receptors represent a subset of the vast space of small drug-like molecules. Here, we compare machine learning models, including dilated graph convolutional networks, that conduct binary classification to quickly identify molecules with activity towards G protein-coupled receptors. The models are trained and validated using a large set of over 600,000 active, inactive, and decoy compounds. The best performing machine learning model, dubbed GPCRLigNet, was a surprisingly simple feedforward dense neural network mapping from Morgan fingerprints to activity. Incorporation of GPCRLigNet into a high-throughput virtual screening workflow is demonstrated with molecular docking towards a particular G protein-coupled receptor, the pituitary adenylate cyclase-activating polypeptide receptor type 1. Through rigorous comparison of docking scores for molecules selected with and without using GPCRLigNet, we demonstrate an enrichment of potentially potent molecules using GPCRLigNet. This work provides a proof of principle that GPCRLigNet can effectively hone the chemical search space towards ligands with G protein-coupled receptor activity.

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Data and Software availability

The Python scripts for generating the training and validation datasets, building, and training machine learning models, and evaluating them are all available on GitHub https://github.com/jrem-chem/GPCRLigNet.git. In addition, the datasets themselves and indexes used for training and validation are provided along with the data and Python scripts to produce the figures in the paper.

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Acknowledgements

The work was mainly supported by an NIH grant R01-GM129431 to J.L. J.B.F. was supported by an NSF award (CHE-1945394 to J.L.). S.T.S. was supported by an NIH R35 award (R35-GM147579).

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Authors and Affiliations

  1. Department of Chemistry, University of Vermont, Burlington, VT, 05405, USA

    Jacob M Remington, Kyle T McKay, Noah B Beckage, Jonathon B Ferrell, Severin T. Schneebeli & Jianing Li

  2. Department of Industrial and Physical Pharmacy, Department of Chemistry, Purdue University, West Lafayette, IN, 47906, USA

    Severin T. Schneebeli

  3. Department of Pathology, University of Vermont, Burlington, VT, 05405, USA

    Severin T. Schneebeli & Jianing Li

  4. Department of Medicinal Chemistry and Molecular Pharmacology, Purdue University, West Lafayette, IN, 47906, USA

    Jianing Li

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Contributions

J.R. and J.L. wrote the main manuscript text. J.R., K.M, and N.B. prepared the figures. All authors reviewed and revised the manuscript.

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Correspondence to Jianing Li.

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Remington, J.M., McKay, K., Beckage, N.B. et al. GPCRLigNet: rapid screening for GPCR active ligands using machine learning. J Comput Aided Mol Des 37, 147–156 (2023). https://doi.org/10.1007/s10822-023-00497-2

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