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Keras/TensorFlow in Drug Design for Immunity Disorders.

Paulina Dragan¹, Kavita Joshi¹, Alessandro Atzei^1,2

¹Faculty of Chemistry, University of Warsaw, Pasteura 1, 02-903 Warsaw, Poland.

International Journal of Molecular Sciences

|October 14, 2023

Summary

This study introduces a novel drug discovery approach using artificial intelligence to identify compounds targeting chemokine receptors involved in immune system regulation. The method enhances the prediction of drug efficacy and receptor selectivity for potential anti-inflammatory therapies.

Keywords:

CCR2 CCR3 CXCR3 G protein-coupled receptors Keras TensorFlow cancer chemokine receptors immunity disorders inflammation molecular dynamics neural network structure-based virtual screening

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An Organotypic High Throughput System for Characterization of Drug Sensitivity of Primary Multiple Myeloma Cells

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Area of Science:

Computational chemistry and cheminformatics
Immunology and pharmacology
Artificial intelligence in drug discovery

Background:

Immune system homeostasis relies on white blood cells and cytokine receptors.
Chemokines and their receptors mediate immune cell movement in health and disease.
Inflammatory disorders necessitate novel, effective therapeutic agents.

Purpose of the Study:

To discover novel compound scaffolds targeting chemokine receptors CCR2, CCR3, and CXCR3.
To utilize a Keras/TensorFlow neural network (NN) as a multi-class classifier for compound screening.
To enhance drug discovery by improving binding affinity and predicting receptor subtype selectivity.

Main Methods:

Structure-based virtual screening (SBVS) combined with Keras/TensorFlow NN.
All-atom molecular dynamics simulations to assess binding affinity.
Comparative analysis of predicted compounds against known receptor antagonists.

Main Results:

Identification of novel compound scaffolds with potential activity against CCR2, CCR3, and CXCR3.
Demonstration of NN's efficacy in classifying compound activity and predicting receptor subtype selectivity.
Achieved high accuracy in receptor subtype prediction, outperforming traditional SBVS for selectivity.

Conclusions:

Keras/TensorFlow NN offers significant advantages in drug discovery, complementing SBVS.
The developed NN models accurately predict receptor subtype selectivity, a challenge for SBVS.
NN models show potential for identifying targeted therapies for immune-related disorders.