Deep learning enhanced drug discovery for novel biomaterials in regenerative medicine utilizing graph neural network approach for predicting cellular responses

Published

15-03-2024

DOI:

https://doi.org/10.58414/SCIENTIFICTEMPER.2024.15.1.04

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Issue

Section

SECTION A: BIOLOGICAL SCIENCES, AGRICULTURE, BIOTECHNOLOGY, ZOOLOGY

Authors

  • Milindkumar N. Dandale Department of Mathematics and Humanities, Yeshwantrao Chavan College of Engineering, Nagpur, Maharashtra, India.
  • Amar P. Yadav Department of Computer Science and Engineering- Artificial Intelligence, Noida Institute of Engineering and Technology Greater Noida, Uttar Pradesh, India.
  • P. S. K. Reddy Department of Mathematics, JSS Science and Technology University, Mysuru, Karnataka, India.
  • Seema G. Kadu Department of Zoology, Shri Shivaji Education Society Amt’, Science College, Congressnagar, Nagpur, Maharashtra, India.
  • Madhusudana T Department of Pharmaceutical Chemistry, Kuvempu University, Kadur, Karnataka, India.
  • Manthan S. Manavadaria Department of Electronics and Communication, CSPIT, CHARUSAT, Changa, Gujarat, India.

Abstract

This study introduces a novel approach to drug discovery in regenerative medicine through the utilization of a graph neural network (GNN). The research methodology integrates the development and training of the GNN with a subsequent evaluation of its performance metrics. The first phase involves the generation of synthetic data simulating a biological network, employing networkX and NumPy libraries to construct a random graph with Erdos-Renyi topology. The data, representing cellular responses to biomaterials, is then converted into PyTorch tensors for compatibility with the GNN architecture. The GNN model, characterized by two fully connected layers with ReLU and log-softmax activations, captures intricate relationships within the graph-structured data. The second phase employs a stochastic gradient descent algorithm, specifically the Adam optimizer, to train the GNN over 100 epochs using the cross-entropy loss for multi-class classification. The research methodology extends to the evaluation phase, producing three distinct output graphs for analysis: Visualization of the graph structure, a comparison between predicted and true labels, and a plot illustrating training loss over epochs. Performance metrics, including accuracy, precision, recall, and F1-score, are computed to assess the model’s predictive capabilities quantitatively. The study concludes with a discussion on the nuances revealed by each graph and their implications for refining GNN models in the context of drug discovery for regenerative medicine.

How to Cite

Dandale, M. N., Yadav, A. P., Reddy, P. S. K., Kadu, S. G., T, M., & Manavadaria, M. S. (2024). Deep learning enhanced drug discovery for novel biomaterials in regenerative medicine utilizing graph neural network approach for predicting cellular responses. The Scientific Temper, 15(01), 1588–1594. https://doi.org/10.58414/SCIENTIFICTEMPER.2024.15.1.04

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