DuBloQ: Blockchain and Q-Learning Based Drug Discovery in Healthcare 4.0

Authors

Urvashi Ramdasani, Institute of Technology, Nirma University, Department of Computer Science and Engineering, Ahmedabad, India
Gunjan Vinzuda, Institute of Technology, Nirma University, Department of Computer Science and Engineering, Ahmedabad, India
Sudeep Tanwar, Institute of Technology, Nirma University, Department of Computer Science and Engineering, Ahmedabad, India
Rajesh Gupta, Institute of Technology, Nirma University, Department of Computer Science and Engineering, Ahmedabad, India
Mohsen Guizani, Mohamed bin Zayed University of Artificial IntelligenceFollow

Document Type

Conference Proceeding

Publication Title

2022 International Wireless Communications and Mobile Computing

Abstract

Drug Discovery is a process by which new potential drugs are discovered and clinically trialed for commercial medicinal purposes. It has several stages of development, where each stage requires a prescribed time for its completion. The stages of drug development are discovery and development, pre-clinical research, clinical development, Food and Drug Administration (FDA) review, and post-market monitoring. The first three stages themselves take nearly 6.5 years. These stages take a huge time in cases where there is an urgent need for a drug. For example, during the COVID-19 pandemic, there was an urgent need for a vaccine. Many research institutes worked $24 \times 7$ to develop a vaccine, but it still took a considerable time to get to a bare minimum vaccine. To tackle this problem, we propose DuBloQ, a novel methodology for drug discovery using Q-Learning. Our Q-Learning model consists of a generator and a predictor model. The generator generates a set of Simplified Molecular Input Line Entry System (SMILES) strings and the Predictor predicts its logp values. Based on the logp values, the reward for the generator is provided to improve its performance. We integrate this model with a blockchain User Interface (UI) that ensures security and privacy. We achieved an accuracy of 76.1% for the generator model. © 2022 IEEE.

First Page

284

Last Page

289

DOI

10.1109/IWCMC55113.2022.9824216

Publication Date

7-19-2022

Keywords

Blockchain, Drug Discovery, Privacy, Q-learning, Security, Smart Contracts, Clinical research, Learning systems, Reinforcement learning, User interfaces, Vaccines

Comments

IR Deposit conditions: non-described

Recommended Citation

U. Ramdasani, G. Vinzuda, S. Tanwar, R. Gupta and M. Guizani, "DuBloQ: Blockchain and Q-Learning Based Drug Discovery in Healthcare 4.0," 2022 International Wireless Communications and Mobile Computing (IWCMC), 2022, pp. 284-289, doi: 10.1109/IWCMC55113.2022.9824216.