EGFR-AI Drug Discovery Computational Workflow
Abstract
This software repository contains the computational workflow developed for an EGFR-targeted artificial intelligence (AI)-assisted drug discovery study. The project includes source code and computational scripts for AI/ML-based analysis, molecular generation, molecular docking, molecular dynamics analysis, and downstream data processing. The workflow is designed to support computational drug discovery and reproducible research. This archived version is associated with the EGFR-AI drug discovery research manuscript and is intended to facilitate transparency, reproducibility, and reuse of the computational methods. The source code was originally developed and maintained in a GitHub repository. This deposit contains the EGFR-AI drug discovery project files and supporting documentation.
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Authors: Anuraj Nayarisseri, Kaparapu Jyothi, Suhane Sajal, Jadhav Vaishnavi, Shree Yug, Jungi Dhruvi, Sharma Rashmi, Sankhala Akansha, Swami Radhika, Panwar Umesh, Aarthy Murali, Madhavi Maddala, Bandaru Srinivas, Belapurkar Pranoti, Ahmad Hafiz, Woo Lee Keun, Bezerra Mendonça Junior Francisco Jaime, T Scotti1 Marcus, Scotti1 Luciana
Institutions: Universidade Federal da Paraíba, Koneru Lakshmaiah Education Foundation, The University of Texas at Dallas, Ras al-Khaimah Medical and Health Sciences University, GITAM University, Devi Ahilya Vishwavidyalaya, Osmania University, International University of Korea, In Silico Biosciences (United States)