EGFR-AI Drug Discovery Computational Workflow

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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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-09-21
DOI
https://doi.org/10.5281/zenodo.22874759
Primary Topic
Computational Drug Discovery Methods
Type
article
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article

EGFR-AI Drug Discovery Computational Workflow

Bandaru Srinivas, Madhavi Maddala, Anuraj Nayarisseri, Belapurkar Pranoti et al.
Zenodo (CERN European Organization for Nuclear Research)
Computational Drug Discovery Methods
article

EGFR-AI Drug Discovery Computational Workflow

Bandaru Srinivas, Madhavi Maddala, Anuraj Nayarisseri, Belapurkar Pranoti, Ahmad Hafiz, Swami Radhika, Sharma Rashmi, T Scotti1 Marcus, Woo Lee Keun, Panwar Umesh, Bezerra Mendonça Junior Francisco Jaime, Kaparapu Jyothi, Scotti1 Luciana, Jungi Dhruvi, Sankhala Akansha, Jadhav Vaishnavi, Shree Yug, Aarthy Murali, Suhane Sajal
article en

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.

Zenodo (CERN European Organization for Nuclear Research)
Devi Ahilya Vishwavidyalaya (IN), International University of Korea (KR), The University of Texas at Dallas (US), Universidade Federal da Paraíba (BR), In Silico Biosciences (United States) (US), Koneru Lakshmaiah Education Foundation (IN), Ras al-Khaimah Medical and Health Sciences University (AE), GITAM University (IN), Osmania University (IN)
Openalex Percentile: Top 9%
Computational Drug Discovery Methods
DOIOpen Access PDFOpenAlex
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EGFR-AI Drug Discovery Computational Workflow — Bandaru Srinivas, Madhavi Maddala, et al. · Zenodo (CERN European Organization for Nuclear Research) (2026) | TGRS Research Map | TGRS