Shaban Ahmad

About

Shaban Ahmad is a Postdoctoral Researcher in the Microbial Ecology and Biotechnology section of the Department of Plant and Environmental Sciences, Faculty of Science at the University of Copenhagen. His research focuses on identifying PFAS-degrading enzymes in bacteria. He integrates artificial intelligence, computational biology, and bioinformatics to tackle global challenges associated with PFAS pollution.
He holds both an MSc and PhD in Bioinformatics from Jamia Millia Islamia, New Delhi, where his doctoral work centred on developing machine learning models for drug design, with a focus on lung cancer therapeutics.
Shaban brings extensive expertise in Robotic Automation, the Schrödinger Suite, and Geneious Prime, and is proficient in Python at a developer level. His earlier research contributions include ML-based drug screening at SRM University, de novo assembly and annotation of Ralstonia solanacearum UTT-25 at ICAR-IARI, and whole genome and chloroplast genome analysis of Vigna umbellata at ICGEB, New Delhi.

Employment

• University of Copenhagen Postdoctoral Researcher
  2025 - Present
• SRM University Haryana - Campus Project Assistant
  2021 - 2021
• Jamia Millia Islamia Project Assistant
  2020 - 2021
• ICAR-IARI DBT- Studentship
  2020 - 2020
• International Centre For Genetic Engineering and Biotechnology New Delhi Research Trainee
  2019 - 2020
• Jamia Hamdard DBT-Studentship
  2019 - 2019
• University of Delhi University of Delhi Innovation project- DBC-301
  2015 - 2016
• Københavns Universitet

Education

• Jamia Millia Islamia PhD Bioinformatics
  2021 - 2025
• University of Delhi B.Sc (Hons) Botany
  2015 - 2018

Projects & Funding

Projects & funding information is unavailable.

Publications (83)

• Natural Products as Therapeutics for Lung and Respiratory Diseases
  Natural Product Communications 2026 DOI: 10.1177/1934578X261450463
  +Save
• Multitarget docking and molecular enumeration reveal DdpMPyPEPhU as a potent modulator of cell cycle, glucocorticoid, and estrogen signalling in breast cancer
  PLOS One 2026 DOI: 10.1371/journal.pone.0344028
  +Save
• DrLungker: A Deep Ensemble Learning Framework for Predicting Anti‐Lung Cancer Compound Activity and Validating Multitarget Potency through WaterMap, DFT, MD Simulations, and MM‐GBSA Analysis
  Advanced Theory and Simulations 2026 DOI: 10.1002/adts.202501550
  +Save
• Multimodal evaluation of mannose engineered poly lactic glycolic acid nanoparticles with granulocyte colony stimulating factor focused delivery to bone marrow for neutropenia
  Scientific Reports 2026 DOI: 10.1038/s41598-025-29790-w
  +Save
• Artificial intelligence in molecular screening: Advances, challenges, and future perspectives
  Artificial Intelligence in Precision Drug Design, Volume 1 2026 DOI: 10.1016/b978-0-443-44415-9.00017-x
  +Save
• Artificial intelligence in multi-omics integration for precision drug design
  Artificial Intelligence in Precision Drug Design, Volume 1 2026 DOI: 10.1016/b978-0-443-44415-9.00014-4
  +Save
• Benchmarking inflammation-nutrition and TyG-related indices for 5-year mortality risk in adults with questionnaire-defined obstructive sleep apnea: a survey-weighted NHANES derivation cohort with multicenter external validation
  Journal of Translational Medicine 2026 DOI: 10.1186/s12967-026-08540-0
  +Save
• Probing the multitargeted potency of Theodrenaline and its comparative evaluation with Crizotinib against transferase, hydrolase, signalling, and apoptosis-related proteins to overcome lung cancer drug resistance
  International Journal of Biological Macromolecules 2025 DOI: 10.1016/j.ijbiomac.2025.142343
  +Save
• Deep learning in variant detection and annotation
  2025 DOI: 10.1016/B978-0-443-27574-6.00010-2
  +Save
• Contributors
  2025 DOI: 10.1016/B978-0-443-27574-6.01002-X
  +Save