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Brave enough to code (live) my individuality #ComputationalBiologist
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Brave enough to code (live) my individuality #ComputationalBiologist

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ShabanAhmad/README.md

Shaban Ahmad

Postdoctoral Researcher · University of Copenhagen, Denmark 🇩🇰

"Brave enough to code (live) my individuality"  |  "Building AI that makes molecules make sense"

LinkedIn Website Scholar ORCID ResearchGate Scopus WoS KU Profile KU-MGB Lab


About Me

I am a computational biologist at PLEN, University of Copenhagen, working on AI-driven pipelines that connect structural biology, machine learning, and environmental biotechnology.

My research path began at Jamia Millia Islamia in New Delhi, where I completed my PhD on deep-learning models for lung cancer drug discovery, followed by a visiting position at the University of Jyvaskyla, Finland. I am now at Copenhagen working on PFAS "forever chemicals". So far, I have 100+ publications, an H-index of 30, 2,100+ citations, and 650+ verified peer reviews, along with open-source tools freely available to the community.

I am seeking an Assistant Professorship to build the Translational Computational Pharmacology (TCP) Group. Full details on my research vision, publications, teaching, and CV are on my personal website.

I contribute both through this account (ShabanAhmad) and through KU-MGB, the Microbial Genomics and Biodegradation research group at PLEN, University of Copenhagen.


Research Impact

Publications Citations H-index Peer Reviews Patents Top 2%


Repositories

Repository Description
DrLungker Deep ensemble (ResNet + FNN + LSTM) for anti-lung-cancer compound activity prediction, with WaterMap, DFT and MD validation
DeepEntXAI Hybrid CNN-LSTM with LIME explainability for Enterobacteriaceae drug activity classification
ChEMBL_LungCancer-BioAssays Python pipeline for ChEMBL bioassay data analysis and FEDPN polypharmacology against lung cancer
PubChem_LungCancer-BioAssay Plotting and analysis scripts for PubChem BioAssay lung cancer manuscript
RMSD-RMSF-Plots Scripts for MD trajectory visualisation (RMSD / RMSF)
LUNGXAI 1D CNN with LIME-based explainability for multi-class lung cancer classification from gene expression data
DeFluorX AI-driven discovery of fluoroacetate dehalogenases (FAcDs) for PFAS defluorination · KU-MGB
HADeFluorX Haloacid dehalogenase (HAD) screening pipeline, physics-aware and UniProt-ID-driven · KU-MGB
GenDeFluorX Generalised defluorination enzyme discovery framework · KU-MGB
KU-MGB.github.io Group website and result dashboards · KU-MGB

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  1. DeepEntXAI DeepEntXAI Public

    DeepEntXAI is an explainable deep learning model for predicting compound activity and efficacy on unlabelled data, integrating Hybrid XAI and LIME to identify key molecular features for drug screen…

    Jupyter Notebook

  2. DrLungker DrLungker Public

    DrLungker combines ResNet, FNN, and RNN-LSTM with ensemble methods (Averaging, Voting, Stacking), trained on 5,886 lung cancer bioassay descriptors from PubChem and ChEMBL. It predicts compound act…

    Jupyter Notebook

  3. SyeddNaseer/LUNGXAI SyeddNaseer/LUNGXAI Public

    LUNGXAI is a deep-learning framework combining a 1D CNN with LIME-based explainability for multi-class lung cancer classification from gene expression data. Trained on merged GEO, TCGA, and ICGC da…

    Jupyter Notebook

  4. KU-MGB/DeFluorX KU-MGB/DeFluorX Public

    DeFluorX: Discovering FAcDs for PFAS Defluorination

    Python 1