CHIRAGKUMAR N. PATEL

Dr. Chiragkumar N. Patel is a proficient scientist with a extensive experience in the fields of computational biology, chemoinformatics and applied artificial intelligence. He has held a variety of academic and research positions, including a Post-Doctoral Fellow at the National Cancer Institute, NIH, where he specialized in Computer-Aided Drug Design, and a Senior Scientist at the Technology Innovation Institute in Abu Dhabi. His research has made a substantial contribution to the disciplines of computational toxicity, molecular dynamics, and drug discovery.

Dr. Patel has been actively engaged in the teaching and mentoring of a significant number of M.Sc. and Ph.D. students at Gujarat University. He has contributed to the advancement of computational methods for biological research by developing and managing numerous bioinformatics software and web-based programs.

Dr. Patel has a robust publication record and has authored and co-authored numerous research articles in high-impact journals. His research interests include the molecular modeling of drug interactions and disease mechanisms. His research has resulted in the creation of predictive models that facilitate the comprehension of drug efficacy and safety, thereby utilizing his expertise to enhance therapeutic strategies.

• Raghvendra Mall, Ankita Singh, Chirag N. Patel and Filippo Castiglione. "Ensemble of fine-tuned ESM2 models for peptide toxicity prediction", U.S. Patent Application 63/641,461.
• University Grant Commission, Govt. of India for doctoral studies.
• Department of Science & Technology (DST), International Travel Grant - Science & Engineering Research Board (SERB), Govt. of India for workshop on Bioinformatics Resources for Protein Biology, 2018 at European Bioinformatics Institute (EMBL-EBI) Welcome Genome Campus, Hinxton, Cambridge, CB10 1SD, United Kingdom.
• Best Oral Presentation Award in Young Scientist Category, Science Excellence, 2018 organized by Department of Botany, Bioinformatics and Climate Change Impacts Management, Gujarat University, Ahmedabad, India.
• Best Presentation Award in Young Scientist Category, Science Excellence, 2014 organized by Department of Botany, Bioinformatics and Climate Change Impacts Management, Gujarat University, Ahmedabad, India.
• Recipient of Budding Scientist Award for Research Article Presentation - National Symposium on 'Evolving Paradigm to Improve Productivity from Dynamic Management and Value Addition for Plant Genetic Resources' held at Department of Botany, Gujarat University, Ahmedabad- 380 009 between Oct 13-15, 2011.

• Strategies for Redesigning Withdrawn Drugs to Enhance Therapeutic Efficacy and Safety: A Review, Patel CN, Shakeel A, Mall R, Alawi KM, Ozerov IV, Zhavoronkov A, Castiglione F., Wiley Interdisciplinary Reviews: Computational Molecular Science, 15, 1, e70004, 2025.
• Mall R, Singh A, Patel CN, Guirimand G, & Castiglione F. (2024). VISH-Pred: an ensemble of fine-tuned ESM models for protein
  toxicity prediction. Briefings in Bioinformatics, 25(4).
• AI-driven drug repurposing and binding pose meta dynamics identifies novel targets for monkeypox virus, Patel CN, Mall R, Bensmail H., Journal of infection and public health, 16, 5, 799-807, 2023, 10.1016/j.jiph.2023.03.007.
• Patel CN, Jani SP, Kumar SP, Modi KM and Kumar Y (2022). Computational investigation of natural compounds as potential main
  protease (Mpro) inhibitors for SARS-CoV-2 virus. Computers in Biology and Medicine, 151, 106318. DOI:
  10.1016/j.compbiomed.2022.106318.
• Kumar SP, Patel CN, Rawal RM and Pandya, HA (2020). Energetic contributions of amino acid residues and its cross‐talk to delineate
  ligand‐binding mechanism. Proteins: Structure, Function, and Bioinformatics, 88(9), 1207-1225. DOI: 10.1002/prot.25894.

• Good Clinical Practice Certification / Human Subject Protection Certification – CITI Program.
• IBM-AI Engineering by Coursera.
• Crash course on Python by Coursera.

• PepAMD (Peptide-Adjuvant-docking-MD)GitHub: PepAMD
  A comprehensive tool for docking peptides with adjuvants followed by molecular dynamics simulations, facilitating the study of peptide-based vaccine designs and their interactions with adjuvants.
  
• Druggability ScoreA computational tool for assessing the druggability of FDA-approved and investigational drugs using a chemoinformatics and biology approach.
  
• ToxiAegisGitHub: ToxiAegis
  A platform for predicting protein toxicity, integrating an ensemble of fine-tuned ESM models designed to enhance the prediction accuracy of toxicological profiles.
  
• VISH-PredA specialized tool for protein toxicity prediction, employing a series of enhanced machine learning models to predict adverse effects based on protein structures and sequences.
  
• PharmRF scoring functionGitHub: PharmRF
  A novel scoring function that utilizes Random Forest algorithms to select the best protein-ligand complexes for structure-based pharmacophore screening.
  
• ECONTACT modelingGitHub: ECONTACT
  This software assesses the energetic contributions of amino acid residues and explores potential crosstalk in protein pockets involved in ligand binding, providing insights into ligand-binding mechanisms.
  
• MDCKpredPreviously hosted at: http://mdckpred.in (Hosting not renewed)
  An online tool for predicting the permeability of compounds across MDCK cell lines, using membrane-interaction chemical features.
  
• Standardization of Ig datasets using universal numbering system and auto-generating Bridged 1D mapsGitHub: Standardization of Ig datasets
  Facilitates the standardization of immunoglobulin datasets and the automatic generation of 1D bridged maps, aiding in the structural analysis of antibodies.
  
• ProteoMapsGitHub: 2D Maps for Ig domains and for RBDs
  A tool for understanding the 3D structure of proteins within a 2D space, focusing on immunoglobulin domains and receptor-binding domains.