Dhairiya Agarwal

PhD Candidate | M.S. (Pharm.) Medicinal Chemistry

National Institute of Pharmaceutical Education and Research (NIPER), Mohali

Computational medicinal chemist specializing in AI-driven drug discovery and multi-target drug design. My research focuses on developing novel therapeutics for Alzheimer's disease using advanced machine learning, deep learning, and network pharmacology approaches.

6
Publications
1
Patent
8.5
PhD CGPA
Download CV View Publications GitHub Profile
Dhairiya Agarwal - PhD Candidate in Pharmacoinformatics at NIPER Mohali
Phone: +91-9015064028
Location: Ghaziabad, UP, India
Education

Academic Background

Pharmaceutical sciences and computational drug discovery training

Doctor of Philosophy (PhD)

Pharmacoinformatics

National Institute of Pharmaceutical Education and Research (NIPER), Mohali

Guide: Prof. Prabha Garg, Professor

πŸ“§ Gmail | πŸ”— LinkedIn | πŸ’» GitHub | πŸŽ“ Google Scholar
CGPA: 8.5/10 2023 - 2026 (Pursuing)

Master of Science (M.S. Pharm.)

Medicinal Chemistry

National Institute of Pharmaceutical Education and Research (NIPER), Raebareli

Thesis: Design, Synthesis and In Silico Studies of Coumarin Derivatives as Multi-targeted Anti-Alzheimer's Agents

Advisor: Dr. Gopal Lal Khatik, Assistant Professor

πŸ“§ Gmail | πŸ”— LinkedIn | πŸŽ“ Google Scholar
CGPA: 8.0/10 2021 - 2023

Bachelor of Pharmacy (B.Pharm)

Pharmaceutical Sciences

Sunder Deep College of Pharmacy, Ghaziabad
Dr. APJ Abdul Kalam Technical University, Lucknow

CGPA: 8.6/10 2016 - 2020

Competitive Examinations Qualified

NIPER-PhD
2023
NIPER-JEE
2021
GPAT
2021
Research Focus

Research Interests

Computational approaches to drug discovery and development

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AI-Driven Drug Discovery

Machine learning and deep learning applications in molecular property prediction and drug design

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Computational Medicinal Chemistry

Structure-based and ligand-based drug design using molecular modeling and docking

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Multi-Target Drug Design

Design and optimization of compounds targeting multiple disease pathways simultaneously

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Anti-Alzheimer's Therapeutics

Development of novel therapeutics targeting acetylcholinesterase and BACE1 enzymes

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Network Pharmacology

Systems biology approaches to understand drug-target-disease interactions

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QSAR/QSPR Modeling

Quantitative structure-activity/property relationships using statistical and ML methods

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Graph Neural Networks

GNN architectures for molecular graph-based learning and interaction prediction

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Generative AI Models

VAE and other generative models for de novo molecular design and optimization

Scientific Contributions

Publications & Patents

Peer-reviewed research articles and intellectual property

Journal Article

Shah, A. J.; Dar, M. Y.; Adnan, M.; Varma, T.; Agarwal, D.; Garg, P.; Mir, R. H.; Meena, R.; Masoodi, M. H.

Integration of phytochemical profiling and computational approaches to evaluate the neuroprotective potential of Nardostachys jatamansi in Alzheimer's disease

Biotechnology Reports

2025
DOI Full Text
Journal Article

Agarwal, D.; Malik, J.; Bhanwala, N.; Ambatwar, R.; Kumar, S.; Chandrakar, L.; Khatik, G. L.

Networkodynamic approach to perceive the key phytoconstituents of E. officinalis (Amla) as natural BACE1 inhibitors: an in-silico study

Journal of Biomolecular Structure and Dynamics

2024
DOI Full Text
Journal Article

Mahajan, A.; Yadav, S.; Malik, J.; Agarwal, D.; Ambatwar, R.; Datusalia, A. K.; Khatik, G. L.

Design, Synthesis, Computational Study, and Antidiabetic Evaluation of Benzoxazole Derivatives

ChemistrySelect

2024
DOI Full Text
Journal Article

Agarwal, D.; Kumar, S.; Ambatwar, R.; Bhanwala, N.; Chandrakar, L.; Khatik, G. L.

Lead Identification Through In Silico Studies: Targeting Acetylcholinesterase Enzyme Against Alzheimer's Disease

Central Nervous System Agents in Medicinal Chemistry

2024
DOI Full Text
Journal Article

Ambatwar, R.; Kumar, S.; Agarwal, D.; Chandrakar, L.; Khatik, G. L.

Cobalt Perchlorate Hexahydrate Catalyzed One-Pot Synthesis of Dihydropyrimidin-ones/-thiones through Sonochemistry and its Mechanistic Study using Density Functional Theory Calculations

Journal of Heterocyclic Chemistry

2024
DOI Full Text
Indian Patent

Khatik, G. L.; Datusalia, A. K.; Mahajan, A.; Yadav, S.; Malik, J.; Agarwal, D.; Ambatwar, R.

BENZOXAZOLE-2-YL-2-PHENOXYACETAMIDE DERIVATIVES AS ALPHA-AMYLASE INHIBITORS AND ANTI-DIABETIC

Indian Patent Office

Patent No. 554,344 (2024)
Patent Office Documentation
Open Source

GitHub Projects & Tools

Computational chemistry tools, AI models, and drug discovery pipelines

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cardiotox_prediction

Machine learning-based prediction of cardiotoxicity for drug candidates. Employs molecular descriptors and fingerprints to classify compounds by cardiac safety risk, aiding early-stage drug safety assessment.

Python Scikit-learn RDKit Toxicology
🐍 Python πŸ“‚ Public
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Gromacs_docker_setup

Dockerized GROMACS environment for reproducible molecular dynamics simulations. Provides a ready-to-use containerized setup, eliminating complex installation dependencies for MD simulation workflows.

Docker GROMACS MD Simulation Shell
🐳 Docker πŸ“‚ Public
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Repurposing

3D-VAE driven molecular repurposing framework. Leverages Variational Autoencoders with 3D molecular representations to identify existing drugs for new therapeutic indications through latent space exploration.

PyTorch VAE 3D Molecules Drug Repurposing
🐍 Python πŸ“‚ Public
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Pks13_Pred

Classification model for Pks13 inhibitors β€” a key target for tuberculosis drug discovery. Uses machine learning to predict inhibitory activity of compounds against the Pks13 enzyme of Mycobacterium tuberculosis.

Python Scikit-learn RDKit Classification
🐍 Python πŸ“‚ Public
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research_paper_chatbot

AI-powered chatbot for interacting with research papers. Upload PDFs and ask questions in natural language β€” the chatbot extracts, processes, and retrieves relevant answers using NLP and retrieval-augmented generation.

Python LLM RAG NLP
🐍 Python πŸ“‚ Public
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Diagnosing-Alzheimer-s-Disease

Deep learning approach for diagnosing Alzheimer's disease from medical imaging data. Utilizes convolutional neural networks to classify brain scans and assist in early-stage AD detection and severity staging.

Python TensorFlow CNN Medical Imaging
🐍 Python πŸ“‚ Public
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ChE_prediction

Cholinesterase inhibitor prediction using machine learning models. Predicts the inhibitory potential of compounds against acetylcholinesterase (AChE) and butyrylcholinesterase (BChE) β€” key targets in Alzheimer's therapy.

Python Scikit-learn RDKit QSAR
🐍 Python πŸ“‚ Public
Expertise

Technical Skills

Computational tools and laboratory techniques

πŸ–₯️ Computational Methods

  • Molecular Docking (AutoDock Vina)
  • Molecular Dynamics Simulation
  • QSAR/QSPR Modeling
  • Network Pharmacology
  • Machine Learning (Scikit-learn)
  • Deep Learning (TensorFlow, PyTorch)
  • Graph Neural Networks
  • ADMET Prediction
  • Binding Free Energy Calculations

πŸ’» Software & Tools

  • SchrΓΆdinger Suite
  • Biovia Discovery Studio
  • ChemDraw Ultra 19.0
  • SwissADME
  • Python (RDKit, Pandas, NumPy)
  • Chem3D
  • MestreNova
  • SciFinder, PubChem, ZINC
  • PyMOL, Chimera

πŸ”¬ Experimental Skills

  • Organic Synthesis & Purification
  • Column Chromatography
  • Preparative Chromatography
  • TLC Analysis
  • NMR Spectroscopy
  • IR Spectroscopy
  • HRMS Analysis
  • Ellman Assay
  • Ξ±-Amylase Assay
  • In-vivo Studies & Animal Handling
Contact

Get In Touch

Open to research collaborations and opportunities

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Email

dhairiyaa@gmail.com

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Phone

+91-9015064028

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Address

06, Rajeev Puram Dasna
Ghaziabad, UP 201015, India

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LinkedIn

View Profile

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GitHub

@pip700