I'm a Computational Chemist and Machine Learning enthusiast passionate about developing AI-driven solutions for drug discovery.

My work focuses on combining Graph Neural Networks, Explainable AI (XAI), QSAR, Active Learning, and Molecular Modeling to build reliable and interpretable models for molecular property prediction and virtual screening.

I enjoy working on projects that bridge Machine Learning with Medicinal Chemistry, transforming raw chemical data into scientifically meaningful insights.

• 🧠 Explainable AI (Integrated Gradients, Model Interpretation)
• 🧬 Graph Neural Networks for Molecular Property Prediction
• 💊 AI-assisted Drug Discovery & Virtual Screening
• 📈 QSAR Modeling & Molecular Machine Learning
• 🎯 Active Learning & Uncertainty Estimation
• ⚗️ Cheminformatics using RDKit
• 🔬 Molecular Dynamics & Computational Chemistry

Developed a hybrid Graph Neural Network framework that combines molecular graph representations with Explainable AI techniques to interpret atom-level contributions and investigate whether the model learns meaningful chemistry rather than dataset-specific bias.

Tech: PyTorch Geometric • RDKit • Captum • Integrated Gradients

Designed a temporal active learning workflow using uncertainty estimation and conformal prediction to prioritize compounds for experimental screening under realistic chronological evaluation.

Tech: Scikit-learn • MAPIE • RDKit • Python

Built end-to-end QSAR workflows including molecular standardization, feature engineering, model development, hyperparameter optimization, and model interpretation.

Python • PyTorch • Scikit-learn • LightGBM • XGBoost • Optuna

RDKit • PyTorch Geometric • Captum • QSAR • Molecular Fingerprints • Molecular Descriptors

GROMACS • AutoDock Vina • Molecular Dynamics • Docking

Pandas • NumPy • Matplotlib • MLflow • MySQL

Git • Linux • Streamlit

• Large Language Models for Scientific Applications
• Generative AI for Molecule Generation
• Advanced Graph Neural Networks
• MLOps for Machine Learning Systems
• Explainable AI for Drug Discovery

I'm always interested in collaborating on projects involving

• Explainable AI
• Drug Discovery
• Molecular Machine Learning
• Graph Neural Networks
• QSAR
• Computational Chemistry

Feel free to connect with me on LinkedIn or explore my repositories below.

Python for Data Science and Machine Learning Bootcamp — Udemy