eSupervisor Detail

About me

Computational drug discovery researcher with a chemistry background, specializing in AI-driven modeling of multimolecular therapeutic systems. Experienced in molecular docking, molecular dynamics, ADMET profiling, and network pharmacology. Focused on convergence analysis, feature engineering, and graph-based drug–target prediction to bridge traditional medicine knowledge with modern computational frameworks.

Interests: AI-Driven Drug Discovery – Development of machine learning and deep learning models for predicting drug–target interactions, especially for complex and multimolecular systems. Multimolecular & Systems Pharmacology – Convergence analysis of phytochemicals and traditional medicine formulations using network pharmacology and graph-based modeling approaches. Computational Molecular Modeling – Molecular docking, molecular dynamics simulations, binding free energy analysis, and ADMET prediction for small molecules and bioactive compounds. Feature Engineering for Biological Activity – Identification of structural, physicochemical, and systems-level descriptors relevant to biological activity prior to predictive modeling. Knowledge Graphs & Data Integration – Construction of AI-assisted knowledge graphs integrating phytochemical data, protein targets, gene networks, and biological pathways. Cluster Molecular & Quantum-Assisted Approaches – Exploring vibrationally assisted quantum tunneling and cluster-based molecular representations beyond traditional single-molecule paradigms. Network Pharmacology in Traditional Medicine – Bridging Ayurveda-inspired descriptors with computational modeling to enhance evidence-based validation of herbal formulations. Open Science & Collaborative Drug Discovery – Promoting transparent, reproducible, and collaborative research frameworks in pharmaceutical sciences.