Projects
Many of my machine learning projects targeting different biomedical problems were inspired by the work of Chanin Nantasenamat, whose research demonstrated how data-driven approaches can be applied across diverse therapeutic areas.
🧠 Machine Learning-Driven QSAR and Docking Pipeline for Alzheimer’s Disease
Description: QSAR modeling + molecular docking to identify AβA4 inhibitors.
Technologies: Python, RDKit, PyRx, ML regression models
Role: Sole developer (end-to-end pipeline)
Outcome: Research presented at Manipal University; identified potential lead compounds, and developed NeuroIC50, an online platform for testing prospective AβA4 inhibitors Link: Access NeuroIC50
🧬 TuberDock: ML QSAR Platform for Tuberculosis Treatment
Description: Web-based tool for predicting pIC50 values for anti-TB compounds.
Technologies: Python, Flask, Machine Learning
Role: Sole developer
Outcome: Platform available online for testing potential TB inhibitors
Link: Access TuberDock
🧠 SchizoDock: ML QSAR Platform for Schizophrenia Treatment
Description: Web server to predict pIC50 values for compounds targeting EAAT3 in schizophrenia.
Technologies: Python, Streamlit, QSAR modeling
Role: Sole developer
Outcome: Deployed and presented at Sathyabama Institute of Science and Technology
Link: Access SchizoDock
🧪 Computer-Aided Drug Discovery Tool for Acute Myeloid Leukemia
Description: Computational drug discovery pipeline targeting Tyrosine Kinase, MCL-1, PKC, and HAT.
Technologies: Python, RDKit, PaDEL, Random Forest Regressor, PyRx
Role: Sole developer
Outcome: Published as Identification of Leukemia Enzyme Inhibitors by Molecular Modeling and Machine Learning Approaches in Current Chemical Biology (2025).
Link: Download Paper Access Tools: Tyrosine Kinase, Histone AcetylTransferase, Induced myeloid leukemia cell differentiation
🔬 TB vs NTM Spectral Analysis Tool
Description: Application performing tuberculosis (TB) vs non-tuberculous mycobacteria (NTM) classification using spectral analysis + machine learning.
Technologies: Python, Machine Learning, Mass Spectrometry Data Processing
Role: Sole developer (Internship outcome)
Outcome: Developed tool enabling upload of spectral data for predictions with visualization and analysis.
Link: Access Tool