I am a Molecular Dynamics (MD) Simulation enthusiast with a strong interest in applying Machine Learning (ML) and Quantum Computing to biological systems. My work revolves around leveraging computational techniques to understand molecular interactions at a deeper level.
- Molecular Dynamics (MD) Simulations in biological systems
- Quantum Computing for molecular modeling
- Machine Learning & AI in computational chemistry and biophysics
- MDAnalysis and AI-driven methods for enhanced trajectory analysis
I am expanding my expertise in MD simulations, enhanced sampling techniques, and the integration of AI-driven analysis to extract meaningful insights from large-scale molecular datasets.
I am open to collaboration on MD simulations, AI-driven trajectory analysis, and quantum computing applications in biomolecular research. If you're working on innovative techniques for molecular modeling, let's connect!
- Molecular Dynamics Simulations: GROMACS, AMBER
- Trajectory Analysis: MDAnalysis, VMD, PyMOL
- Machine Learning: Python, TensorFlow, PyTorch, Scikit-Learn
- Quantum Computing: Qiskit
- Programming & Scripting: Python, Bash, Julia, Fortran
I am particularly excited about the next-generation MD simulations, where AI and ML models enhance energy landscape exploration, conformational sampling, and binding free energy predictions. By integrating deep learning models with MDAnalysis, we can unlock new insights into complex biomolecular interactions at an unprecedented scale.
⚡ Fun Fact: The combination of AI and MD simulations is revolutionizing drug discovery and protein dynamics, making computational research more efficient and predictive than ever before!
💡 "Computational simulations are not just about numbers, but about uncovering the molecular mysteries of life."
Let's push the boundaries of computational science together! 🚀