I am on my way to become an AI engineer in Drug Discovery Domain. My passion lies in the intersection of Drug Discovery Modelling, Artificial Intelligence, and Computational Chemistry. I specialize in implementing Agentic AI and applying Explainability Techniques to ML Models to interpret molecular predictions, making AI-driven drug discovery more transparent and trustworthy. I am fascinated by how molecules and data can have a massive impact on human health.
- Agentic AI: Implementing autonomous system using agentic AI to accelerate drug discovery.
- Interpretable AI for Drug Discovery: Applying LRP and other techniques to explain GNN-based models in the context of medicinal chemistry
- PK-PD Modeling & Simulation: Building PKPD pipeline to enhance automation.
- Cheminformatics & Computational Chemistry: Working with molecular fingerprints, docking, MD simulations, and structure-activity relationships (SAR).
- 🔗 LinkedIn: linkedin.com/in/dinh-long-huynh-996193241
- 💻 Blog: https://medium.com/@dinhlong240600
- 📧 Reach me via email: [email protected]
- Programming: Python, R, Bash, SQL
- ML/AI: PyTorch, TensorFlow, scikit-learn, RayTune, LangChain, LangGraph
- Cheminformatics: RDKit, Schrödinger Suite, Glide
- PKPD Modeling: NONMEM and R-based PKPD tools
- Tools & Workflow: Git, Docker, Linux, Jupyter Notebooks
🧑💻 Always open to discussions on AI, pharmacology, and computational modeling—let's connect!