I'm Islam Omar, an AI Researcher working at the intersection of artificial intelligence and computational chemistry, with a focus on AI-driven, structure-based drug discovery. My work explores how modern deep learning — graph neural networks, equivariant architectures, generative diffusion models, and protein language models — can accelerate the discovery of safe, effective therapeutics.
- 🔬 Researching foundation models for molecular science and protein–ligand interactions
- 🧠 Designing geometric deep learning and graph transformer architectures for chemistry
- 💊 Building pipelines for molecular docking, binding affinity prediction, and molecular optimization
- 🧫 Bridging scientific machine learning with quantum chemistry and molecular dynamics
- 🌍 Passionate about open science, reproducibility, and trustworthy AI for medicine
- ✉️ Reach me at islamomar662@gmail.com
| Domain | Focus Areas |
|---|---|
| 🤖 AI & ML | Artificial Intelligence · Machine Learning · Deep Learning · Reinforcement Learning |
| 🧪 Computational Chemistry | Structure-Based Drug Discovery · Drug Design · Molecular Modeling · Molecular Dynamics |
| 🧬 Protein Science | Protein–Ligand Modeling · Binding Affinity Prediction · Protein Engineering · Protein Language Models |
| 🌐 Geometric & Generative AI | Graph Neural Networks · Geometric Deep Learning · Equivariant Neural Networks · Diffusion Models · Generative AI |
| 📊 Representation & Optimization | Molecular Representation Learning · Bayesian Optimization · Active Learning |
| ⚛️ Frontier AI for Science | Foundation Models · Quantum Machine Learning · AI for Precision Medicine |
research:
- AI-guided Molecular Optimization
- Foundation Models for Molecular Science
- Protein Language Models
- Graph Transformers
- Geometric Deep Learning
- Molecular Docking
- Binding Affinity Prediction
- Autonomous Molecular Discovery
- Scientific AI Systems
- Large Language Models for Science"Great science emerges where rigorous chemistry meets rigorous machine learning — I aim to build AI systems that are not only accurate, but interpretable, reproducible, and trustworthy enough to guide real-world drug discovery decisions."
I believe the next generation of breakthroughs in medicine will come from AI systems grounded in physical and chemical reality — models that respect symmetry, geometry, and biophysics rather than treating molecules as mere strings or images. My research is driven by curiosity, scientific rigor, and a commitment to open, reproducible, and collaborative science.
A 3D visualization of my yearly contribution history — explore it interactively at skyline.github.com/Islamomar-1
| 🧬 Structure-Based Drug Discovery |
🌐 Geometric Deep Learning |
🤖 Foundation Models for Molecules |
| 🔗 Protein–Ligand Interaction Modeling |
🌀 Generative & Diffusion Models for Chemistry |
⚛️ Quantum Machine Learning |
For a complete and up-to-date list of my peer-reviewed publications, preprints, and citation metrics, please visit my Google Scholar profile:
- 🎓 Active researcher in AI-driven drug discovery and computational chemistry
- 🧠 Developer of geometric deep learning models for molecular and protein systems
- 🌍 Contributor to open-source tools in scientific machine learning
- 📝 Published research indexed on Google Scholar and ORCID
- 🤝 Collaborator across interdisciplinary AI and chemistry research teams
- 🚀 Publish high-impact AI-for-drug-discovery research
- 🧬 Build foundation models for molecular science
- 🌐 Contribute to open-source computational chemistry tools
- 🔒 Develop trustworthy and interpretable AI systems for science
- 🤝 Collaborate internationally with leading research labs
I'm always open to discussing research collaborations, AI for drug discovery, and scientific machine learning. Feel free to reach out!
