Aly Dhedhi
Designed blood-brain barrier-penetrant peptide therapeutics targeting glioblastoma using a fully computational pipeline. Published at IEEE CIBCB 2026 — one of the largest computational biology conferences in the world.
Where Aly Started
His Background
- • 10th grader at Jumeirah College, Dubai
- • Working with an MIT professor on an AI project
- • Completed Harvard Pre-Med program
- • Built Peptide Nexus — his own scientific platform
- • Strong CS skills: Python, C++, JavaScript
- • Created verticals for Mantis AI
His Goals
- • Publish peer-reviewed research in computational biology
- • Apply AI to real problems in medicine and drug design
- • Build a competitive edge for college admissions
- • Contribute to cancer therapeutics research
- • Develop a fully reproducible computational pipeline
The Problem He Wanted to Solve
Glioblastoma multiforme (GBM) is the most lethal primary brain cancer, with median survival of just 14-18 months. The blood-brain barrier (BBB) blocks most drug delivery efforts, making treatment incredibly difficult. Aly wanted to use computational methods to design peptide therapeutics that could penetrate the BBB and target the tumour-exclusive receptor variant EGFRvIII — a compelling but underexploited molecular target.
The Research
Working with YRI mentor Dr. Rajeshkhanna Bhuthkuri from Jawaharlal Nehru Technological University, Aly designed and evaluated four modular peptide constructs that fuse the validated BBB-shuttle motif Angiopep-2 with the EGFR-targeting sequence GE11. The entire pipeline was built as a fully cloud-native, reproducible computational workflow.
Computational Design and Evaluation of Blood-Brain Barrier-Penetrant Peptide Therapeutics Targeting EGFRvIII in Glioblastoma: A Fully In Silico Pipeline Study
The blood-brain barrier blocks most drug delivery to glioblastoma tumours, and EGFRvIII is a tumour-exclusive receptor that remains underexploited as a therapeutic target
Designed 4 peptide constructs using ESMFold for 3D structure prediction, PatchDock for molecular docking, and BioPython for physicochemical profiling
Construct P3 (cyclized GE11 variant) was the only candidate rated "High" for BBB permeability, with 281 hydrophobic contacts, 33 salt bridges, and the strongest predicted binding
Fully reproducible pipeline running entirely in Google Colab with zero proprietary software — democratizing computational drug design
Computational Drug Design at 15 Years Old
What makes Aly's research exceptional is the sophistication of the methodology. He didn't just run a simple analysis — he built a complete in silico drug design pipeline integrating protein structure prediction (ESMFold/Meta AI), molecular docking (PatchDock), binding affinity estimation, and BBB permeability profiling. The pipeline is fully reproducible — any researcher can replicate his results from a single Google Colab session with zero proprietary software. This level of computational rigor is typically seen in PhD-level research.
Peptide Constructs
Hydrophobic Contacts (P3)
BBB Permeability (P3)
Cloud Reproducible
The Outcome
Accepted at IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology
IEEE CIBCB 2026 — one of the largest computational biology conferences
2026
Computational Design and Evaluation of BBB-Penetrant Peptide Therapeutics Targeting EGFRvIII in Glioblastoma
Accepted for Presentation & Publication
Strong CS and biology background with AI experience, but no peer-reviewed publication or computational drug design experience
Published at a premier IEEE conference with a novel computational drug design pipeline for brain cancer therapeutics — as a 10th grader
The Bigger Picture
Median survival for glioblastoma patients — Aly's research targets a pathway that could change this
Years old — Aly tackled PhD-level computational drug design as a 10th grader from Dubai
One of IEEE's premier conferences in computational biology — where cutting-edge bioinformatics research is presented
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