Peer-Reviewed Research
Pioneering AML Drug Discovery Through AI Research
Our multidisciplinary research program combines computational biology, machine learning, and structural chemistry to accelerate the discovery of novel therapeutics for Acute Myeloid Leukemia.
Core Research Areas
Our research spans four interconnected disciplines — each reinforcing the others to create a truly integrated drug discovery pipeline.
Transcriptomic Profiling of AML Subtypes
We apply advanced RNA-Seq analysis pipelines to identify differentially expressed genes and cryptic splicing events across AML patient cohorts, enabling the discovery of subtype-specific therapeutic vulnerabilities.
Generative Models for Molecular Design
Our generative AI engine leverages evolutionary algorithms and fragment-based assembly to design novel small molecules with optimized binding affinity, selectivity, and pharmacokinetic properties.
Network-Based Target Prioritization
Using co-expression network analysis and multi-omic repository cross-referencing, we identify and validate the most druggable protein targets driving leukemogenesis and treatment resistance.
Protein Structure Prediction & Docking
We integrate AlphaFold predictions with high-resolution geometric docking to map binding pockets, identify allosteric sites, and evaluate ligand-protein interactions at atomic resolution.
Research Highlights
Tackling the most challenging mutations in AML with AI-driven precision medicine.
Targeted Protein Degradation
Developing PROTACs and molecular glues to eliminate oncogenic proteins previously considered ‘undruggable’ by standard inhibitors. Our AI models predict optimal linker geometries and E3 ligase recruiters.
Splicing Modulators
Harnessing deep RNA-seq insights to identify and correct aberrant splicing patterns that drive leukemogenesis and drug resistance. Our pipeline detects novel splice junctions invisible to traditional tools.
Publications & Theses
Academic contributions from the AML2Ligand research program at Cairo University.
Automated Target Identification via Differential Expression Analysis in AML
AML2Ligand Research Team — Graduation Project — Cairo University, FCAI
A comprehensive pipeline that processes RNA-Seq data from TCGA to identify the top 20 protein targets most relevant to Acute Myeloid Leukemia through automated differential expression and co-expression network analysis.
De Novo Ligand Generation Using Evolutionary Fragment Assembly
AML2Ligand Research Team — Graduation Project — Cairo University, FCAI
A generative molecular design system that employs fragment-based assembly with evolutionary mutation algorithms to create novel chemical entities tailored to specific protein binding geometries.
End-to-End Drug Discovery: From Transcriptomic Data to Validated Lead Compounds
AML2Ligand Research Team — Graduation Project — Cairo University, FCAI
An integrated platform combining multi-phase algorithmic processing — transcriptomic profiling, target prioritization, structural modeling, ligand generation, and physicochemical profiling — into a unified drug discovery workflow.
Our Methodology
A rigorous, multi-phase approach that bridges computational prediction and biological validation.
Data Ingestion
TCGA RNA-Seq normalization
Target Ranking
Network co-expression
Structure Mapping
AlphaFold integration
Ligand Design
Generative fragment assembly
Validation
Docking & ADMET profiling