Michael Akpabey

Computational Biologist at the Broad Institute of MIT and Harvard building ML/AI, perturbation-screening, and scientific data systems for biological and drug discovery. I lead computational work spanning 100K+ small-molecule screens and billion-scale virtual screening, translating biological data and models into mechanism-of-action insights, candidate prioritization, and experimental decisions. My work combines computational biology with scientific engineering: Python, R, SQL, Nextflow, Docker, HPC/cloud, model benchmarking, LLM/RAG workflows, and scientist-facing analytical tools. I’ve worked across functional genomics, CRISPRi, multi-omics, RNA-seq, microbial genomics, spatial transcriptomics, and medicinal-chemistry/ADMET triage. My training in both bioinformatics and toxicology gives me a translational perspective on dose response, biomarkers, pharmacology, and preclinical decision-making. I’m especially interested in computational biology, ML/AI engineering, scientific AI, target discovery, drug discovery, and scientific data systems.