ZYCO Institute
Eniola Olutogun
Computational neuroscience · AI & drug discovery · neural engineering · biomathematics · pharmacy
Computational scientist building validation-first models for drug safety and the brain. I validate before I believe, publish the method, and report what fails.
ZYCO Institute
About
I'm a computational scientist working across computational neuroscience, AI-driven drug discovery, neural engineering and biomathematics. Through ZYCO Institute I build models that predict where drugs fail — drug-resistance and cardiotoxicity (Topologix), and addiction-liability and reward-memory encoding (the CCT model, IMPRINT). My background is in pharmacy. The throughline is rigour: every method is proven on an honest benchmark before it counts.
Selected work
CCT model
A tripartite dynamical and Bayesian model of dopaminergic reward-memory encoding; falsifiable, pre-registered, validated by ODE simulation and MCMC.
Topologix
Topological (persistent-homology) models of drug–protein interaction, applied to drug-resistance prediction and hERG cardiotoxicity.
IMPRINT
Addiction-liability screening from receptor-binding profiles to encoding-risk classification.
GATE
An open framework for safety in closed-loop neuromodulation.
Publications
- The Conjunctive Consolidation Threshold (CCT) Model of Addiction: Toward Multi-Axis Acute Encoding Preventionosf.io/kg7b5
- Formal Mathematical Specification of the CCT Modelosf.io/emy4u
- Consolidation Threshold Dynamics in Human Drug-Associated Learningzenodo.org · 10.5281/zenodo.20492472