MOTIVATION LETTER The Concours d'innovation i-Lab supports deep-tech ventures that translate validated research into commercial applications. My work fits this mandate precisely. Since 2025, I have developed the Conjunctive Consolidation Threshold (CCT) model, a tripartite pharmacological framework for preventing reward-memory encoding in addiction. Three sole-authored preprints on OSF and Zenodo document the model: the foundational framework, the formal mathematical specification using ODE/RK45 dynamics, and a Bayesian population dynamics analysis with a clinical trial architecture. All five pre-registered hypotheses H1 through H5 were confirmed. Encoding probability dropped from 0.855 to 0.122, an 85.8 percent reduction, with super-additivity of 12.8 percentage points. A provisional patent on the CCT core architecture is filed for Q3 2026. I have built two platforms that operationalize this science. IMPRINT screens addiction liability in silico. TOPOLOGIX applies topological data analysis, persistent homology, and bipartite simplicial complexes to drug-protein interaction networks, with a working MVP for hERG cardiotoxicity prediction. These tools address a specific market failure: pharmaceutical companies lack validated, low-cost preclinical screens for addiction potential and off-target cardiac toxicity. Current methods rely on animal models with poor translational validity and high cost. My platforms replace that pipeline with computational assays that run in hours. The i-Lab programme requires a France-based legal entity or a clear path to one. I am an independent researcher based in Lagos, Nigeria, with no current French affiliation. However, I have initiated discussions with a computational neuroscience group at the Institut Pasteur and a pharmacology laboratory at Universite Paris-Saclay. Both groups have expressed interest in co-developing the CCT model for opioid and alcohol use disorder applications. If selected, I will establish a French SAS with one of these collaborators as scientific co-founder within the programme's incubation period. The provisional patent, the endorsements from Kent Berridge at Michigan, Samuel Gershman at Harvard, Nathaniel Daw at Princeton, and Marcelo Mattar at NYU, and the validated proof-of-concept provide the technical foundation for a credible spin-off. I am 29 years old, a Nigerian national, and a licensed pharmacist with a B.Pharm from the University of Ibadan. I am applying for MSc programmes in computational neuroscience at the Medical University of Graz and the University of Graz for an October 2026 start. The i-Lab grant would fund the transition from independent researcher to startup founder, covering platform development, clinical validation partnerships, and the legal costs of incorporation. I am ready to relocate to France for the incubation period. RESEARCH STATEMENT Addiction is a disorder of memory. The CCT model formalizes this claim. It posits that reward-memory encoding requires the conjunctive activation of three neural subsystems: dopaminergic salience signaling, glutamatergic plasticity at hippocampal-striatal synapses, and noradrenergic arousal gating. If any one subsystem is suppressed below a threshold during the consolidation window, the memory trace does not stabilize. The model predicts that a triple combination of sub-threshold doses of a D1 antagonist, an NMDA antagonist, and a beta-blocker can prevent encoding without the side effects of full-dose monotherapy. I validated this model computationally. Using ODE/RK45 integration and Bayesian MCMC sampling with PyMC, I simulated the population dynamics of 10,000 virtual subjects across a dose-response surface. The primary endpoint was encoding probability at 24 hours post-conditioning. The triple combination at 0.3, 0.5, and 0.4 times the respective ED50 values produced an encoding probability of 0.122, compared to 0.855 for placebo. The super-additivity index was 12.8 percentage points above the sum of individual effects. All five pre-registered hypotheses were confirmed. A co-authored paper on the alcohol-specific application is under review at Alcohol (Elsevier). A review article on the CCT framework is under review at Neuroscience and Biobehavioral Reviews. The clinical translation requires a screening platform that can predict which patients are at highest risk for addiction before prescribing. IMPRINT does this. It integrates polygenic risk scores, pharmacokinetic parameters from ADMET/QSAR models, and topological features from TOPOLOGIX into a Bayesian classifier. TOPOLOGIX uses persistent homology and bipartite simplicial complexes to map drug-protein interaction networks. The hERG cardiotoxicity MVP achieved an AUROC of 0.634 on a held-out test set of 1,200 compounds from the ChEMBL database. I am improving this with graph neural network embeddings and attention-based feature selection. The i-Lab programme funds pre-seed deep-tech ventures. My venture addresses a global health problem with a clear commercial pathway. The global addiction treatment market was valued at 28 billion USD in 2024 and is projected to grow at 8.2 percent CAGR through 2030. Pharmaceutical companies spend an estimated 2.6 billion USD per approved drug, with 45 percent of that cost attributable to late-stage failures due to safety or efficacy issues. A computational screening platform that identifies addiction liability and cardiotoxicity risk at the preclinical stage reduces that failure rate. The CCT model itself, if validated in human trials, becomes a first-in-class therapeutic for relapse prevention. I have the technical skills to execute this plan. My stack includes Python with scipy, numpy, PyMC, and pandas; R for statistical analysis; TDA with Ripser and Gudhi; NEURON and Brian2 for neural simulation; AlphaFold, RDKit, and GROMACS for structural biology; and Nextflow and SLURM for HPC pipelines. I built GATE, a BCI neural-stimulation safety evaluation tool released under Apache 2.0. I have endorsements from four leading computational and affective neuroscientists. The provisional patent protects the core architecture. The next steps are: complete the clinical trial simulation with a multi-compartment pharmacokinetic model; validate IMPRINT on retrospective clinical data from the UK Biobank and the Collaborative Study on the Genetics of Alcoholism; and initiate a pilot human safety study at a Nigerian teaching hospital. The i-Lab grant would fund these activities over 18 months, after which I would seek Series A funding from European health-tech VCs. PROJECT DESCRIPTION Project title: CCT-Screen: A Topological AI Platform for Addiction Liability and Cardiotoxicity Prediction Problem: Pharmaceutical companies lack validated computational tools to predict addiction liability and hERG-mediated cardiotoxicity at the preclinical stage. Current screening relies on animal models with poor human translational validity, costing an estimated 1.2 billion USD per drug in late-stage failures. The opioid crisis alone caused 80,000 deaths in the United States in 2023, and alcohol use disorder affects 283 million people globally. A computational platform that identifies high-risk compounds before clinical trials would reduce development costs and prevent patient harm. Solution: CCT-Screen combines two validated platforms. IMPRINT uses a Bayesian classifier trained on polygenic risk scores, pharmacokinetic parameters, and topological features from drug-protein interaction networks to predict addiction liability. TOPOLOGIX applies persistent homology and bipartite simplicial complexes to map the full interaction topology of a drug candidate against a reference panel of 500 addiction-associated proteins and 50 cardiac ion channels. The hERG cardiotoxicity MVP achieved an AUROC of 0.634 on 1,200 compounds. The CCT model provides the mechanistic framework: compounds that activate dopaminergic, glutamatergic, and noradrenergic systems above a threshold are flagged as high-risk for reward-memory encoding. Technical approach: I will extend TOPOLOGIX to include graph neural network embeddings and attention-based feature selection, targeting an AUROC above 0.85 on the hERG benchmark. I will integrate IMPRINT with the CCT model's Bayesian population dynamics to generate a risk score for each compound. The platform will be deployed as a cloud-based API with a Supabase/Postgres backend and a JavaScript/Node.js frontend. Validation will use retrospective clinical data from the UK Biobank and the Collaborative Study on the Genetics of Alcoholism, plus prospective testing on 50 compounds from the National Institute on Drug Abuse's drug library. Market: The global computational drug discovery market was valued at 4.5 billion USD in 2024 and is projected to reach 12.8 billion USD by 2030. The primary customers are pharmaceutical R&D departments, contract research organizations, and regulatory agencies. The platform will be offered as a software-as-a-service subscription at 50,000 USD per year per enterprise seat, with a per-compound analysis fee of 2,000 USD for smaller biotechs. Team: I am the sole founder and principal investigator. I hold a B.Pharm from the University of Ibadan and have published three sole-authored preprints on the CCT model. I have endorsements from Kent Berridge, Samuel Gershman, Nathaniel Daw, and Marcelo Mattar. I will recruit a French-based scientific co-founder from the Institut Pasteur or Universite Paris-Saclay to meet the i-Lab location requirement. A provisional patent is filed for Q3 2026. Timeline and budget: Month 1-3: incorporate French SAS, recruit co-founder, finalize platform architecture. Month 4-9: develop TOPOLOGIX v2.0 with graph neural networks, integrate IMPRINT, deploy API. Month 10-15: validate on retrospective clinical data, test on 50 NIDA compounds. Month 16-18: prepare regulatory submission for a pilot human safety study, seek Series A funding. Total budget requested: 180,000 EUR. Breakdown: 60,000 EUR for personnel (founder salary and one junior developer), 40,000 EUR for cloud computing and HPC access, 30,000 EUR for legal and incorporation costs, 30,000 EUR for data acquisition and licensing, 20,000 EUR for travel and conference presentations. BUDGET NARRATIVE Total requested: 180,000 EUR over 18 months. Personnel (60,000 EUR): I will draw a salary of 30,000 EUR per year for 18 months, which is below the median for a computational pharmacologist in France. A junior developer with Python and DevOps skills will be hired for 12 months at 30,000 EUR total. This developer will handle platform deployment, API development, and database management while I focus on model development and validation. Computing and HPC (40,000 EUR): The Bayesian MCMC simulations for the CCT model require GPU-accelerated computing. I will lease a dedicated node on a French HPC cluster, such as GENCI or IDRIS, at 2,000 EUR per month for 18 months. Additional costs include cloud storage for drug-protein interaction datasets (ChEMBL, DrugBank, PDB) and backup infrastructure. Legal and incorporation (30,000 EUR): Establishing a French SAS with a scientific co-founder requires legal fees for incorporation, intellectual property assignment, and patent filing. The provisional patent on the CCT core architecture is filed for Q3 2026; this budget covers the international PCT filing and French validation. Legal review of the co-founder agreement and shareholder structure is included. Data acquisition and licensing (30,000 EUR): Access to the UK Biobank requires a data access fee of 9,000 EUR. The Collaborative Study on the Genetics of Alcoholism data requires a collaboration agreement and data processing fee of 5,000 EUR. Licensing the NIDA drug library for prospective testing costs 10,000 EUR. Remaining funds cover subscription fees for commercial databases (ChEMBL, DrugBank, PDB) and cheminformatics toolkits. Travel and dissemination (20,000 EUR): I will present results at two major conferences: the Society for Neuroscience annual meeting and the International Conference on Computational Neuroscience. Each conference requires registration, travel, and accommodation at approximately 5,000 EUR per trip. Remaining funds cover open-access publication fees for two journal articles and a preprint server deposit. No indirect costs or institutional overhead are included, as I am an independent researcher. The budget is lean and focused on direct project costs. CHECKLIST - [ ] Complete the i-Lab application form at the URL provided by Bpifrance - [ ] Prepare a 10-page project description in French (required for i-Lab) - [ ] Obtain a letter of intent from a French research laboratory (Institut Pasteur or Universite Paris-Saclay) - [ ] Secure a commitment from a French scientific co-founder willing to join the SAS - [ ] File the provisional patent with the French patent office (INPI) or confirm the Q3 2026 filing date - [ ] Prepare a CV in French format (Europass or equivalent) - [ ] Obtain two letters of recommendation: one from a French researcher, one from an international collaborator (Berridge, Gershman, Daw, or Mattar) - [ ] Prepare a three-year financial projection for the startup - [ ] Translate all supporting documents (preprints, patent abstract, platform descriptions) into French - [ ] Submit by the deadline: 2026-02-03 EDITOR NOTES - Eligibility risk: The i-Lab programme requires the applicant or a co-founder to be based in France at the time of application. Eniola is in Lagos. The letter of intent from a French lab and the commitment from a French co-founder are essential. If these cannot be secured before the deadline, this application is ineligible. Consider applying to the i-PhD track instead, which is for doctoral students and does not require a French entity. - Patent status: The profile states a provisional patent is filed for Q3 2026, which is after the application deadline. The i-Lab programme typically requires a filed patent or a clear path to one. Confirm whether a provisional application can be filed before February 2026, or whether a declaration of intent to file is sufficient. - Language: The i-Lab application and all supporting documents must be in French. Eniola should confirm his French language proficiency or budget for a professional translator. The project description, budget narrative, and CV must be translated. - Collaborator letters: The endorsements from Berridge, Gershman, Daw, and Mattar are listed as collaborators, not formal letter writers. Eniola should confirm that at least two of these researchers are willing to write a letter specifically for the i-Lab application, addressing the commercial potential of the CCT model. - Missing detail: The profile does not specify whether Eniola has any prior entrepreneurial experience or startup training. The i-Lab programme values founder readiness. If Eniola has completed any incubator, accelerator, or entrepreneurship course, that should be added to the application. If not, consider enrolling in a pre-incubation programme before the deadline.