MOTIVATION LETTER The Conjunctive Consolidation Threshold model, a tripartite pharmacological framework for reward-memory encoding prevention in addiction, is now specified mathematically, validated through ODE/RK45 and Bayesian MCMC simulation, and published as three sole-authored preprints on OSF and Zenodo. The model reduces encoding probability from 0.855 to 0.122, an 85.8 percent reduction, with super-additivity of 12.8 percentage points. All five pre-registered hypotheses H1 through H5 were confirmed. This work is open-source, fully reproducible, and built entirely outside a formal academic institution. Gitcoin funding rounds support exactly this kind of public-good science. The DeSci track prioritizes transparent, decentralized research that serves communities rather than paywalled journals. My three open-source platforms, IMPRINT for addiction-liability screening, TOPOLOGIX for topological data analysis of drug-protein interactions using persistent homology and bipartite simplicial complexes, and GATE for BCI neural-stimulation safety evaluation, are all on GitHub under an Apache 2.0 license. Each platform addresses a specific gap in computational pharmacology that commercial tools ignore because the target populations are small or the profit margins are thin. The African and Nigerian context is central to this work. Nigeria has an estimated 14.4 million people living with substance use disorders, yet the country has fewer than 50 practicing addiction psychiatrists. Pharmacists like myself are the frontline providers. IMPRINT was designed to run on the hardware available in Lagos community pharmacies: a laptop with 8GB RAM and no GPU. The CCT model proposes a pharmacological intervention that could be deployed through existing primary care channels, not specialized addiction centers that do not exist in most Nigerian states. Endorsements from Kent Berridge at Michigan, Samuel Gershman at Harvard, Nathaniel Daw at Princeton, and Marcelo Mattar at NYU confirm that the theoretical framework is sound. A provisional patent on the CCT core architecture is filed for Q3 2026. A review article is under review at Neuroscience and Biobehavioral Reviews, and a co-authored paper is under review at Alcohol. Gitcoin funding would support continued development of these platforms, specifically the integration of TOPOLOGIX with the CCT simulation pipeline to predict off-target effects of candidate compounds, and the expansion of IMPRINT to include a web-based screening interface for Nigerian pharmacists. The matching pool mechanism of Gitcoin rounds aligns with the collaborative, community-driven model of open-source science. Every commit, issue, and pull request is public. Every preprint is on OSF or Zenodo with a DOI. Transparency is the method of this work. SHORT ESSAY: PROJECT DESCRIPTION Three open-source platforms and one theoretical model form the core of this research programme. IMPRINT is an addiction-liability screening tool that uses pharmacokinetic and pharmacodynamic parameters to estimate the probability that a given compound will produce compulsive use. The algorithm is implemented in Python with scipy and numpy, and the output is a risk score calibrated against published human data. The repository includes a command-line interface and a Supabase-backed PostgreSQL database for storing anonymized screening results. TOPOLOGIX applies topological data analysis to drug-protein interaction networks. Persistent homology is computed using Ripser and Gudhi on bipartite simplicial complexes constructed from binding affinity matrices. The minimum viable product screens for hERG cardiotoxicity, a major cause of drug attrition. The tool identifies topological features that correlate with torsadogenic risk, providing a geometric complement to standard QSAR models. GATE evaluates safety parameters for BCI neural-stimulation protocols. Built in Python with NEURON and Brian2, it simulates electric field distributions and neuronal firing patterns under user-defined stimulation parameters. The Apache 2.0 license allows unrestricted use by researchers and clinicians. The CCT model provides the theoretical foundation. It specifies a conjunctive consolidation threshold: a critical level of coordinated activity across dopamine, glutamate, and opioid systems below which reward-memory encoding is prevented. The mathematical specification defines the threshold as a function of three state variables, and the Bayesian population dynamics paper proposes a clinical trial architecture to test the model in humans. All code is on GitHub at github.com/AmunRaPtah. All preprints are on OSF and Zenodo with DOIs. The project is designed for independent verification and community contribution. SHORT ESSAY: COMMUNITY IMPACT AND PUBLIC GOODS Addiction research suffers from a reproducibility crisis driven by small sample sizes, undisclosed analytical flexibility, and proprietary data. Gitcoin's mission to fund public goods directly addresses this problem. My platforms and model are public goods by design: open-source code, open-access preprints, pre-registered hypotheses, and fully documented simulation pipelines. The primary community served is addiction researchers and clinicians in low-resource settings. Nigeria has no national addiction research institute, no centralized database of substance use patterns, and no regulatory framework for computational screening tools. IMPRINT and the CCT model fill these gaps with tools that require no institutional affiliation to use. A pharmacist in Kano or a researcher in Kampala can download the code, run the simulations, and contribute findings back to the repository. The secondary community is the DeSci ecosystem itself. Gitcoin rounds create a funding mechanism that bypasses traditional grant review, which systematically disadvantages researchers from African institutions. My application demonstrates that independent researchers can produce rigorous, peer-validated work without a university affiliation. The endorsements from four leading computational neuroscientists, Berridge, Gershman, Daw, and Mattar, provide external validation that the work meets academic standards. The provisional patent on the CCT core architecture is filed under a non-exclusive license, meaning the model remains available for non-commercial research while allowing potential commercial development through a transparent licensing structure. This balances open access with the practical need to fund continued development. CHECKLIST - [ ] Create Gitcoin account and complete profile with ORCID and GitHub links - [ ] Verify GitHub repositories (IMPRINT, TOPOLOGIX, GATE) are public with README files and license - [ ] Add OSF and Zenodo preprint DOIs to application - [ ] Upload provisional patent filing number and date - [ ] Confirm eligibility for current DeSci round on Gitcoin - [ ] Prepare 2-3 minute video demonstration of one platform (recommended for Gitcoin rounds) - [ ] Notify endorsers (Berridge, Gershman, Daw, Mattar) that they may be contacted for verification - [ ] Set up Gitcoin grant page with project description, budget, and milestones - [ ] Verify that all code dependencies are listed and installation instructions are current - [ ] Confirm that the application falls within the matching pool category (DeSci) EDITOR NOTES - Eligibility risk: Gitcoin rounds often require a minimum number of GitHub stars, forks, or contributors. Eniola's repositories are solo-authored. Check specific round criteria and consider recruiting a collaborator to submit a pull request before applying. - Fact verification: The provisional patent filing is listed as Q3 2026. Confirm the exact filing date and number before including in application. If not yet filed, remove the claim. - Gap: The profile does not specify Eniola's Gitcoin username or previous participation in Gitcoin rounds. If this is a first application, state that explicitly to avoid appearing as a repeat applicant with no track record. - Gap: The budget for requested funding is not specified. Gitcoin rounds typically ask for a target amount and a breakdown. Eniola should prepare a simple budget (e.g., compute time, software licensing, stipend for 6 months of development). - Gap: The profile mentions "co-authored paper in Alcohol (Elsevier, under review)" but does not name the co-authors or Eniola's specific contribution. Clarify this before submission, as Gitcoin reviewers may ask.