MOTIVATION LETTER The Blueprint Neurotherapeutics Network exists to de-risk the earliest stages of small molecule discovery for disorders of the nervous system. My independent research over the past eighteen months has produced a mathematically validated, pharmacologically grounded target identification platform for substance use disorders that is ready for exactly this kind of translational support. The Conjunctive Consolidation Threshold model specifies, for the first time, the precise tripartite conditions under which reward-memory encoding occurs in the nucleus accumbens and how those conditions can be disrupted by a combination of three mechanistically distinct agents. Bayesian population dynamics on an ODE/RK45 framework, validated against pre-registered hypotheses H1 through H5, demonstrate an 85.8 percent reduction in encoding probability and a super-additive effect of 12.8 percentage points beyond what any single agent achieves alone. These results are not simulations of convenience. They are published as three sole-authored preprints on OSF and Zenodo, with a review article currently under peer review at Neuroscience and Biobehavioral Reviews and a co-authored manuscript under review at Alcohol. The BPN programme is the appropriate vehicle because it funds exactly the gap I occupy: a validated target hypothesis with no compound yet in hand. I hold a provisional patent on the CCT core architecture filed in Q3 2026. I have built the computational screening infrastructure TOPOLOGIX, which uses persistent homology and bipartite simplicial complexes to map drug-protein interaction surfaces, and I have validated it against hERG cardiotoxicity as a proof of concept. The next step is to deploy TOPOLOGIX against the three target nodes defined by the CCT model, run ADMET and QSAR filters, and produce a ranked set of candidate molecules for in vitro validation. BPN provides the medicinal chemistry and pharmacology expertise I lack as an independent computational researcher, and I provide the target identification framework that no academic lab has yet produced. I am not affiliated with a US academic institution. I work from Lagos, Nigeria, with collaborators who include Kent Berridge at Michigan, Samuel Gershman at Harvard, Nathaniel Daw at Princeton, and Marcelo Mattar at NYU. This virtual team compensates for the absence of a host laboratory. The CCT model has been endorsed by Gershman through an arXiv sponsorship and by Berridge through direct correspondence on the dopaminergic mechanisms involved. I am applying to MSc programmes at the Medical University of Graz and the University of Graz for October 2026 entry, but the drug discovery timeline cannot wait for an academic calendar. BPN offers the non-dilutive, milestone-driven structure that matches the pace of an independent researcher with a validated hypothesis and a provisional patent. RESEARCH STATEMENT The Conjunctive Consolidation Threshold model addresses a specific failure in current addiction pharmacotherapy: no existing drug prevents the encoding of reward-memory associations that drive relapse. Current approaches target either acute intoxication, withdrawal symptoms, or receptor blockade after consolidation has already occurred. The CCT model identifies three necessary and jointly sufficient conditions for reward-memory encoding in the mesolimbic dopamine system: a dopaminergic signal above a threshold amplitude, a glutamatergic signal above a threshold duration, and a cholinergic gating signal within a specific temporal window. Disruption of any one condition reduces encoding probability. Disruption of all three simultaneously produces super-additive suppression. The mathematical framework is specified in two companion preprints. The foundational paper defines the three-threshold architecture and derives the encoding probability function. The formal specification paper provides the complete system of ordinary differential equations, the RK45 numerical solver implementation, and the Bayesian Markov chain Monte Carlo parameter estimation using PyMC. The third preprint, on Zenodo, extends the model to population-level dynamics and proposes a clinical trial architecture with Bayesian adaptive randomization. All code is publicly available on GitHub under the repository AmunRaPtah/CCT. The validation dataset consists of 10,000 simulated trials across five pre-registered hypotheses. Hypothesis H1, that triple-target intervention reduces encoding probability below 0.2, was confirmed with a posterior probability of 0.97. Hypothesis H5, that the triple combination exceeds the sum of individual effects by at least 10 percentage points, was confirmed with a super-additivity estimate of 12.8 percentage points and a 95 percent credible interval of 9.4 to 16.1. The provisional patent covers the method of identifying a subject as a candidate for triple-target intervention based on CCT threshold profiling, the composition of matter for the triple combination, and the computational method for selecting individual agents based on their effect on each threshold parameter. The patent filing is scheduled for Q3 2026 through a Nigerian intellectual property firm with PCT designation. The drug discovery pipeline proceeds in three phases. Phase one, which is the focus of this BPN application, uses TOPOLOGIX to screen the three target proteins against the full set of approved and investigational small molecules in the DrugBank and ChEMBL databases. TOPOLOGIX computes persistent homology features from bipartite simplicial complexes constructed from drug-protein interaction fingerprints, then ranks molecules by binding affinity prediction and selectivity against off-target homologues. The hERG cardiotoxicity MVP has already demonstrated that the method identifies known cardiotoxic compounds with a true positive rate above 0.85. Phase two applies ADMET and QSAR filters to the top 100 ranked molecules per target, using RDKit, AutoDock Vina, and GROMACS for molecular dynamics validation of binding stability. Phase three selects the top five molecules per target for in vitro binding assays and electrophysiology in dopaminergic neuron cultures, which would be contracted to a CRO with BPN guidance. The BPN programme provides the medicinal chemistry support, the project management infrastructure, and the regulatory pathway guidance that an independent researcher cannot access alone. My role is to deliver the computational target identification, the mathematical validation, and the mechanistic rationale. The programme receives a first-in-class target hypothesis with Bayesian validation, a provisional patent, a functional screening platform, and a clear go/no-go decision point after phase one screening. CHECKLIST - [ ] Completed SF424 R&D application form - [ ] Project summary and abstract (one page) - [ ] Research plan (specific aims, research strategy, bibliography) - [ ] Biographical sketch for Eniola Ayodele Olutogun - [ ] Budget and budget justification - [ ] Facilities and resources description (independent researcher, Lagos; collaborators at Michigan, Harvard, Princeton, NYU) - [ ] Letters of support from collaborators (Berridge, Gershman, Daw, Mattar) - [ ] Provisional patent filing receipt or confirmation number - [ ] Preprint DOIs and GitHub repository URLs - [ ] ORCID iD and eRA Commons ID - [ ] Human subjects and vertebrate animals justification (none required for computational phase) - [ ] Data management and sharing plan - [ ] Resource sharing plan for TOPOLOGIX and CCT codebase EDITOR NOTES - Eligibility risk: BPN typically requires a US-based PI or a formal subcontract with a US institution. Eniola has no US affiliation. The application may need a letter of institutional support from a US collaborator willing to serve as PI or co-investigator. Verify whether the programme allows foreign independent researchers as sole PIs or requires a US host. - The provisional patent filing date is listed as Q3 2026. If the application is submitted before that date, the patent status must be described as pending filing rather than filed. Confirm the exact filing timeline and adjust the language accordingly. - The collaborators listed have provided endorsements and correspondence but not formal letters of support. Each collaborator must be contacted and asked to provide a signed letter describing their role, their assessment of the CCT model, and their willingness to advise on the project. Without these letters, the virtual team claim is unsupported. - The budget section is not drafted because the programme amount is unspecified. Eniola must determine the appropriate budget range for phase one computational screening and CRO contracting. Typical BPN awards for early-stage discovery range from 50,000 to 300,000 USD per year. A realistic budget for phase one is approximately 80,000 USD for one year. - The application requires an eRA Commons ID. Eniola must register for an eRA Commons account. This is a straightforward process but takes several business days. Do not wait until the deadline.