MOTIVATION LETTER Addiction psychiatry has a measurement problem. The field can describe reward-memory encoding at the molecular level and at the behavioral level, but the bridge between those scales is built from narrative inference, not mathematics. My CCT model, the Conjunctive Consolidation Threshold, is an attempt to build that bridge. It is a tripartite pharmacological framework that couples dopaminergic reward prediction error, NMDAR-dependent long-term potentiation, and affective contrast into a single system of ordinary differential equations, solved with RK45 and calibrated with Bayesian MCMC using PyMC's DEMetropolisZ sampler across 14 free parameters. The priors were elicited from a systematic screen of 1,847 records in the addiction neuroscience literature. All five pre-registered hypotheses, H1 through H5, were confirmed, with posterior super-additivity of 13 to 22 percentage points across model versions. Three sole-authored preprints are under review at peer-reviewed journals: IART, PNPBP, and NBR. A co-authored paper is under review at Alcohol, Elsevier. I am applying to the IMPRS-TP doctoral program in translational psychiatry because the program's stated mission, to bridge basic neuroscience and clinical application, is precisely the gap my work occupies. The CCT model makes a falsifiable prediction: that pharmacological intervention at any of the three coupled axes, dopaminergic RPE, NMDAR-dependent LTP, or affective contrast, should reduce the probability of reward-memory consolidation below a threshold, and that combined intervention should produce super-additive effects. That prediction is testable in human subjects with existing compounds. I want to design those experiments. My computational toolkit extends beyond the CCT model. I have built neurocascade, a receptor-to-behavior simulation engine that couples pharmacokinetics to receptor binding to Wilson-Cowan circuit dynamics to behavioral readout, calibrated for mu-opioid, D2 dopamine, and GABA-A systems, with 62 of 62 tests passing. I have also run negative-result studies that I consider equally important: a pre-registered, powered replication showing that bipartite persistent homology does not beat a plain descriptor baseline for hERG cardiotoxicity prediction, AUROC 0.8426 versus 0.8782, and a resistance-prediction study showing the same topological constructs carry almost no signal, AUROC 0.425 and 0.485. These results settled comparisons the literature had never actually run. I report failures directly rather than reframing them. The IMPRS-TP selection criteria ask for demonstrated research experience, clear motivation, and fit with at least three research groups. My experience is documented across preprints, peer review, and open-source code. My motivation is specific: I want to move the CCT model from a calibrated in silico framework to a clinically actionable tool for addiction psychiatry. I have identified three groups within IMPRS-TP whose work aligns with my research goals, and I will detail those preferences in the application form. My B.Pharm from the University of Ibadan, CGPA 5.1 of 7.0, German equivalent 1.9, and my enrollment in the M.Sc. Digital Health program at Hasso Plattner Institute and University of Potsdam provide the pharmacological and computational foundation this work requires. RESEARCH STATEMENT The CCT model, Conjunctive Consolidation Threshold, addresses a specific unresolved question in addiction psychiatry: under what conditions does a reward-associated memory become consolidated such that it drives compulsive drug-seeking, and can that consolidation be prevented pharmacologically? The model posits that three coupled systems must conjunctively cross a threshold for consolidation to occur: dopaminergic reward prediction error signaling, NMDAR-dependent long-term potentiation in reward circuitry, and affective contrast, the emotional salience differential between the drug state and the baseline state. No single system is sufficient; the threshold requires all three. The model is implemented as a system of ordinary differential equations solved with RK45. The 14 free parameters were calibrated using Bayesian MCMC with PyMC's DEMetropolisZ sampler. Priors were elicited from a systematic screen of 1,847 records in the addiction neuroscience literature, ensuring that the parameter space reflects published empirical findings rather than arbitrary ranges. All five pre-registered hypotheses, H1 through H5, were confirmed. The key quantitative finding is posterior super-additivity: combined intervention across the three axes produces 13 to 22 percentage points greater reduction in consolidation probability than the sum of individual interventions. This is a conjunctive effect, not an additive one, and it is the model's central claim. The translational implication is direct. If the conjunctive threshold is correct, then combination pharmacotherapy targeting dopaminergic signaling, NMDAR function, and affective processing should outperform single-target interventions in preventing reward-memory consolidation. This is a testable hypothesis in human subjects. Existing compounds exist for each axis. The experimental design would use established cue-reactivity and reinstatement paradigms, with consolidation probability as the primary outcome. My second relevant line of work is neurocascade, a receptor-to-behavior simulation engine. It couples four ODE layers: pharmacokinetics, receptor binding, Wilson-Cowan circuit dynamics, and behavioral readout. Three receptor-circuit systems are calibrated from the literature: mu-opioid, D2 dopamine, and GABA-A. The engine is Bayesian-calibrated with PyMC, and 62 of 62 tests pass. The circuit-layer parameters are explicitly labeled illustrative pending real behavioral-data fits, which is exactly the next step I want to take at IMPRS-TP: fitting neurocascade to human behavioral data from addiction studies. I also bring a track record of rigorous negative results. My pre-registered, powered replication of the claim that bipartite persistent homology predicts hERG cardiotoxicity found that topological features do not beat a plain descriptor baseline, AUROC 0.8426 versus 0.8782. My interface-topology-for-resistance study found the same class of constructs carry almost no signal for drug-resistance prediction, AUROC 0.425 and 0.485 on the Platinum benchmark. These results are published as preprints and are under review. I report them because they demonstrate the methodological discipline that translational psychiatry needs: the willingness to test a hypothesis and report the answer even when the answer is no. The IMPRS-TP program's translational focus is the right environment for the next phase of this work. The CCT model needs empirical validation in human populations. neurocascade needs fitting to real behavioral data. Both require the clinical infrastructure and supervision that a doctoral program in translational psychiatry provides. My B.Pharm, my M.Sc. in Digital Health at HPI and University of Potsdam, and my five years of independent computational research have prepared me for this specific transition from in silico modeling to clinically grounded investigation. ESSAY: RESEARCH EXPERIENCE My research experience spans five years of independent computational work across addiction neuroscience, protein machine learning, and dynamical systems. The central line is the CCT model, a tripartite pharmacological framework for reward-memory encoding prevention in addiction. The model couples dopaminergic reward prediction error, NMDAR-dependent long-term potentiation, and affective contrast into a system of ODEs solved with RK45. I calibrated 14 free parameters using Bayesian MCMC with PyMC's DEMetropolisZ sampler, with priors elicited from a systematic screen of 1,847 records. All five pre-registered hypotheses were confirmed, with posterior super-additivity of 13 to 22 percentage points. Three sole-authored preprints are under review at IART, PNPBP, and NBR; a co-authored paper is under review at Alcohol, Elsevier. My second line is neurocascade, a receptor-to-behavior simulation engine coupling pharmacokinetics, receptor binding, Wilson-Cowan circuit dynamics, and behavioral readout. Three systems are calibrated: mu-opioid, D2 dopamine, and GABA-A. The engine is Bayesian-calibrated with PyMC; 62 of 62 tests pass. Circuit-layer parameters are labeled illustrative pending real behavioral-data fits. I have also conducted rigorous negative-result studies. A pre-registered, powered replication found that bipartite persistent homology does not beat a plain descriptor baseline for hERG cardiotoxicity prediction, AUROC 0.8426 versus 0.8782. A resistance-prediction study found the same topological constructs carry almost no signal, AUROC 0.425 and 0.485 on the Platinum benchmark. These results are under review. My current project, TOPOLOGIX, uses ESM-2 protein-language-model delta-embeddings plus Morgan fingerprints and a Random Forest classifier to predict drug-resistance mutations from sequence alone. It achieves AUROC 0.804 plus or minus 0.025 on the Platinum benchmark, 553 mutations, and 0.634 on SKEMPI 2.0. It beats structure-based baselines such as mCSM-lig at approximately 0.70 while covering 100 percent of mutations versus approximately 18 percent for structure-limited tools. My employment history includes a National Product Manager role at Synthcare, clinical pharmacy at Ramset Pharmacy, research assistance at CDDDP on NMDA and insulin docking, and bioinformatics research at GHRU-GSAR on AMR genomics and surveillance pipelines. I am licensed by the Pharmacists Council of Nigeria. ESSAY: MOTIVATION FOR IMPRS-TP The IMPRS-TP program is the first doctoral program I have found whose stated mission, bridging basic and clinical research in psychiatry, matches the actual structure of my work. The CCT model is not a purely theoretical exercise. It makes a quantitative, falsifiable prediction about combination pharmacotherapy for addiction: that intervention across three coupled axes, dopaminergic RPE, NMDAR-dependent LTP, and affective contrast, should produce super-additive reductions in reward-memory consolidation probability. That prediction needs clinical testing. IMPRS-TP is where I want to design and run those tests. The program's selection criteria emphasize demonstrated research experience, clear motivation, and fit with specific research groups. My research experience is documented in three sole-authored preprints under review, a co-authored paper under review, and open-source code on GitHub. My motivation is specific: I want to fit neurocascade to real human behavioral data from addiction studies, and I want to translate the CCT model's conjunctive threshold into a clinical protocol. I have identified at least three IMPRS-TP research groups whose work aligns with these goals, and I will specify those preferences in the application form. My background is unusual but directly relevant. I hold a B.Pharm from the University of Ibadan, CGPA 5.1 of 7.0, German equivalent 1.9, and I am licensed by the Pharmacists Council of Nigeria. I am enrolled in the M.Sc. Digital Health program at Hasso Plattner Institute and University of Potsdam, starting Winter Semester 2026-27. I have worked as a clinical pharmacist and as a national product manager. I know how drugs are prescribed, how they are dispensed, and how they act at the molecular level. What I need from IMPRS-TP is the clinical research training and supervision to test my models in human populations. I am a Nigerian researcher. The addiction burden in West Africa is understudied and under-resourced. A translational framework validated in Munich can be adapted to Nigerian clinical contexts, and I intend to do that work after my doctoral training. IMPRS-TP's international structure is the right place to build that bridge. CHECKLIST - [ ] Verify the official IMPRS-TP application page at imprs-tp.mpg.de/2879/application; the provided URL may be an aggregator blog, not the official programme page - [ ] Confirm the application deadline from the official IMPRS-TP website - [ ] Confirm eligibility: Master's degree or equivalent must be obtained before PhD start; verify whether current M.Sc. enrollment at HPI satisfies this or if completion is required first - [ ] Obtain two letters of recommendation; confirm referees can submit by the deadline - [ ] Request official transcripts: University of Ibadan B.Pharm, HPI/Potsdam M.Sc. (if available), high school records - [ ] Prepare CV in IMPRS-TP format, including ORCID 0009-0001-9272-6735 and GitHub github.com/AmunRaPtah - [ ] Select at least three IMPRS-TP research groups and write specific research interests for each - [ ] Draft the brief essay on research experience as required by the application form - [ ] Confirm whether the application requires a project proposal or only the motivation essay - [ ] Verify the DAAD funding track: confirm whether this scholarship is tied to IMPRS-TP or is a separate DAAD application - [ ] Prepare for interview process, March to May 2027; rehearse presentation of CCT model and neurocascade - [ ] Confirm whether the application portal requires English proficiency test scores (IELTS/TOEFL) or if prior English-medium education suffices EDITOR NOTES - Research line selected: CCT model and neurocascade, because IMPRS-TP is a translational psychiatry program and the CCT model's conjunctive threshold is directly testable in human addiction studies. The cardiotoxicity and TOPOLOGIX lines are protein-ML work that does not fit this program's mission. The ergofluids line is behind a real-data validation gate and should not be presented as validated. psyche-twin is a personal knowledge-graph project, not translational psychiatry. - Eligibility risk: the applicant is currently enrolled in an M.Sc. at HPI/Potsdam starting Winter 2026-27. IMPRS-TP requires a Master's degree or equivalent before PhD start. If the M.Sc. is not completed before the PhD start date, this is a hard eligibility failure. Verify the program's policy on concurrent enrollment or completion timelines. - The provided URL is a blog aggregator, not the official IMPRS-TP page. The applicant must verify the actual application portal, deadline, and requirements before submitting anything. The DAAD funding track may be a separate application from the IMPRS-TP admission application. - The CCT model's three preprints are under review, not published. The applicant should be prepared to share the OSF/Zenodo preprints and the pre-registration documents with the selection committee. The co-authored paper at Alcohol, Elsevier is also under review; confirm its status before the interview. - The applicant must insert personal details not in this profile: specific names of the three IMPRS-TP research groups and principal investigators they wish to work with, the names and affiliations of their two referees, and any personal circumstances relevant to relocation to Munich for the PhD duration.