MOTIVATION LETTER BIO-Europe Spring Startup Spotlight selects companies developing new therapeutic solutions, technology, or digital platforms. My venture, TopoLogix Bio, fits this mandate precisely. TopoLogix Bio is a computational drug-resistance prediction platform built on a protein-language-model foundation. The core technology, TOPOLOGIX, uses ESM-2 protein-language-model delta-embeddings combined with Morgan/ECFP drug fingerprints and a Random Forest classifier to predict drug-resistance mutations from sequence alone. On the Platinum benchmark of 553 mutations, TOPOLOGIX achieves an AUROC of 0.804 plus or minus 0.025. On SKEMPI 2.0, it reaches 0.634. These numbers beat structure-based baselines such as mCSM-lig, which scores approximately 0.70, while covering 100 percent of mutations compared to roughly 18 percent for structure-limited tools. The innovation is not incremental. I discovered that interface topology, measured through bipartite persistent homology, carries almost no signal for drug-resistance prediction. My pre-registered study on the Platinum benchmark returned AUROC values of 0.425 and 0.485, ruling out interface geometry as the driver. This negative result, reported directly rather than reframed, motivated a sequence-representation approach that now outperforms the field. TopoLogix Bio is the vehicle to deploy this method as a platform for pharmaceutical companies and clinical researchers. The commercial potential is clear. Drug resistance costs the global health system billions annually in failed therapies and extended hospital stays. A platform that predicts resistance mutations from sequence alone, without requiring protein structures that often do not exist, reduces preclinical failure rates and accelerates target validation. The platform targets CNS and oncology programs where resistance is the primary cause of attrition. The Africa angle strengthens the proposition. Nigeria and sub-Saharan Africa carry a disproportionate burden of infectious disease resistance, yet local researchers lack computational tools calibrated to their genomic data. TopoLogix Bio will build an Africa-specific resistance database and prediction layer, starting with antimalarial and antitubercular compounds. This differentiates the venture from competitors focused exclusively on Western oncology pipelines. TopoLogix Bio is a pre-revenue, pre-seed startup incubated over the past 18 months. I am the sole founder, with a B.Pharm from the University of Ibadan and current enrollment in the M.Sc. Digital Health program at Hasso Plattner Institute in Potsdam, Germany. My computational pharmacology research has been endorsed by Kent Berridge at Michigan, Samuel Gershman at Harvard, Nathaniel Daw at Princeton, and Marcelo Mattar at NYU. I have built four independent DuckDB-based ingest-to-analyze pipelines and self-hosted production systems for local LLM serving. The technical foundation is ready. The Startup Spotlight jury at BIO-Europe Spring in Lisbon will see a platform that solves a real problem, outperforms existing tools, and has a clear path to market. SHORT ESSAY: INNOVATION AND COMMERCIAL POTENTIAL TOPOLOGIX solves a specific problem that structure-based tools cannot touch. Approximately 82 percent of protein-ligand complexes in the Protein Data Bank lack a co-crystallized structure for the mutant form. Tools like mCSM-lig require that structure, so they simply cannot score most resistance mutations. TOPOLOGIX requires only the wild-type sequence and the drug fingerprint. This coverage advantage is not theoretical. On the Platinum benchmark, TOPOLOGIX scores every one of 553 mutations. mCSM-lig scores fewer than 100. The method is computationally light. A single mutation prediction runs in under two seconds on a standard laptop. The Random Forest classifier, trained on ESM-2 embeddings and Morgan fingerprints, generalizes across protein families and drug classes. I have validated this on two independent benchmarks with consistent results. Commercial potential flows from this coverage and speed. A pharmaceutical company running a preclinical resistance screen on a new chemical entity currently waits days for structure-based predictions on a subset of mutations. TOPOLOGIX delivers genome-wide predictions in minutes. The addressable market includes every CNS, oncology, and infectious disease program where resistance limits therapeutic lifespan. I will target early-stage discovery teams first, offering a subscription-based API with a per-mutation pricing model. Later phases include a clinical decision-support module for selecting second-line therapies based on predicted resistance profiles. SHORT ESSAY: AFRICA AND GLOBAL HEALTH ANGLE Nigeria has the third-highest burden of multidrug-resistant tuberculosis globally. Artemisinin-resistant malaria parasites have been confirmed in the Greater Mekong Subregion and are spreading. African genomic surveillance programs, including the one I worked on at GHRU-GSAR, generate sequence data faster than local researchers can analyze it for resistance markers. TOPOLOGIX closes this gap. The platform will be deployed first on antimalarial and antitubercular compounds, using publicly available resistance mutation databases and African genomic surveillance data. I will build a Nigeria-specific resistance prediction layer calibrated to local allele frequencies. This layer will be made available to African research institutions at reduced cost or through a freemium model funded by pharmaceutical partnerships. The global health impact is measurable. Faster identification of resistance mutations means faster deployment of alternative therapies. For malaria, where artemisinin combination therapies are the frontline treatment, a two-week delay in detecting resistance can translate to thousands of excess cases. TOPOLOGIX reduces that delay to hours. CHECKLIST - [ ] Complete BIO-Europe Spring 2026 Startup Spotlight application form on the Informa Connect website - [ ] Prepare 6-minute pitch presentation with slides covering problem, technology, validation, commercial model, and Africa angle - [ ] Upload one-page executive summary of TopoLogix Bio (company overview, technology description, benchmark results, team, funding status) - [ ] Provide proof of company registration or incubation status (company created within past three years or innovation incubated less than or equal to three years) - [ ] Confirm aggregate funding is less than or equal to 10 million euros and fewer than 25 employees - [ ] Prepare video or live pitch for jury evaluation at BIO-Europe Spring in Lisbon - [ ] Submit by programme deadline listed on the BIO-Europe Spring website EDITOR NOTES - Eligibility risk: The programme requires a company created in the past three years or an innovation incubated for three years or less. Eniola has not registered a company yet. He should register TopoLogix Bio as a legal entity (e.g., a Nigerian or German GmbH) before the application deadline. If registration is not possible, he must document the incubation start date (e.g., first TOPOLOGIX preprint or GitHub commit) to prove the innovation is less than three years old. - Fact to verify: The Platinum benchmark AUROC of 0.804 plus or minus 0.025 and SKEMPI 2.0 AUROC of 0.634 are from the profile. Eniola should confirm these numbers are from a pre-print or published paper and cite the source in the application. - Gap to fill: The profile does not state whether Eniola has any co-founders, advisors, or team members. The programme requires fewer than 25 employees. If he is the sole founder, he should state this clearly. If he has collaborators who will join the venture, he should name them and describe their roles. - Personal detail needed: The application may ask for a brief bio or founder background. Eniola should prepare a two-sentence summary of his qualifications: B.Pharm from University of Ibadan, M.Sc. Digital Health candidate at Hasso Plattner Institute, independent computational researcher with publications in addiction neuroscience and protein ML. - Funding status: The profile does not mention any funding received. The programme requires aggregate funding less than or equal to 10 million euros. Eniola should confirm he has received no external funding or, if he has, state the amount and source.