MEDIUM confidence
Researched 2026-07-28 12:40 · profile: researcher
Programme Thesis
The Merck Neuroscience / AI Grant 2026 funds innovative research at the intersection of neuroscience and artificial intelligence, aiming to accelerate CNS drug development by supporting projects that apply AI/ML to neuropharmacology, target discovery, or translational biomarkers. The programme exists to leverage Merck's expertise in both neuroscience and AI to de-risk early-stage drug discovery and foster open innovation with external researchers.
Selection Criteria
- Scientific excellence and novelty of the proposed research
- Relevance to CNS drug development and alignment with Merck's neuroscience/AI focus
- Feasibility of the work plan and methodology
- Applicant's track record and expertise in both neuroscience and AI/computational methods
- Potential for translational impact and scalability
- Clarity of budget justification and value for money (up to €500,000)
- Eligibility: early-career to established researchers; independent or affiliated; LMIC applicants welcome
Past Winners / Cohort Profiles
Past winners of Merck Research Grants (including the In Silico Cup and earlier neuroscience rounds) typically include academic groups with strong computational biology or AI-driven drug discovery portfolios. Examples include teams from European universities (e.g., TU Munich, University of Cambridge) working on deep learning for target identification or predictive toxicology. Profiles often feature a mix of wet-lab validation and dry-lab modeling, with a clear path to preclinical application. No specific named winners for the 2026 Neuroscience/AI track are publicly listed yet.
Ideal Candidate Fingerprint
The platonic ideal applicant is a mid-career researcher (postdoc or early PI) with a dual background in neuroscience and AI/ML, a strong publication record in both fields, and a concrete, well-scoped project that directly addresses a bottleneck in CNS drug development (e.g., target discovery, patient stratification, or predictive pharmacology). They have preliminary data, a clear computational pipeline, and ideally a collaboration with a wet-lab partner for validation.
Recommended Framing
Eniola should frame the application around the TOPOLOGIX platform as a scalable AI tool for predicting drug-resistance mutations in CNS targets (e.g., GPCRs or ion channels relevant to addiction or neurodegeneration), not as a basic neuroscience project. The CCT model can be positioned as a translational biomarker framework for addiction pharmacotherapy, using dynamical-systems AI to predict treatment response. Emphasize the platform architecture (ESM-2 + RF, Bayesian calibration, open-source) and its potential to de-risk Merck's CNS pipeline by identifying resistance early, leveraging Eniola's independent track record and LMIC perspective to highlight underserved patient populations.
Watch Out
Eniola is an independent researcher without a formal academic affiliation (though enrolled in an MSc), which may raise concerns about institutional support and lab infrastructure for wet-lab validation. The programme may prioritize established PIs or groups with a track record of funded grants. Additionally, the CCT model is purely computational with no experimental validation, which could be seen as high-risk. Mitigation: propose a collaboration with a wet-lab partner (e.g., Berridge or Gershman) and emphasize the pre-registered, reproducible methodology.