# AI for Grant Writing — Build a Verified Biomedical Grant Backbone > **Reviewed:** 2026-05-28 > **Canonical HTML:** https://bioskepsis.ai/use-cases/ai-for-grant-writing > **Publisher:** BioSkepsis (EFEVRE TECH LTD, Larnaca, Cyprus) ## Goal Construct a full grant proposal backbone — from State of the Art through to lab-ready methodology — by sequentially activating every BioSkepsis generation feature. Each step produces a discrete artefact that maps directly to a grant section reviewers expect. ## Built for biomedical researchers writing grants - **Postdocs preparing K99/R00 and R01:** save weeks of literature review during the proposal window. Generate State of the Art, preliminary data rationale, and Specific Aims with traceable citations. - **Principal Investigators:** delegate the literature backbone of your next R01, ERC Starting/Consolidator, or Horizon Europe submission to a verified AI workflow. - **Grant writers and consultants:** scale your biomedical grant practice with verified citations and reproducible workflow steps that map to NIH/ERC structure. ## The 7-step workflow — broad query to lab-ready protocol ### Step 1 — Enter a detailed, multi-part research question Structure the query to cover the scope a grant reviewer expects: underlying biological or biophysical principles, current status of key targets or interventions, clinical trial updates, mechanisms of action, and the competitive landscape. The more specific and multi-dimensional the initial question, the richer the outputs. **Example query** > "Provide a detailed review of biomolecular condensate modulators (c-mods) as drug targets. Specifically, focus on: 1. The biophysical principles of liquid-liquid phase separation (LLPS) being leveraged for drug discovery. 2. Current status of 'undruggable' targets like β-catenin, MYC, and TDP-43. 3. Clinical trial updates for lead candidates. 4. Mechanisms of action for c-mods, including condensate dissolution, induction, and reprogramming. 5. Major biotech players and their pharmaceutical partnerships." ### Step 2 — Review the initial verified literature review → State of the Art BioSkepsis returns a synthesis covering the foundational science, the status of each target or intervention, and clinical-stage candidates. Critically, it reports **"NR" (not reported)** for entities it cannot find in the corpus rather than fabricating information. Review the **unverified citations panel** for flagged references. ### Step 3 — Request the Research Landscape Synthesis → Background & Significance Ask BioSkepsis to generate a cohesive narrative overview. This produces: - A **temporal evolution** of the field in distinct phases - **Network analysis** (hubs, bridges, replication patterns) - An **assessment of biases** (including measurement artefacts) - An **evaluation of translational significance** **Example query** > "Generate a cohesive Research Landscape Synthesis" ### Step 4 — Request the Mechanistic Links Table → Preliminary Data Rationale Ask BioSkepsis to generate a mechanistic links table. This produces a structured multi-row table where each row is a distinct molecular interaction — factor, link type, target, effect direction, biological context, and verified PMID. Each row functions as a line of preliminary evidence in the grant application. **Example query** > "Generate a mechanistic links table" ### Step 5 — Request a testable hypothesis → Specific Aims Ask BioSkepsis to generate an empirically testable hypothesis. The output includes a mechanistic rationale chain, explicit predictions, a proposed study design, confounders and controls, risks and limitations, and falsification criteria. The system flags unverified citations within the hypothesis itself, transparently marking where the proposed work extends beyond existing evidence. **Example query** > "Generate an empirically testable hypothesis" ### Step 6 — Request the experimental methodology → Research Plan Ask BioSkepsis to generate a detailed, lab-ready protocol that tests the hypothesis from Step 5. The output covers design and model system justification, sample size and power analysis, interventions and assays, controls and replicates, endpoints with Go/No-Go thresholds, statistical analysis plan, confounder handling, risks and limitations, bioethics and QC. Unverified citations here typically reflect the fact that the proposed combination of reagents and model systems is novel — which is exactly what a grant proposal should demonstrate. **Example query** > "Generate a detailed, lab-ready experimental methodology that tests the above hypothesis." ### Step 7 — Request knowledge gaps and a wet-lab workflow → Future Directions Ask BioSkepsis to identify current knowledge gaps from the corpus and propose a multi-stage wet-lab workflow for addressing them. **Example query** > "Define the current knowledge gaps and suggest a wet-lab workflow for solving them." ## What you walk away with - **Grant-ready State of the Art** — PubMed-grounded citations and explicit "NR" flags where evidence is missing - **Preliminary data rationale** — each mechanistic link as a verified row with PMID - **Falsifiable Specific Aims** — hypothesis with mechanistic rationale, predictions, controls, and falsification criteria - **Lab-ready Research Plan** — methodology with sample size, Go/No-Go thresholds, and statistical plan ## Try the live workflow Pre-loaded BioSkepsis session built around biomolecular condensate modulators as drug targets: **https://app.bioskepsis.ai/research/ai-grant-writing-literature-reviews-use-case-biomolecular/ilzFKe9ZEj-miUlxFO43rg** ## Honest limits - **AI does not replace PI judgement.** Specific Aims, methodology choice, and budget remain the PI's decisions. AI compresses the literature backbone; you write the science. - **AI cannot retrieve what is not indexed.** Grey literature, conference proceedings, unpublished reagents, and personal communications remain manual to include. - **AI does not guarantee funding.** Reviewer preferences, institutional context, and political/strategic positioning of your aims are human craft. - **Final review remains human.** Every claim in your grant should be verified against the cited source before submission. ## FAQ **Which biomedical grants does BioSkepsis support?** NIH (R01, R21, K99/R00, U01), NSF biology programs, ERC (Starting, Consolidator, Advanced, Synergy), Horizon Europe Health cluster, Wellcome Trust biomedical grants, MRC, and equivalent national funders. **How does BioSkepsis avoid hallucinated citations in a grant?** Every citation is grounded in the 40M+ paper biomedical corpus and verified at generation time. Unverified or out-of-corpus references are flagged in a separate panel with diagnostics. Entities not found in the corpus are reported as "NR" (not reported) — never fabricated. **Can I use BioSkepsis output directly in my grant submission?** Yes — the output is structured to map to NIH/ERC sections and includes verified PMIDs. Most users edit and adapt the text to match their voice; the citations and structure can be used as-is. **Is the grant workflow reproducible across multiple proposals?** Yes. Each step is a feature you can run on any biomedical topic to produce the equivalent section. **Does BioSkepsis cover preclinical and translational biomedical research?** Yes — the corpus spans PubMed + biorxiv + medrxiv. Coverage includes molecular biology, biochemistry, pharmacology, structural biology, drug discovery, immunology, neuroscience, oncology, and clinical translational research. ## Related - [Use Case: AI for Drug Repurposing](/use-cases/ai-for-drug-repurposing) - [Use Case: AI for Precision Medicine](/use-cases/ai-for-precision-medicine) - [Blog: How to Start Your Grant Proposal Writing](/blog/ai-to-start-your-grant-writing-proposal) --- BioSkepsis is a product of EFEVRE TECH LTD (Larnaca, Cyprus). Third-party funder and programme names ("NIH", "R01", "K99/R00", "ERC", "Horizon Europe", "Wellcome Trust", "MRC", "NSF") are referenced solely for identification and comparison under the doctrine of nominative fair use.