# AI for Literature Gap Analysis — Evaluate a Therapeutic Modality End-to-End > **Reviewed:** 2026-05-29 > **Canonical HTML:** https://bioskepsis.ai/use-cases/ai-for-literature-gap-analysis > **Publisher:** BioSkepsis (EFEVRE TECH LTD, Larnaca, Cyprus) ## Goal Comprehensively evaluate an emerging therapeutic modality or drug platform — covering its mechanisms, clinical pipeline, design challenges, resistance landscape, expansion potential beyond the lead indication, and unresolved questions — to inform R&D portfolio decisions, investment due diligence, or review articles. The workflow ends by mapping the frontier: where the evidence is weakest, where it contradicts itself, and which open questions matter most. ## Built for portfolio review, due diligence, and review articles - **R&D portfolio reviewers:** judge whether an emerging modality is worth a program before resources are committed. - **Investment & due-diligence analysts:** map a platform's breadth, durability, and open risks before investing. - **Review-article & thesis authors:** build a comprehensive, gap-aware evidence base across the whole modality. ## The 8-step workflow — modality overview to prioritised gap map ### Step 1 — Enter a comprehensive research question covering mechanisms, clinical status, and design challenges Frame the question to span the full breadth a portfolio reviewer or field expert would need: how the technology works, where it stands in clinical development, and what barriers remain unsolved. **Example query** > "What are the current mechanisms, clinical progress, and design challenges of targeted protein degradation therapeutics, including PROTACs and molecular glue degraders, across oncology and emerging non-oncology indications?" ### Step 2 — Review the initial AI synthesis, then re-run the query as a deep research request BioSkepsis first returns a structured overview. If the initial synthesis is broad but shallow (as is common with modality-wide questions), re-submit the same question with a "do a deep research" instruction to trigger a more thorough literature pull with full citation verification. Compare the two outputs — the deep version will have more PMIDs, more granular confidence ratings, and a more complete unverified citations panel. **Example query** > "do a deep research — What are the current mechanisms, clinical progress, and design challenges of targeted protein degradation therapeutics, including PROTACs and molecular glue degraders, across oncology and emerging non-oncology indications?" ### Step 3 — Request the Research Landscape Synthesis Ask BioSkepsis to generate a narrative overview of the modality's evolution. This produces a temporal map of the field's phases (proof-of-concept, mechanistic optimisation, clinical translation), the network structure (which E3 ligases, targets, or platforms act as hubs and bridges), replication patterns and evidence maturity for each sub-claim, and an assessment of biases (e.g., over-representation of oncology targets, recency effects). **Example query** > "Generate a cohesive Research Landscape Synthesis" ### Step 4 — Map the mechanistic landscape and clinical pipeline side by side Ask which specific approaches exist within the modality, how they differ mechanistically, and which have reached clinical trials with reported efficacy data. This produces a structured overview linking mechanism to therapeutic candidate to trial phase. **Example query** > "What are the main approaches to targeted protein degradation, how do they differ mechanistically, and which have reached clinical trials with reported efficacy data?" ### Step 5 — Drill into the biophysical and pharmacological determinants of in vivo efficacy Ask what structural, biophysical, and pharmacological factors determine whether a candidate in this modality will actually work in patients — and what translational barriers have limited clinical progress so far. **Example query** > "What structural, biophysical, and pharmacological factors determine whether a protein degrader will be effective in vivo, and what are the main translational challenges that have limited clinical progress?" ### Step 6 — Probe the resistance and failure landscape Ask whether there is evidence that the modality's therapeutic effect can be evaded, and through what mechanisms. This surfaces the durability risk that differentiates a validated modality from a one-cycle therapy. **Example query** > "Is there evidence that cells develop resistance to protein degradation therapies, and if so, through what mechanisms?" ### Step 7 — Assess expansion potential beyond the lead indication Ask where else the modality has shown promise and what criteria from the literature would guide whether it offers an advantage over conventional approaches for a given target. This tests the breadth of the platform's applicability. **Example query** > "Beyond the most studied oncology targets, where else has targeted protein degradation shown promise, and what criteria from the literature would guide whether degradation offers an advantage over conventional inhibition for a given target?" ### Step 8 — Identify the weakest evidence, contradictions, and highest-impact unresolved questions Ask where the published evidence is thinnest, where findings contradict each other, and which unanswered questions would have the greatest impact if resolved. This closes the assessment by mapping the frontier. **Example query** > "Where does the published evidence in this field remain weakest or most contradictory, and which unresolved questions would have the highest impact if answered?" ## What you walk away with - **Modality-wide overview** — mechanisms, clinical pipeline, and design challenges mapped together, with a deep-research pass for full citation verification - **Temporal & network landscape** — the field's phases, hub targets and platforms, and evidence maturity for each sub-claim - **Resistance & durability read** — whether and how the therapeutic effect can be evaded - **Prioritised gap map** — the weakest and most contradictory evidence, plus the highest-impact unresolved questions ## Try the live workflow Pre-loaded BioSkepsis session evaluating targeted protein degradation end to end: **https://app.bioskepsis.ai/research/are-current-mechanisms-clinical-progress-and-design/HB3EIe_iNeesKQd7qlS-Cg** ## Honest limits - **"Gaps" are gaps in the published literature BioSkepsis can see.** They are not proof that nothing exists in unpublished, confidential, or unindexed work. - **Deep research is more thorough but still bounded.** It is limited by what is indexed; recency effects and over-representation (e.g., oncology) are surfaced and named, not erased. - **Confidence ratings reflect replication in the corpus, not a guarantee.** A high rating means the evidence is well-replicated here, not that it is settled science. - **A gap worth a grant or review still needs expert framing.** BioSkepsis points to the frontier; you decide which gap is worth pursuing and how. ## FAQ **How is this different from the Literature Gap Finder feature?** The Literature Gap Finder feature is built to surface a single underexplored question fast. This use case is a full, multi-step modality assessment that ends in a gap map of the weakest, most contradictory, and highest-impact open questions. Use the feature to spot a gap quickly; use this workflow for a portfolio- or review-grade evaluation. **What does adding "do a deep research" to the query change?** For broad, modality-wide questions the first synthesis can be wide but shallow. Re-submitting with a "do a deep research" instruction triggers a more thorough literature pull with full citation verification — more PMIDs, more granular confidence ratings, and a more complete unverified citations panel. **What is the Research Landscape Synthesis?** A generated narrative overview of how a modality evolved: a temporal map of its phases, the network structure of hub targets and platforms, replication patterns and evidence maturity, and an assessment of biases such as oncology over-representation or recency effects. **Can I use the output for a review article or portfolio memo?** Yes — the output maps into a review article's sections or an R&D portfolio assessment, with verified PMIDs. Final framing and interpretation remain yours. **Does it cover preclinical, clinical, and resistance literature?** Yes. The corpus spans preclinical mechanistic studies, biophysical and pharmacological work, clinical-trial reports, and resistance/failure literature across a 40M+ paper biomedical corpus. ## Related - [Use Case: AI for Biomedical Thesis Writing](/use-cases/ai-for-thesis-writing) - [Use Case: AI for Drug Target Validation](/use-cases/ai-for-drug-target-validation) - [Use Case: AI for Pharma Competitive Intelligence](/use-cases/ai-for-competitive-intelligence-pharma) - [Feature: AI Literature Gap Finder](/features/literature-gap-finder) --- BioSkepsis is a product of EFEVRE TECH LTD (Larnaca, Cyprus). Third-party product and method names are referenced for identification only under the doctrine of nominative fair use.