AI Prompts for Biotech

Act as a molecular biologist. I am investigating [[DISEASE_NAME]], a [[DISEASE_TYPE]] disorder. My proposed target is [[PROTEIN_NAME]] (UniProt: [[UNIPROT_ID]]). Based on publicly available literature up to your last update, provide a concise summary of the evidence supporting or refuting the association between [[PROTEIN_NAME]] and the pathophysiology of [[DISEASE_NAME]]. Organize the output into the following sections: 1. **Executive Summary (3 sentences):** State the overall strength of the association. 2. **Supporting Evidence:** List key findings from genetic, preclinical, and clinical studies that link the target to the disease. Use bullet points and cite PubMed IDs (PMIDs) where possible. 3. **Contradictory Evidence:** List any findings that challenge the association. 4. **Knowledge Gaps & Future Research:** Identify key unanswered questions.

Act as a drug discovery project lead. Create a target dossier brief for [[GENE_SYMBOL]] in the context of [[THERAPEUTIC_AREA]]. The brief should be a maximum of 500 words and formatted for a slide presentation. Include the following sections: - **Target & Mechanism:** What is the protein and its primary biological function? - **Link to Disease:** What is the core hypothesis linking this target to [[DISEASE_NAME]]? - **Druggability Assessment:** Is this target class (e.g., kinase, GPCR, transcription factor) considered druggable? Are there known binders or existing tool compounds? - **Safety/Toxicity Concerns:** Are there any known liabilities based on the target's expression profile or knockout phenotype data? - **Proposed Next Steps:** What are the 2-3 key experiments needed to validate this target?

My team is considering [[PROTEIN_TARGET]] for [[DISEASE]]. The protein is a [PROTEIN_CLASS, e.g., 'scaffolding protein with no known active site']. Acting as a panel of medicinal chemists and chemical biologists, brainstorm three distinct strategies to drug this "undruggable" target. For each strategy, describe: 1. **The Approach:** (e.g., Molecular Glue, PROTAC, Stabilizer, Allosteric Modulator). 2. **Rationale:** Why is this approach suitable for [[PROTEIN_TARGET]]? 3. **Key Challenges:** What are the primary obstacles for this strategy? 4. **Starting Points:** What initial screens or assays would you propose?

Act as a competitive intelligence analyst for a biotech firm. I am developing a [[MODALITY_TYPE, e.g., 'small molecule inhibitor']] for [[TARGET_NAME]]. Search the public domain (clinical trial registries, press releases, recent publications) and generate a markdown table summarizing all known drug candidates targeting [[TARGET_NAME]]. The table columns should be: - **Company Name** - **Compound Name** (if public) - **Modality** (e.g., Small Molecule, Antibody, ASO) - **Development Phase** (e.g., Preclinical, Phase I, Phase II) - **Most Recent Data Point** (e.g., "Presented at ASCO 2026," "Published in Nature") and the source URL if available.

I am providing abstracts from three review articles on [TOPIC, e.g., 'the role of NLRP3 inflammasome in neuroinflammation']. Abstract 1: [[Paste Abstract 1]] Abstract 2: [[Paste Abstract 2]] Abstract 3: [[Paste Abstract 3]] Synthesize these abstracts into a single, coherent narrative summary of 300 words. Your summary must: 1. Identify the consensus points across all three reviews. 2. Highlight any areas of disagreement or differing emphasis. 3. Extract the key "future outlook" or "unanswered questions" mentioned by the authors.

I have uploaded a scientific paper: [Provide paper text or link if using a tool that can access it]. Act as a research assistant. Extract the following information and present it in a structured format: - **Primary Hypothesis:** What was the main question the study aimed to answer? - **Key Models Used:** (e.g., cell lines, animal models). - **Primary Assays:** (e.g., Western Blot, ELISA, RNA-seq). - **Core Findings:** Summarize the main results in 3-5 bullet points, including quantitative data where possible (e.g., "Compound X showed a 50% reduction in tumor volume (p<0.05)"). - **Authors' Main Conclusion:** What was their final interpretation of the results?

Act as a computational toxicologist. I have the following predicted ADMET properties for a new chemical entity, [[COMPOUND_ID]]: - **LogP:** 3.8 - **Aqueous Solubility (logS):** -4.5 - **hERG Inhibition (pIC50):** 5.2 - **CYP3A4 Inhibition:** High probability - **Predicted Ames Mutagenicity:** Negative Based on this profile, provide a risk assessment. Structure your response as follows: 1. **Overall Assessment:** A one-sentence summary of the compound's viability. 2. **Key Liabilities:** Identify the top 2-3 risks (e.g., "The high probability of CYP3A4 inhibition suggests significant drug-drug interaction risk."). 3. **Favorable Properties:** What aspects of the profile are positive? 4. **Mitigation/Next Steps:** Suggest 2-3 specific experiments or chemical modifications to address the liabilities (e.g., "Synthesize analogs with reduced lipophilicity to lower hERG risk.").

Act as a regulatory writer. Based on the following preclinical toxicology findings for [[DRUG_CANDIDATE]], write a draft summary paragraph for an Investigational New Drug (IND) application. The tone should be formal, objective, and data-driven. **Findings:** - 14-day rat toxicology study, oral gavage. - No mortality up to 100 mg/kg. - At 100 mg/kg, observed mild, reversible liver enzyme (ALT/AST) elevation (<2x ULN). - Histopathology of the liver at 100 mg/kg showed minimal centrilobular hypertrophy, which resolved after a 7-day recovery period. - No-Observed-Adverse-Effect Level (NOAEL) determined to be 30 mg/kg.

My lead compound, [[COMPOUND_NAME]], which has a [[CHEMICAL_SUBSTRUCTURE, e.g., '2-aminopyridine']] moiety, was flagged for potential hepatotoxicity in a predictive model. Act as a medicinal chemist. Search public databases (e.g., PubChem, ChEMBL) and the literature for evidence of hepatotoxicity associated with the [[CHEMICAL_SUBSTRUCTURE]] scaffold. Provide a summary that includes: 1. Is this a well-known structural alert for liver injury? 2. List 2-3 examples of known drugs or compounds with this moiety and their associated liver safety profile. 3. Suggest bioisosteric replacements for the [[CHEMICAL_SUBSTRUCTURE]] that might mitigate this risk while preserving target activity.

Act as a medicinal chemist. I am providing a table of analog data for a series of inhibitors against [[TARGET_PROTEIN]]. | Compound ID | R1 Group | R2 Group | IC50 (nM) | LE (Ligand Efficiency) | |---|---|---|---|---| | Cmpd-01 | H | Phenyl | 500 | 0.25 | | Cmpd-02 | Me | Phenyl | 250 | 0.26 | | Cmpd-03 | H | 4-Cl-Phenyl | 50 | 0.31 | | Cmpd-04 | H | 2-F-Phenyl | 800 | 0.23 | | Cmpd-05 | Me | 4-Cl-Phenyl | 25 | 0.33 | Based on this data, generate a concise SAR summary. - What is the effect of methylation at R1? - How does substitution on the R2 phenyl ring affect potency? - Is there an obvious "activity cliff" (e.g., the 2-F substitution)? - Based on this limited data, what is the most promising quadrant for exploration (e.g., "small alkyl groups at R1 combined with para-halogenated phenyls at R2")?

Based on the SAR summary from the previous prompt, and with the goal of improving both potency and ligand efficiency, propose a list of 5 specific new chemical structures to synthesize next. For each proposed analog, provide: 1. **Structure:** (Describe the R1 and R2 groups). 2. **Hypothesis:** Why is this analog being proposed? (e.g., "To test if a cyclopropyl group at R1 can improve LE while maintaining the potency from Cmpd-05."). 3. **Potential Risk:** What is a possible negative outcome? (e.g., "The added bulk may create a steric clash.").

Our project team has just completed a campaign to optimize a hit compound, [[HIT_ID]]. We synthesized 50 analogs. The key findings were: - Potency was improved from 1 µM to 10 nM. - The primary driver of potency was a [[KEY_INTERACTION, e.g., 'hydrogen bond with Serine-252']]. - However, all potent compounds suffered from poor permeability (Caco-2 < 1 x 10^-6 cm/s) and high plasma protein binding (>99%). Act as a project lead and write a "Lessons Learned" paragraph for the project archives. Summarize the key takeaways for future projects targeting [[TARGET_PROTEIN]].

Act as a regulatory CMC specialist. I need to draft a subsection for an IND application. **Topic:** 3.2.S.2.2 Description of Manufacturing Process and Process Controls **Information:** The drug substance, [[API_NAME]], is synthesized via a 3-step linear synthesis. - Step 1: Suzuki coupling of [[Starting Material A]] and [[Starting Material B]] using a palladium catalyst. - Step 2: Boc deprotection using trifluoroacetic acid. - Step 3: Salt formation with hydrochloric acid to yield the final API. - Each step is followed by purification via crystallization. Write a formal, descriptive paragraph detailing this manufacturing process suitable for an FDA submission. Mention the starting materials, reagents, and purification methods.

Act as an experienced NIH grant writer. I want to write an R01 grant proposal. **Project Idea:** To develop a novel PROTAC degrader for [[TARGET_PROTEIN]], which is implicated in [[DISEASE]]. We have preliminary data showing our lead degrader, [[COMPOUND_X]], reduces protein levels by 90% in vitro. Generate a detailed outline for the "Specific Aims" page of the grant. The outline should include: - A compelling introductory paragraph setting up the problem. - **Specific Aim 1:** (e.g., Optimize the lead degrader for improved potency and DMPK properties). Include 2 sub-aims. - **Specific Aim 2:** (e.g., Demonstrate in vivo efficacy in a mouse model of [[DISEASE]]). Include 2 sub-aims. - A concluding "payoff" paragraph summarizing the expected outcomes and impact.

Take the following informal text and rewrite it in the formal, passive voice typical of an FDA guidance document. **Informal Text:** "We think our AI model is pretty good because we tested it on three different hospitals' data sets. We made sure to check for bias against different age groups and it looked okay. We plan to keep an eye on it after launch to make sure it doesn't drift.

You are an expert medical writer. Analyze the provided clinical trial protocol for [[DRUG NAME]] in [[DISEASE]]. Generate a one-page summary for a regulatory agency that includes the following sections: 1. **Background & Rationale:** Briefly explain the disease, the drug's mechanism of action, and the unmet need. 2. **Study Objectives:** List the primary and key secondary endpoints. 3. **Study Design:** Describe the trial phase, randomization, blinding, and duration. 4. **Inclusion/Exclusion Criteria:** Summarize the top 5 criteria for each. 5. **Statistical Analysis Plan:** Briefly outline the primary analysis method and sample size justification.

You are a clinical data manager. I will provide you with an adverse event (AE) verbatim description, the coded MedDRA term, and related data from the concomitant medications (CM) and medical history (MH) forms. Your task is to: 1. Summarize the AE. 2. Check for inconsistencies between the AE verbatim, the coded term, and the patient's reported medical history and current medications. 3. Identify any information that appears to be missing. 4. Draft a potential data query for the site to clarify the inconsistency. **AE Verbatim:** "Patient reports severe headache and dizziness starting yesterday." **Coded MedDRA PT:** "Migraine" **Medical History:** "Hypertension, diagnosed 2022." **Concomitant Medications:** "Lisinopril 10mg daily, Acetaminophen as needed." Analyze this information and provide your output.

): IC50 = 150 nM 2. Compound B (SMILES:

Act as a principal scientist preparing a target validation report for a new drug discovery program. Based on the information below, write a concise, one-page brief summarizing the case for pursuing [[TARGET_GENE]] as a therapeutic target for [[DISEASE]]. **Your brief must include:** 1. **Biological Rationale:** How is the target linked to the disease pathophysiology? 2. **Genetic Evidence:** Is there human genetic data (e.g., from GWAS) supporting the link? 3. **Druggability Assessment:** Is the target amenable to small molecule or biologic modulation? 4. **Competitive Landscape:** Are there other companies developing drugs against this target? 5. **Key Risks & Mitigation:** What are the primary risks (e.g., potential toxicity, weak linkage) and how can we de-risk them in early experiments? **Source Information:** [[Paste relevant excerpts from papers, databases, etc. here]]

You are an expert computational biologist. I am preparing a target validation report for [[TARGET_GENE_NAME]] in the context of [[DISEASE_NAME]]. Synthesize the following preclinical data points into a concise, 3-paragraph summary suitable for an executive overview. Data points: 1. Gene Expression Data: [Paste relevant data, e.g., "Upregulated 4-fold in disease tissue vs. healthy control via RNA-seq."] 2. In Vitro Model Results: [Paste relevant data, e.g., "siRNA knockdown of TARGET_GENE_NAME in disease cell line X resulted in a 50% reduction of phenotype Y."] 3. In Vivo Model Results: [Paste relevant data, e.g., "Knockout mouse model showed resistance to disease induction."] 4. Known Safety/Toxicity Concerns: [Paste relevant data, e.g., "Global knockout is embryonically lethal, suggesting a need for targeted delivery or conditional knockout."] Your summary must: - Start with a clear "bottom-line" statement on the target's viability. - Integrate the in vitro and in vivo data to build a cohesive argument. - Explicitly state the primary risk or knowledge gap identified from the safety data. - Maintain a professional, objective tone.

Analyze the biological target data for [[INSERT TARGET PROTEIN/GENE]] in [[INSERT DISEASE INDICATIONS]]. Generate a structured assessment evaluating target novelty, available structural data, potential off-target liabilities, and suitability for small molecule intervention before proceeding with AI-driven screening workflows.