docs/appendix_prompts.md

Appendix A: LLM Benchmark Prompt Templates

This appendix documents all prompt templates used in the NegBioDB LLM benchmark (tasks L1--L4). Templates are reproduced verbatim from src/negbiodb/llm_prompts.py and src/negbiodb/llm_eval.py.

A.1 System Prompt (Shared Across All Tasks)

You are a pharmaceutical scientist with expertise in drug-target interactions, assay development, and medicinal chemistry. Provide precise, evidence-based answers.

A.2 L1: Activity Classification (Multiple Choice)

A.2.1 Zero-Shot Template

{context}

A.2.2 Few-Shot Template

Here are some examples of drug-target interaction classification:

{examples}

Now classify the following:

{context}

A.2.3 Answer Format Instruction

Respond with ONLY the letter of the correct answer: A, B, C, or D.

The answer format instruction is appended after both zero-shot and few-shot templates.

A.3 L2: Structured Extraction

A.3.1 Zero-Shot Template

Extract all negative drug-target interaction results from the following abstract.

Abstract:
{abstract_text}

For each negative result found, extract:
- compound: compound/drug name
- target: target protein/gene name
- target_uniprot: UniProt accession (if determinable)
- activity_type: type of measurement (IC50, Ki, Kd, EC50, etc.)
- activity_value: reported value with units
- activity_relation: relation (=, >, <, ~)
- assay_format: biochemical, cell-based, or in vivo
- outcome: inactive, weak, or inconclusive

Also report:
- total_inactive_count: total number of inactive results mentioned
- positive_results_mentioned: true/false

Respond in JSON format.

A.3.2 Few-Shot Template

Extract negative drug-target interaction results from abstracts.

{examples}

Now extract from this abstract:

Abstract:
{abstract_text}

Respond in JSON format.

Few-shot examples include the abstract text and the corresponding gold extraction in JSON format, separated by --- delimiters.

A.4 L3: Scientific Reasoning

A.4.1 Zero-Shot Template

{context}

Provide a detailed scientific explanation (3-5 paragraphs) covering:
1. Structural compatibility between compound and target binding site
2. Known selectivity profile and mechanism of action
3. Relevant SAR (structure-activity relationship) data
4. Pharmacological context and therapeutic implications

A.4.2 Few-Shot Template

Here are examples of scientific reasoning about inactive drug-target interactions:

{examples}

Now explain the following:

{context}

A.4.3 LLM-as-Judge Rubric

Responses are evaluated by a judge model (Gemini 2.5 Flash) using the following rubric:

Rate the following scientific explanation of why a compound is inactive against a target.

Compound: {compound_name}
Target: {target_gene} ({target_uniprot})

Explanation to evaluate:
{response}

Rate on these 4 dimensions (1-5 each):
1. Accuracy: Are the scientific claims factually correct?
2. Reasoning: Is the logical chain from structure to inactivity sound?
3. Completeness: Are all relevant factors considered (binding, selectivity, SAR)?
4. Specificity: Does the explanation use specific molecular details, not generalities?

Respond in JSON: {{"accuracy": X, "reasoning": X, "completeness": X, "specificity": X}}

The judge returns scores as JSON with four dimensions (accuracy, reasoning, completeness, specificity), each rated 1--5.

A.5 L4: Tested vs Untested Discrimination

A.5.1 Zero-Shot Template

{context}

A.5.2 Few-Shot Template

Here are examples of tested/untested compound-target pair determination:

{examples}

Now determine:

{context}

A.5.3 Answer Format Instruction

Respond with 'tested' or 'untested' on the first line. If tested, provide the evidence source (database, assay ID, or DOI) on the next line.

The answer format instruction is appended after both zero-shot and few-shot templates.

A.6 Model Configuration

Parameter	Value
Temperature	0.0 (deterministic)
Max output tokens	1024 (L1/L4), 2048 (L2/L3)
Few-shot sets	3 independent sets (fs0, fs1, fs2)
Retry policy	Exponential backoff, max 8 retries

Models

Model	Provider	Inference
Claude Haiku-4.5	Anthropic API	Cloud
Gemini 2.5 Flash	Google Gemini API	Cloud
GPT-4o-mini	OpenAI API	Cloud
Qwen2.5-7B-Instruct	vLLM	Local (A100 GPU)
Llama-3.1-8B-Instruct	vLLM	Local (A100 GPU)

Gemini 2.5 Flash uses thinkingConfig: {thinkingBudget: 0} to disable internal reasoning tokens and ensure the full output budget is available for the response.