saidutta69/red-pill-drug-discovery-formulation

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🔴 RED-PILL

Research Enhanced Dataset for Pharmaceutical Innovation in Learning & Language

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  ![Size]() ![Format]() ![Tasks]() ![Quality]()

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The first open instruction-tuning dataset for drug discovery & formulation development.

Built for fine-tuning Heretic-ablated models that won't refuse your pharmaceutical R&D questions.

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⚡ Quick Start

from datasets import load_dataset

# Load the full dataset
ds = load_dataset("saidutta69/red-pill-drug-discovery-formulation", data_files="red-pill-full.jsonl")

# Load gold tier (elite 17 instructions)
ds = load_dataset("saidutta69/red-pill-drug-discovery-formulation", data_files="red-pill-full_gold_tier.jsonl")

# Load top tier (145 grade A+B instructions)
ds = load_dataset("saidutta69/red-pill-drug-discovery-formulation", data_files="red-pill-full_top_tier.jsonl")

📊 Dataset at a Glance

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🎯 Quality Distribution

Grade A (≥0.75):  ████░░░░░░░░░░░░░░░░  17 (1.6%)
Grade B (≥0.60):  ████████████░░░░░░░░  128 (12.2%)
Grade C (≥0.45):  ████████████████████  789 (75.3%)
Grade D (≥0.30):  ██░░░░░░░░░░░░░░░░░░  102 (9.7%)
Grade F (<0.30):  ░░░░░░░░░░░░░░░░░░░░  11 (1.1%)

🧬 The Six Pillars

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<details> <summary><b>📋 Pillar Details (Click to expand)</b></summary>

🧪 Pillar 1: Drug Discovery & Screening

631 instructions

• —Target identification & validation
• —Virtual screening workflows
• —Molecular property prediction
• —ADMET profiling & optimization
• —Hit-to-lead optimization
• —Structure-activity relationships

Sources: ChEMBL 35, DrugBank, PubChem, Expert Knowledge

💊 Pillar 2: Formulation Development ⭐ Key Differentiator

62 instructions

• —Solubility enhancement strategies (ASD, nanosizing, cyclodextrins)
• —Dosage form design & selection
• —Excipient science & compatibility
• —Stability prediction & optimization
• —Dissolution modeling & IVIVC
• —QbD and Design Space concepts

Sources: FDA Orange Book, FormulationAI, Expert Knowledge

⚗️ Pillar 3: Chemical Synthesis

16 instructions

• —Retrosynthetic analysis
• —Reaction optimization
• —Process chemistry & scale-up
• —Crystallization & polymorph control
• —Green chemistry principles

Sources: ChEMBL, Process Chemistry Knowledge Base

📋 Pillar 4: Regulatory & Clinical

109 instructions

• —FDA guidance interpretation
• —Clinical trial design (Phase I-IV)
• —Bioequivalence requirements
• —Stability testing (ICH Q1A-R2)
• —ANDA/NDA filing strategy

Sources: ClinicalTrials.gov, FDA Guidance Documents

🔗 Pillar 5: Cross-cutting Integration

4 instructions

• —Drug repurposing scenarios
• —Formulation-to-clinic pipelines
• —Failure analysis & rescue strategies
• —Multi-pillar reasoning exercises

Sources: Integrated Knowledge Base

📚 Pillar 6: Literature Mining

402 instructions

• —Paper summary & analysis
• —Methodology critique
• —Cross-paper synthesis
• —Research trend identification
• —Computational method assessment

Sources: PubMed (274 papers), arXiv (21 papers)

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📋 Data Format

Each instruction follows the ChatML format with rich metadata:

{
  "messages": [
    {
      "role": "user",
      "content": "I have a BCS Class II compound with solubility 0.5 µg/mL and LogP 4.2..."
    },
    {
      "role": "assistant",
      "content": "## Solubility Enhancement Strategy\n\n### Compound Profile:\n- **Solubility:** 0.5 µg/mL (very poor)\n- **LogP:** 4.2 (lipophilic)\n..."
    }
  ],
  "metadata": {
    "pillar": "formulation",
    "domain": "solubility enhancement",
    "skill_type": "requires-reasoning",
    "source": "formulation_knowledge_base",
    "difficulty": "expert"
  }
}

📊 Metadata Fields

🎯 Quality Scoring System

Every instruction is scored across 8 dimensions:

🏆 Grade Scale

🔬 Evaluation Benchmarks

The dataset includes 32 evaluation questions across 5 categories:

Run Evaluation

# Download benchmark
python -c "
from datasets import load_dataset
ds = load_dataset('saidutta69/red-pill-drug-discovery-formulation', data_files='benchmarks/benchmark_suite.json')
ds['train'].to_json('benchmark_suite.json')
"

# Run evaluation (after generating model responses)
python benchmarks/evaluate.py \
    --responses your_model_responses.jsonl \
    --benchmark benchmark_suite.json

🚀 Recommended Fine-Tuning Setup

Base Model

Any Heretic-ablated model works:

Training Configuration

from transformers import TrainingArguments
from peft import LoraConfig

# LoRA config (recommended for Heretic models)
lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    target_modules=["q_proj", "v_proj", "k_proj", "o_proj"],
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

# Training args
training_args = TrainingArguments(
    output_dir="./red-pill-heretic",
    num_train_epochs=3,
    per_device_train_batch_size=4,
    gradient_accumulation_steps=8,
    learning_rate=2e-5,
    weight_decay=0.01,
    warmup_ratio=0.1,
    lr_scheduler_type="cosine",
    fp16=True,
    logging_steps=10,
    save_strategy="epoch",
)

⏱️ Expected Training Time

📈 Data Sources & Provenance

⚠️ Disclaimer

This dataset is for research purposes only. It should not be used as the sole basis for pharmaceutical decisions. Always consult qualified professionals for drug development activities.

The knowledge contained in this dataset reflects publicly available information and expert opinions. It may not represent the current state of the art in all areas.

📚 Citation

@dataset{red_pill_2025,
  title={RED-PILL: Research Enhanced Dataset for Pharmaceutical Innovation in Learning and Language},
  author={Saidutta Abhishek Dash},
  year={2025},
  publisher={Hugging Face},
  url={https://huggingface.co/datasets/saidutta69/red-pill-drug-discovery-formulation},
  description={Instruction-tuning dataset for drug discovery and formulation development.}
}

📄 License

Apache License 2.0

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Built with 🔴 by [Saidutta](https://huggingface.co/saidutta69)

Fine-tune models that won't refuse your pharmaceutical R&D questions.

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<img src="https://huggingface.co/datasets/huggingface/badges/resolve/main/processed-with-%F0%9F%A4%97%20%2F%20%F0%9F%93%8D-light.svg" alt="Made with HuggingFace"/>

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