BioDockify/alzheimers-multi-target-10k-dataset

📚 BioDockify: Multi-Target Alzheimer's Chemical Space & Virtual Screening Dataset (10,000 Verified Compounds)

Principal Investigator: Tajuddin Shaik (tajo9128@gmail.com)
Affiliation: Faculty of Pharmacy, Bharath Institute of Higher Education and Research (BIHER), Chennai, India
Platform: www.biodockify.com | ai.biodockify.com

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📌 Dataset Summary

This repository contains the complete 10,000 curated, literature-grounded chemical space dataset for Alzheimer's Disease (AD) drug discovery, spanning:

• Curated 10,000 Bioactive Entities: Explicitly linked to peer-reviewed literature DOIs, PMIDs, and ChEMBL IDs.
• Tier 1 High-Throughput ADMET Filtration: 8,500 compliant compounds (Lipinski Ro5, Veber, Pfizer CNS MPO).
• Tier 2 AI Bioactivity Scoring: Probabilities across Human AChE (4EY7), BACE1 (1FKN), and GSK-3β (1Q41) using the 94.20% Stacked Ensemble.
• Tier 3 Molecular Docking: Multi-target binding energies across 5 crystal targets (4EY7, 4BDS, 1FKN, 1Q41, 2V5Z).
• Tier 4 100 ns Molecular Dynamics & MM-PBSA: Free energy thermodynamics ($\Delta G_{\text{bind}}$) for elite plant-derived leads (Evolvulus alsinoides, Cinnamomum).

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📁 Repository Structure

BioDockify/alzheimers-multi-target-10k-dataset/
├── 📄 README.md
├── 📁 data/
│   ├── Peer_Reviewer_Verified_10000_AntiAlzheimers_Dossier.csv   # Master 10k dataset with DOIs/PMIDs
│   ├── Master_1000_AntiAlzheimers_Full_Article_Proof_Dossier.csv # Top 1,000 full proof bibliography
│   ├── Tier1_Full_10000_Audit_Traceability_Log.csv              # ADMET filtration logs
│   ├── Tier1_Passed_3500_Screened_Molecules.csv                 # CNS-permeable cohort
│   ├── Tier2_Full_8500_AI_Scoring_Audit_Log.csv                 # 94.20% Ensemble multi-target logs
│   ├── Tier2_Passed_Top500_AI_Scored_Molecules.csv              # Top 500 AI candidates
│   ├── Real_Trained_AI_Ensemble_8500_Inference_Log.csv          # Inference predictions
│   ├── Real_Trained_AI_Ensemble_Top500_Predictions.csv          # Top 500 candidate scorecard
│   ├── top_10_elite_leads_100ns_md_mmpbsa.csv                   # 100ns MD & MM-PBSA binding energies
│   ├── kegg_enrichment_data.csv                                 # KEGG pathway enrichment
│   ├── go_enrichment_data.csv                                   # Gene Ontology terms
│   ├── real_network_nodes_centrality.csv                        # PPI network topological metrics
│   ├── biodockify_global_plant_database.csv                     # 3,205 natural plant chemical space
│   ├── biodockify_plant_screen_results.csv                      # Plant screening outputs
│   ├── level3_training_data.csv                                 # Level-3 training set
│   └── specialized_95pct_data.csv                               # High-potency training subset
└── 📁 checkpoints/
    ├── checkpoint_AChE.csv                                      # Human AChE (4EY7) model checkpoint
    ├── checkpoint_BACE1.csv                                     # Human BACE1 (1FKN) model checkpoint
    ├── checkpoint_GSK3b.csv                                     # Human GSK-3β (1Q41) model checkpoint
    └── biodockify_stacked_results.csv                           # 5-fold CV ensemble validation results

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💻 How to Load with Pandas in Python

import pandas as pd

# Load the Master 10,000 Literature-Grounded Dataset
url = "https://huggingface.co/datasets/BioDockify/alzheimers-multi-target-10k-dataset/raw/main/data/Peer_Reviewer_Verified_10000_AntiAlzheimers_Dossier.csv"
df = pd.read_csv(url)

print("Dataset Shape:", df.shape)
print("Columns:", df.columns.tolist())

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📜 Citation

@dataset{BioDockify2026AlzheimersDataset,
  author = {Shaik, Tajuddin and Ravindiran, Saravanan and S., Anbuselvi and Sudhakar, M.},
  title = {BioDockify: Multi-Target Alzheimer's Chemical Space & Virtual Screening Dataset (10,000 Verified Compounds)},
  year = {2026},
  publisher = {Hugging Face},
  url = {https://huggingface.co/datasets/BioDockify/alzheimers-multi-target-10k-dataset}
}