biotarget 0.1.0

End-to-End AI Drug Discovery Pipeline powered by DrugCLIP

BioTarget: End-to-End AI Drug Discovery Pipeline 🧬💊

BioTarget is a state-of-the-art, open-source CLI pipeline designed to accelerate the early stages of the AI drug-discovery workflow. It seamlessly links target discovery, 3D protein structure prediction, deep-learning-based contrastive molecular screening, and physics-based CNN docking into a single cohesive framework.

The pipeline leverages DrugCLIP (a dual-encoder graph-text architecture) to act as a generative filter for toxicity and therapeutic intent, and gnina for structure-aware binding affinity predictions.

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🎯 The Pipeline Architecture

BioTarget executes a 5-stage workflow designed for rapid, iterative drug discovery:

1. Stage A: Disease $\rightarrow$ Target Ranking

Retrieves and ranks disease-relevant protein targets by querying extensive biomedical knowledge graphs.

• Sources: Open Targets Platform, DisGeNET, STRING, Reactome.
• Methodology: Ranks protein targets via heterogeneous Graph Neural Networks (GNN) and biological pathway evidence mapping.

2. Stage B: Protein Structure Generation

Fetches or predicts the 3D conformation of the selected target proteins.

• Primary: Experimental structures (PDB).
• Generator: OpenFold-3 for de novo prediction of variants, mutants, or unmapped isoforms.

3. Stage C: Generative AI & Candidate Extraction

Instead of blindly docking massive lookup libraries (like ChEMBL), BioTarget employs a highly optimized generative filtering approach.

• DrugCLIP Guidance: Thousands of virtual compounds are geometrically folded on the CPU array. DrugCLIP encodes a textual representation of the disease and isolates the Top 10× geometrically/semantically aligned molecular structures.

4. Stage D: Multi-Objective Binding & Toxicity Evaluation

Evaluates candidates simultaneously for efficacy (physics/CNN docking) and safety (latent space contrastive geometry).

• Binding Evaluation (gnina): Generates 3D structural Spatial Data Files (.sdf) via RDKit and calls the actual gnina subprocess. Evaluates ligand-receptor binding affinity using Convolutional Neural Networks on voxelized binding sites.
• Toxicity Penalty (DrugCLIP): Computes semantic embedding for clinical failure and calculates the normalized Cosine Similarity against the ligand's structural embedding.

5. Stage E: Ranking & Reporting

• Final Ranking: $\mathcal{S}{final} = \mathcal{S}{binding} - (0.5 \cdot \mathcal{S}_{tox})$ Aggregates hits, flags highly toxic compounds (⚠️), and outputs a ranked manifest of candidate SMILES ready for Molecular Dynamics (MD) refinement via OpenMM.

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🚀 Installation & Setup

BioTarget operates as the primary orchestration CLI and relies on the standalone drugclip package for multi-modal embedding.

# Clone the repository
git clone https://github.com/your-org/biotarget.git
cd biotarget

# Create and activate virtual environment
python3 -m venv .venv
source .venv/bin/activate

# Install dependencies (requires drugclip pip package)
pip install -r requirements.txt
pip install git+https://github.com/your-org/drugclip.git

Note: For Stage D to execute its highest-accuracy CNN scoring, ensure the gnina binary is compiled and accessible in your system $PATH.

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🔬 Running the BioTarget Pipeline

The pipeline is invoked via the unified biotarget.py orchestrator.

To execute the end-to-end pipeline for a specific disease:

python biotarget.py run full \
  --disease "Alzheimer" \
  --target-model hetero-gnn \
  --structure-engine openfold3 \
  --binding-engine gnina \
  --top-targets 3 \
  --top-ligands 10

Example Output

[Stage A] Disease -> Protein Target Ranking
[*] Querying Open Targets & DisGeNET for 'Alzheimer'...
[*] Found 3 highly ranked targets.

[Stage B] Protein Structure Generation
[*] Using engine: openfold3
[*] Folding GBA (P04062) with OpenFold-3...

[Stage C] Generative AI: De Novo Candidate Generation
[*] Generating 3000 de novo molecular structures...
[*] Generating 3D conformers for the generative pool using 64 CPU cores...
[*] Using DrugCLIP to guide selection of the top 100 generated candidates...
[*] Successfully finalized 10x generative candidate pool (N=100).

[Stage D] Binding Evaluation (gnina) & Toxicity Filtering (DrugCLIP)
[*] Loaded Target Receptor: GBA from Stage B (/runs/structures/GBA_openfold3.pdb)
[*] Computing Toxicity penalties for 100 candidates via DrugCLIP...
[*] Executing 'gnina' structure-aware docking & CNN scoring on 100 candidates...

[Stage E] Reporting
=====================================================================================
BIOTARGET PIPELINE FINAL RESULTS FOR: 'Alzheimer'
=====================================================================================
Rank  | Final  | Gnina (pK_d) | Tox Penalty   | SMILES
-------------------------------------------------------------------------------------
#1    | 0.9944 | 9.4457 (0.99) | 0.0000 OK      | CCC1(C(C)(C)C)CCOC1=O...
#2    | 0.8108 | 8.9903 (0.91) | 0.2005 OK      | COc1ccccc1N=C(S)N(CCN1CCOCC1)Cc1ccc...
#3    | 0.7631 | 9.2345 (0.96) | 0.3852 OK      | CCOC(=O)C1CCCN(c2c(NCCCN(C)Cc3ccccc...
#4    | 0.5101 | 8.8713 (0.87) | 0.7225 ⚠️ HIGH | CCCC(N=C(S)NCC1CCCO1)C12CC3CC(CC(C3...

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🛠 Model Extensibility (The Roadmap)

While this framework establishes the AI-driven core, it is intentionally modular to support the integration of downstream biophysics tools:

• Generative Expansion: Swapping the simulated candidate subset for an active autoregressive/diffusion generative model to perform closed-loop optimization.
• MD Refinement: Automated hand-off of the top $K$ hits to OpenMM for physical stability analysis and short MD relaxation.