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| import gradio as gr | |
| import sys | |
| import os | |
| import time | |
| # Add parent to path for local testing | |
| sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) | |
| # Try importing pharmacore modules | |
| try: | |
| from pharmacore.discovery import DeNovoDiscoveryEngine | |
| from pharmacore.repurposing import DrugRepurposingEngine, KNOWN_DRUGS | |
| MODULES_AVAILABLE = True | |
| except ImportError: | |
| MODULES_AVAILABLE = False | |
| # --- De Novo Discovery --- | |
| def run_discovery(target_name, target_sequence, n_molecules, seed): | |
| if not MODULES_AVAILABLE: | |
| return format_discovery_demo(target_name) | |
| try: | |
| engine = DeNovoDiscoveryEngine(seed=int(seed)) | |
| start = time.time() | |
| result = engine.discover( | |
| target_name=target_name, | |
| target_sequence=target_sequence if target_sequence.strip() else None, | |
| n_molecules=int(n_molecules), | |
| ) | |
| elapsed = time.time() - start | |
| lines = [f"## Results for {target_name}", f"Generated {len(result.molecules)} candidates in {elapsed:.1f}s\n"] | |
| lines.append("| Rank | Name | Score | Scaffold | SMILES |") | |
| lines.append("|------|------|-------|----------|--------|") | |
| for i, mol in enumerate(result.molecules, 1): | |
| lines.append(f"| {i} | {mol.name} | {mol.composite_score:.3f} | {mol.scaffold_name} | `{mol.smiles}` |") | |
| # Top candidate details | |
| top = result.molecules[0] | |
| lines.append(f"\n### Top Candidate: {top.name}") | |
| lines.append(f"- **Scaffold:** {top.scaffold_name}") | |
| lines.append(f"- **QED (Drug-likeness):** {top.qed:.3f}") | |
| lines.append(f"- **Target Compatibility:** {top.target_score:.3f}") | |
| lines.append(f"- **Synthetic Accessibility:** {top.sa_score:.3f}") | |
| lines.append(f"- **Lipinski:** {'PASS' if top.lipinski_pass else 'FAIL'}") | |
| return "\n".join(lines) | |
| except Exception as e: | |
| return f"Error: {str(e)}" | |
| def format_discovery_demo(target_name): | |
| """Fallback demo output when modules aren't available (for HF Space)""" | |
| return f"""## Results for {target_name} | |
| Generated 5 candidates in ~8s (demo mode — full inference requires Apple Silicon) | |
| | Rank | Name | Score | Scaffold | SMILES | | |
| |------|------|-------|----------|--------| | |
| | 1 | PC-{target_name[:4].upper()}-0001 | 0.849 | quinazoline | `NC(=O)c1c(O)ccc2ncc(-c3ccncc3)nc12` | | |
| | 2 | PC-{target_name[:4].upper()}-0002 | 0.799 | quinoline | `FC(F)(F)c1ccc2cccnc2c1` | | |
| | 3 | PC-{target_name[:4].upper()}-0003 | 0.795 | benzimidazole | `CNC(=O)c1ccc2[nH]cnc2c1` | | |
| | 4 | PC-{target_name[:4].upper()}-0004 | 0.791 | quinoline | `c1cnc2ccc(-c3ccncc3)cc2c1` | | |
| | 5 | PC-{target_name[:4].upper()}-0005 | 0.770 | indole | `O=C(O)c1cc2[nH]ccc2c(C(=O)O)c1C(=O)O` | | |
| ### Top Candidate: PC-{target_name[:4].upper()}-0001 | |
| - **Scaffold:** quinazoline (known kinase inhibitor scaffold) | |
| - **QED (Drug-likeness):** 0.731 | |
| - **Target Compatibility:** 0.900 | |
| - **Synthetic Accessibility:** 1.000 | |
| - **Lipinski:** PASS | |
| > 💡 *This is a demo preview. For real-time inference, clone the repo and run on Apple Silicon.* | |
| """ | |
| # --- Drug Repurposing --- | |
| def run_repurposing(target_name, target_sequence, reference_smiles, top_k): | |
| if not MODULES_AVAILABLE: | |
| return format_repurposing_demo(target_name) | |
| try: | |
| engine = DrugRepurposingEngine() | |
| start = time.time() | |
| result = engine.screen( | |
| target_name=target_name, | |
| target_sequence=target_sequence if target_sequence.strip() else None, | |
| reference_smiles=reference_smiles if reference_smiles.strip() else None, | |
| top_k=int(top_k), | |
| ) | |
| elapsed = time.time() - start | |
| lines = [f"## Repurposing Screen for {target_name}", f"Screened {len(KNOWN_DRUGS)} FDA-approved drugs in {elapsed:.1f}s\n"] | |
| lines.append("| Rank | Drug | Score | Confidence | Original Indication |") | |
| lines.append("|------|------|-------|------------|---------------------|") | |
| for i, c in enumerate(result.candidates, 1): | |
| lines.append(f"| {i} | {c.drug_name} | {c.composite_score:.3f} | {c.confidence} | {c.original_indication} |") | |
| top = result.candidates[0] | |
| lines.append(f"\n### Top Candidate: {top.drug_name}") | |
| lines.append(f"- **Original Use:** {top.original_indication}") | |
| lines.append(f"- **Mechanism:** {top.mechanism}") | |
| lines.append(f"- **Protein Compatibility:** {top.protein_score:.1%}") | |
| lines.append(f"- **Molecular Similarity:** {top.molecular_similarity:.1%}") | |
| return "\n".join(lines) | |
| except Exception as e: | |
| return f"Error: {str(e)}" | |
| def format_repurposing_demo(target_name): | |
| """Fallback demo output""" | |
| return f"""## Repurposing Screen for {target_name} | |
| Screened 12 FDA-approved drugs (demo mode) | |
| | Rank | Drug | Score | Confidence | Original Indication | | |
| |------|------|-------|------------|---------------------| | |
| | 1 | Erlotinib | 0.699 | medium | Non-small cell lung cancer | | |
| | 2 | Sorafenib | 0.312 | low | Renal cell carcinoma | | |
| | 3 | Sildenafil | 0.288 | low | Erectile dysfunction | | |
| | 4 | Celecoxib | 0.265 | low | Arthritis pain | | |
| | 5 | Remdesivir | 0.264 | low | Ebola (repurposed for COVID-19) | | |
| ### Top Candidate: Erlotinib | |
| - **Original Use:** Non-small cell lung cancer | |
| - **Mechanism:** EGFR tyrosine kinase inhibitor | |
| - **Protein Compatibility:** 14.0% | |
| - **Molecular Similarity:** 100.0% | |
| > ✅ Erlotinib is a known EGFR inhibitor — correctly identified as top candidate. | |
| > 💡 *Demo preview. For real inference, run on Apple Silicon locally.* | |
| """ | |
| # --- Gradio Interface --- | |
| with gr.Blocks( | |
| title="PharmaCore — AI Drug Discovery", | |
| theme=gr.themes.Soft(primary_hue="purple", secondary_hue="blue"), | |
| ) as demo: | |
| gr.Markdown(""" | |
| # 🧬 PharmaCore — AI Drug Discovery on Apple Silicon | |
| **The first AI drug discovery platform that runs entirely on consumer hardware.** | |
| No cloud GPUs. No API keys. No data leaves your machine. | |
| [GitHub](https://github.com/reacherwu/PharmaCore) | [Models](https://huggingface.co/collections/stephenjun8192/pharmacore-sparse-models-69e5842a51579e4b12d42f30) | |
| """) | |
| with gr.Tab("🧬 De Novo Discovery"): | |
| gr.Markdown("Generate novel drug candidates for a protein target using sparse AI models.") | |
| with gr.Row(): | |
| with gr.Column(): | |
| target_name_disc = gr.Textbox(label="Target Name", value="EGFR kinase", placeholder="e.g., EGFR kinase, BRAF V600E") | |
| target_seq_disc = gr.Textbox(label="Target Sequence (optional)", value="", placeholder="Protein amino acid sequence...", lines=3) | |
| n_mols = gr.Slider(minimum=3, maximum=10, value=5, step=1, label="Number of Molecules") | |
| seed = gr.Number(label="Random Seed", value=42) | |
| btn_disc = gr.Button("🚀 Generate Candidates", variant="primary") | |
| with gr.Column(): | |
| output_disc = gr.Markdown(label="Results") | |
| btn_disc.click(run_discovery, inputs=[target_name_disc, target_seq_disc, n_mols, seed], outputs=output_disc) | |
| with gr.Tab("💊 Drug Repurposing"): | |
| gr.Markdown("Screen existing FDA-approved drugs for new therapeutic uses.") | |
| with gr.Row(): | |
| with gr.Column(): | |
| target_name_rep = gr.Textbox(label="Target Name", value="EGFR", placeholder="e.g., EGFR, ACE2, BRAF") | |
| target_seq_rep = gr.Textbox(label="Target Sequence (optional)", value="", placeholder="Protein amino acid sequence...", lines=3) | |
| ref_smiles = gr.Textbox(label="Reference SMILES (optional)", value="COCCOc1cc2ncnc(Nc3cccc(C#C)c3)c2cc1OCCOC", placeholder="Known ligand SMILES for similarity scoring") | |
| top_k = gr.Slider(minimum=3, maximum=12, value=5, step=1, label="Top K Results") | |
| btn_rep = gr.Button("🔍 Screen Drugs", variant="primary") | |
| with gr.Column(): | |
| output_rep = gr.Markdown(label="Results") | |
| btn_rep.click(run_repurposing, inputs=[target_name_rep, target_seq_rep, ref_smiles, top_k], outputs=output_rep) | |
| with gr.Tab("ℹ️ About"): | |
| gr.Markdown(""" | |
| ## How It Works | |
| PharmaCore uses **sparse AI models** (50% pruned) for efficient inference: | |
| | Model | Role | Params | Speed (M4) | | |
| |-------|------|--------|------------| | |
| | ESM-2 35M | Protein encoding | 33.5M → 16.7M | 7.8ms | | |
| | ChemBERTa-zinc | Molecule encoding | 44.1M → 22M | 4.9ms | | |
| ### De Novo Discovery Pipeline | |
| 1. Encode protein target with sparse ESM-2 | |
| 2. Enumerate drug-like scaffolds (quinazoline, quinoline, benzimidazole, etc.) | |
| 3. Score candidates: QED + target compatibility + synthetic accessibility | |
| 4. Rank and filter by Lipinski/Veber rules | |
| ### Drug Repurposing Pipeline | |
| 1. Encode target protein and reference ligand | |
| 2. Compute protein-drug compatibility for 12 FDA-approved drugs | |
| 3. Calculate molecular fingerprint similarity | |
| 4. Rank by composite score with confidence levels | |
| ### Key Differentiators | |
| - **100% Local** — no data leaves your machine | |
| - **Apple Silicon MPS** — optimized for M1/M2/M3/M4 | |
| - **Transparent** — full audit trail for every computation | |
| - **Fast** — sub-10ms protein inference, sub-5ms molecular inference | |
| - **Open Source** — MIT licensed, all models on HuggingFace | |
| """) | |
| if __name__ == "__main__": | |
| demo.launch() | |