Move project docs to docs/project-docs and update references

- Move AGENTS.md, CLEANUP_SUMMARY.md, DOCUMENTATION_GUIDE.md, IMPLEMENTATION_SUMMARY.md, QUICK_COMMANDS.md to docs/project-docs/ - Update AGENTS.md to include splicing module documentation - Update mkdocs.yml navigation to include project-docs section - Update .gitignore to track docs/ directory - Add docs/plans/ splicing design documents Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
2026-03-18 17:56:03 +08:00
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--- a/docs/plans/2026-01-23-tylosin-splicing-design.md
+++ b/docs/plans/2026-01-23-tylosin-splicing-design.md
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+# Tylosin High-Throughput Splicing & Screening System Design
+
+## 1. System Overview
+
+The **Tylosin Splicer** is a combinatorial chemistry engine designed to optimize the Tylosin scaffold. It systematically modifies positions 7, 15, and 16 of the macrolactone ring by splicing high-potential fragments identified by the SIME platform, then immediately evaluating their predicted antibacterial activity.
+
+## 2. Component Architecture
+
+```mermaid
+componentDiagram
+    package "Inputs" {
+        [Tylosin SMILES] as InputCore
+        [Fragment CSVs] as InputFrags
+        note right of InputFrags: SIME predicted\nhigh-activity fragments
+    }
+
+    package "Core Preparation" {
+        [Scaffold Preparer] as CorePrep
+        [Ring Numbering] as RingNum
+        note right of CorePrep: Identifies 7, 15, 16\nReplaces groups with anchors
+    }
+
+    package "Fragment Processing" {
+        [Fragment Loader] as FragLoad
+        [Attachment Point Selector] as AttachSel
+        note right of AttachSel: Heuristic rules to\nfind connection points
+    }
+
+    package "Splicing Engine" {
+        [Combinatorial Splicer] as Splicer
+        [Conformer Validator] as Validator
+        note right of Splicer: RDKit ChemicalReaction\nor ReplaceSubstructs
+    }
+
+    package "Evaluation (SIME)" {
+        [Activity Predictor] as Predictor
+        [Broad Spectrum Model] as Model
+    }
+
+    package "Outputs" {
+        [Ranked Results CSV] as Output
+    }
+
+    InputCore --> CorePrep
+    RingNum -.-> CorePrep : "Locate positions"
+
+    InputFrags --> FragLoad
+    FragLoad --> AttachSel
+
+    CorePrep --> Splicer : "Scaffold with Anchors (*)"
+    AttachSel --> Splicer : "Activated Fragments (R-Groups)"
+
+    Splicer --> Validator : "Raw Candidates"
+    Validator --> Predictor : "Valid 3D Structures"
+
+    Predictor --> Model : "Inference"
+    Model --> Output : "Scores & Rankings"
+```
+
+## 3. Data Flow Strategy
+
+### Step 1: Scaffold Preparation (`CorePrep`)
+- **Input**: Tylosin SMILES.
+- **Action**:
+    1. Parse SMILES using `macro_split` utils.
+    2. Use `RingNumbering` to identify atoms at indices 7, 15, 16.
+    3. Perform "surgical removal": Break bonds to existing side chains at these indices.
+    4. Attach "Anchor Atoms" (Isotopes or Dummy Atoms `[*:1]`, `[*:2]`, `[*:3]`) to the ring carbons.
+
+### Step 2: Fragment Activation (`AttachSel`)
+- **Input**: Fragment SMILES from SIME CSVs.
+- **Action**: Convert a standalone molecule into a substituent (R-Group).
+    - **Strategy A (Smart)**: Identify heteroatoms (-NH2, -OH) as attachment points.
+    - **Strategy B (Random)**: Randomly replace a Hydrogen with an attachment point.
+    - **Strategy C (Linker)**: Add a small linker (e.g., -CH2-) if needed.
+
+### Step 3: Combinatorial Splicing (`Splicer`)
+- **Input**: 1 Scaffold + N Fragments.
+- **Action**:
+    - **Single Point**: Modify only pos 7, or 15, or 16.
+    - **Multi Point**: Combinatorial modification (e.g., 7+15).
+    - **Reaction**: use `rdkit.Chem.rdChemReactions` or `ReplaceSubstructs`.
+
+### Step 4: High-Throughput Prediction (`Predictor`)
+- **Integration**: Import `SIME.utils.mole_predictor`.
+- **Batching**: Collect valid spliced molecules into batches of 128/256.
+- **Scoring**: Run `ParallelBroadSpectrumPredictor`.
+- **Filtering**: Keep only molecules with `broad_spectrum == True` or high inhibition scores.
+
+## 4. Technology Stack
+- **Core Logic**: Python 3.9+
+- **Chemistry Engine**: RDKit
+- **Data Handling**: Pandas, NumPy
+- **ML Inference**: PyTorch (via SIME models)
+- **Parallelization**: Python `multiprocessing` (via SIME batch predictor)
--- a/docs/plans/2026-01-23-tylosin-splicing-implementation.md
+++ b/docs/plans/2026-01-23-tylosin-splicing-implementation.md
@@ -0,0 +1,183 @@
+# Tylosin Splicing System Implementation Plan
+
+> **For Claude:** REQUIRED SUB-SKILL: Use superpowers:executing-plans to implement this plan task-by-task.
+
+**Goal:** Build a pipeline to splice SIME-identified fragments onto the Tylosin scaffold at positions 7, 15, and 16, and predict their antibacterial activity.
+
+**Architecture:** A Python-based ETL pipeline using RDKit for structural manipulation (`macro_split`) and PyTorch for activity prediction (`SIME`).
+
+**Tech Stack:** Python, RDKit, Pandas, PyTorch (SIME), Pytest.
+
+---
+
+### Task 1: Environment & Project Structure Setup
+
+**Files:**
+- Create: `scripts/tylosin_splicer.py` (Main entry point stub)
+- Create: `src/splicing/__init__.py`
+- Create: `src/splicing/scaffold_prep.py`
+- Create: `tests/test_splicing.py`
+
+**Step 1: Create directory structure**
+```bash
+mkdir -p src/splicing
+touch src/splicing/__init__.py
+```
+
+**Step 2: Create a basic test to verify environment**
+Write a test that imports both `macro_split` and `SIME` modules to ensure the workspace handles imports correctly.
+
+```python
+# tests/test_env_integration.py
+import sys
+import os
+sys.path.append("/home/zly/project/SIME")  # Hack for now, will clean up later
+sys.path.append("/home/zly/project/merge/macro_split")
+
+def test_imports():
+    from src.ring_numbering import get_macrolactone_numbering
+    from utils.mole_predictor import ParallelBroadSpectrumPredictor
+    assert True
+```
+
+**Step 3: Run test**
+`pixi run pytest tests/test_env_integration.py`
+
+---
+
+### Task 2: Scaffold Preparation (The "Socket")
+
+**Files:**
+- Modify: `src/splicing/scaffold_prep.py`
+- Test: `tests/test_scaffold_prep.py`
+
+**Step 1: Write failing test**
+Test that `prepare_tylosin_scaffold` returns a molecule with dummy atoms at positions 7, 15, and 16.
+
+```python
+# tests/test_scaffold_prep.py
+from rdkit import Chem
+from src.splicing.scaffold_prep import prepare_tylosin_scaffold
+
+TYLOSIN_SMILES = "CCC1OC(=O)C(C)C(O)C(C)C(O)C(C)C(OC2CC(C)(O)C(O)C(C)O2)CC(C)C(=O)C=CC=C1COC3OS(C)C(O)C(N(C)C)C3O" # Simplified/Example
+
+def test_scaffold_prep():
+    scaffold, mapping = prepare_tylosin_scaffold(TYLOSIN_SMILES, positions=[7, 15, 16])
+    # Check if we have mapped atoms
+    assert 7 in mapping
+    assert 15 in mapping
+    assert 16 in mapping
+    # Check if they are dummy atoms or have specific isotopes
+```
+
+**Step 2: Implement `prepare_tylosin_scaffold`**
+Use `get_macrolactone_numbering` to find the atom indices.
+Use `RWMol` to replace side chains at those indices with a dummy atom (e.g., At number 0 or Isotope).
+
+**Step 3: Run tests**
+`pixi run pytest tests/test_scaffold_prep.py`
+
+---
+
+### Task 3: Fragment Activation (The "Plug")
+
+**Files:**
+- Create: `src/splicing/fragment_prep.py`
+- Test: `tests/test_fragment_prep.py`
+
+**Step 1: Write failing test**
+Test that `activate_fragment` takes a SMILES and returns a molecule with *one* attachment point.
+
+```python
+# tests/test_fragment_prep.py
+from src.splicing.fragment_prep import activate_fragment
+
+def test_activate_fragment_smart():
+    # Fragment with -OH
+    frag_smiles = "CCO"
+    activated = activate_fragment(frag_smiles, strategy="smart")
+    # Should find the O and replace H with attachment point
+    assert "*" in Chem.MolToSmiles(activated)
+
+def test_activate_fragment_random():
+    frag_smiles = "CCCCC"
+    activated = activate_fragment(frag_smiles, strategy="random")
+    assert "*" in Chem.MolToSmiles(activated)
+```
+
+**Step 2: Implement `activate_fragment`**
+- **Smart**: Look for -NH2, -OH, -SH. Use SMARTS to find them, replace a H with `*`.
+- **Random**: Pick a random Carbon, replace a H with `*`.
+
+**Step 3: Run tests**
+`pixi run pytest tests/test_fragment_prep.py`
+
+---
+
+### Task 4: Splicing Engine (The Assembly)
+
+**Files:**
+- Create: `src/splicing/engine.py`
+- Test: `tests/test_splicing_engine.py`
+
+**Step 1: Write failing test**
+Test connecting an activated fragment to the scaffold.
+
+```python
+def test_splice_molecules():
+    scaffold = ... # prepared scaffold
+    fragment = ... # activated fragment
+    product = splice_molecule(scaffold, fragment, position=7)
+    assert product is not None
+    assert Chem.MolToSmiles(product) != Chem.MolToSmiles(scaffold)
+```
+
+**Step 2: Implement `splice_molecule`**
+Use `Chem.ReplaceSubstructs` or `Chem.rdChemReactions`.
+Ensure the connection is chemically valid.
+
+**Step 3: Run tests**
+`pixi run pytest tests/test_splicing_engine.py`
+
+---
+
+### Task 5: Prediction Pipeline Integration
+
+**Files:**
+- Create: `src/splicing/pipeline.py`
+- Test: `tests/test_pipeline.py`
+
+**Step 1: Write failing test (Mocked)**
+Mock the SIME predictor to avoid loading heavy models during unit tests.
+
+```python
+def test_pipeline_flow(mocker):
+    # Mock predictor
+    mocker.patch('utils.mole_predictor.ParallelBroadSpectrumPredictor')
+
+    frags = ["CCO", "CCN"]
+    results = run_splicing_pipeline(TYLOSIN_SMILES, frags, positions=[7])
+    assert len(results) > 0
+```
+
+**Step 2: Implement `run_splicing_pipeline`**
+1. Prep scaffold.
+2. Loop fragments -> activate -> splice.
+3. Batch generate SMILES.
+4. Call `ParallelBroadSpectrumPredictor`.
+5. Return results.
+
+**Step 3: Run tests**
+
+---
+
+### Task 6: CLI and Final Execution
+
+**Files:**
+- Create: `scripts/run_tylosin_optimization.py`
+
+**Step 1: Implement CLI**
+Arguments: `--input-scaffold`, `--fragment-csv`, `--positions`, `--output`.
+
+**Step 2: Integration Test**
+Run with a small subset of the fragment CSV (head -n 10).