---
date: 2024-11-20
type: daily
tags: [lab-notes]
---

# 2024-11-20 - Wednesday

## Experiments

Completed [[Experiment-2024-11-20]] - [[ML]] [[Drug-Target Prediction]] model.

- [[Trained Random Forest]] on 30K drug-[[Target]] pairs
- AUC-ROC: 0.89 ([[Excellent]] [[Performance]])
- Network centrality is [[Top]]-3 [[Feature]] (validates [[Systems]] biology)

**Feature importance**:
1. Binding pocket properties (0.18)
2. Drug lipophilicity (0.14)
3. Network degree centrality (0.12) ← from PPI analysis!
4. Molecular weight (0.09)

## Analysis

Predicted 47 drug candidates for EGFR hub proteins:
- Ranked by binding probability
- [[Top 10]] advanced to docking (starting [[Friday]])
- [[Focus]] on SRC and PIK3CA (druggable kinases)

## Code

```python
# Best model
rf = RandomForestClassifier(n_estimators=500, max_depth=20)
rf.fit(X_train, y_train)
```

Pushed to GitHub: `nexus-lab/drug-target-ml`

## [[Lab Work]]

- Harvested lentivirus from yesterday
- Titered: 1.2 × 10^7 TU/mL ([[Good]])
- [[Ready]] for transduction tomorrow

## [[Papers Read]]

[[Libbrecht2015-ML-Genomics]] - excellent [[Overview]] of ML for genomics.

## [[Next Steps]]

- Molecular docking for top 10 compounds (Friday)
- Transduce CRISPR cells (Thursday)

## Notes

Network [[Features]] significantly boost ML performance. [[Integration]] of [[Multiple]] [[Data]] types is powerful.
