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license: cc-by-4.0
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license: cc-by-4.0
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# 🧬 DrugCLIP data repository

This repository hosts benchmark datasets, pre-computed molecular embeddings, pretrained model weights, and supporting files used in the **DrugCLIP** project. It also includes data and models used for **wet lab validation experiments**.

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## 📁 Repository Contents

### 1. `DUD-E.zip`
- Full dataset for the **DUD-E benchmark**.
- Includes ligand and target files for all targets.

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### 2. `LIT-PCBA.zip`
- Full dataset for the **LIT-PCBA benchmark**.
- Includes ligand and target files for all targets.

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### 3. `encoded_mol_embs.zip`
- Pre-encoded molecular embeddings from the **ChemDiv** compound library.
- Each `.pkl` file contains:
  - `name_list`: `[hitid, SMILES]`
  - `embedding_list`: list of **128-dimensional** vectors
- Versions included:
  - **8-fold** version of the full ChemDiv library
  - **6-fold** version of the full ChemDiv library
  - **6-fold** version of a filtered ChemDiv library



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### 4. `benchmark_weights.zip`
Contains **pretrained model weights** for **benchmark experiments** on the DUD-E and LIT-PCBA datasets using various ligand and target filtering strategies.

#### 🔬 DUD-E: Ligand Filtering Strategies

| Filename             | Description |
|----------------------|-------------|
| `dude_ecfp_90.pt`    | Trained by removing ligands with **ECFP4 similarity > 0.9**. |
| `dude_ecfp_60.pt`    | Trained by removing ligands with **ECFP4 similarity > 0.6**. |
| `dude_ecfp_30.pt`    | Trained by removing ligands with **ECFP4 similarity > 0.3**. |
| `dude_scaffold.pt`   | Trained by removing ligands sharing **scaffolds** with test set. |

#### 🧬 DUD-E: Target Filtering Strategies

| Filename               | Description |
|------------------------|-------------|
| `dude_identity_90.pt`  | Removed targets with **MMseqs2 identity > 0.9**. |
| `dude_identity_60.pt`  | Removed targets with **MMseqs2 identity > 0.6**. |
| `dude_identity_30.pt`  | Removed targets with **MMseqs2 identity > 0.3**. |
| `dude_identity_0.pt`   | Removed targets based on **HMMER sequence identity**. |

#### 🧪 LIT-PCBA: Target Filtering Strategy

| Filename                | Description |
|-------------------------|-------------|
| `litpcba_identity_90.pt`| Removed targets with **MMseqs2 identity > 0.9**. |

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### 5. `model_weights.zip`
Contains model weights trained specifically for **wet lab experiments**. These models were trained using:
- **6-fold** data splits
- **8-fold** data splits

Used to predict compounds validated in real-world assays for the following targets:
- `5HT2a`
- `NET`
- `Trip12`

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### 6. `WetLab_PDBs_and_LMDBs`
Target data used for wet lab validation experiments:

- **LMDB files**: For DrugCLIP screening

Includes data for:
- `5HT2a`
- `NET`
- `Trip12`

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### 7. `benchmark_throughput`

Files for reproducing throughput benchmark results.