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README.md

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-license: cc-by-4.0
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+license: cc-by-4.0
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+---
+license: cc-by-4.0
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+# 🧬 DrugCLIP: Molecular Benchmark Models, Embeddings & Wet Lab Data
+
+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**.
+
+---
+
+## 📁 Repository Contents
+
+### 1. `DUD-E.zip`
+- Full dataset for the **DUD-E benchmark**.
+- Includes ligand and target files for all targets.
+
+---
+
+### 2. `LIT-PCBA.zip`
+- Full dataset for the **LIT-PCBA benchmark**.
+- Includes ligand and target files for all targets.
+
+---
+
+### 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
+
+All molecular embeddings are stored as Python pickled (`.pkl`) files, each containing:
+- `name_list`: A list of `[hitid, SMILES]`
+- `embedding_list`: A list of 128-dimensional float vectors
+
+
+---
+
+### 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 > 0.39**. |
+| `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**. |
+
+---
+
+### 5. `model_weights.zip`
+Contains model weights trained specifically for **wet lab experiments**. These models were trained using:
+- **6-fold** ChemDiv data splits
+- **8-fold** ChemDiv data splits
+
+Used to predict compounds validated in real-world assays for the following targets:
+- `5HT2a`
+- `NET`
+- `Trip12`
+
+---
+
+### 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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