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Add concise TL;DR usage summary and Quick Start download steps to README #2

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21 changes: 17 additions & 4 deletions README.md
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Expand Up @@ -8,6 +8,12 @@ DRGS-Net implements a hybrid molecular representation model that concatenates:

The combined embedding is passed to a small prediction head for downstream molecular property prediction (classification or regression).

## TL;DR (Quick usage)
1) Install dependencies (see "Quick start" below for full setup commands).
2) Copy the default config: `cp DRGS-Net.yaml config_concatenate.yaml`
3) Edit `config_concatenate.yaml` with your dataset paths and ChemBERTa checkpoint.
4) Run: `python DRGS-Net_finetune.py`

This repository contains training/finetuning scripts, dataset wrappers, model definitions, and utilities. The hybrid model is implemented in `models/concatenate_model.py` (class `HybridModel`). The finetuning orchestration is in `DRGS-Net_finetune.py` which expects a configuration file named `config_concatenate.yaml` by default — if you only have `DRGS-Net.yaml`, copy/rename it to `config_concatenate.yaml` before running (see "Quick start").
## Methodology
![DRGS-Net Architecture](./fig/Methodology.png)
Expand Down Expand Up @@ -50,7 +56,14 @@ See the model card: https://huggingface.co/DeepChem/ChemBERTa-77M-MLM

## Quick start

1) Prepare config file
1) Download the repository

```bash
git clone https://github.com/thkim-01/DRGS-Net.git
cd DRGS-Net
```

2) Prepare config file

The finetune script (`DRGS-Net_finetune.py`) by default loads `config_concatenate.yaml`. If you only have `DRGS-Net.yaml`, create a copy with the expected name:

Expand All @@ -60,7 +73,7 @@ cp DRGS-Net.yaml config_concatenate.yaml

Edit the YAML to set `task_name`, dataset paths, `fine_tune_from` (pretrained GNN checkpoint folder under `./ckpt/`) and `hybrid_specific.chemberta_model_name` (local path or Hugging Face model id).

2) Create environment & install dependencies (suggested)
3) Create environment & install dependencies (suggested)

This project requires PyTorch, HuggingFace Transformers, and optional packages like RDKit and NVIDIA Apex for mixed precision.

Expand All @@ -80,11 +93,11 @@ pip install tensorboard scikit-learn pandas numpy tqdm pyyaml

Note: Install compatible `torch-geometric` packages for your PyTorch/CUDA setup if you use PyG layers in the GNN models.

3) Prepare data
4) Prepare data

Place downstream datasets under `data/<dataset_name>/` following the MoleculeNet CSV formats. The default config uses paths like `data/bbbp/BBBP.csv` etc. See `DRGS-Net_finetune.py` for dataset mapping by `task_name`.

4) Run finetuning
5) Run finetuning

```bash
python DRGS-Net_finetune.py
Expand Down