code/downstream/upstream_molas/README.md

MolAS: Molecular Embedding–Based Algorithm Selection in Protein–Ligand Docking

MolAS is a learning-based framework that predicts, for each protein–ligand pair, which docking or pose-generation method is most likely to succeed. This repository contains the full training, evaluation, and analysis code used in the accompanying paper Molecular Embedding–Based Algorithm Selection in Protein–Ligand Docking.

---

Repository Layout

molas/
│
├── main.py                 # Training / testing entry point (PyTorch Lightning)
├── main.sh                 # 5-fold and multi-GPU script (torchrun presets)
│
├── data/
│   ├── dataset.py          # Dataset construction from CSVs and precomputed graphs
│   ├── data_interface.py.  # LightningDataModule with K-Fold / split support
│   └── ...
│
├── model/                  # MolAS architectures and loss functions
│   └── ...
│
├── embedding_generation/   # Scripts for generating ESM and ChemBERTa embeddings (for completeness)
│
├── stats_test.py           # Fold-concatenated reporting and paired statistics
├── environment.yaml        # Python dependencies
└── README.md

---

Installation

Create a new environment and install PyTorch separately.

# 1) Create env
conda env create -f environment.yaml
conda activate as_dock

# 2) Install PyTorch (choose a CUDA build)
pip install --index-url https://download.pytorch.org/whl/cu124 \
    torch torchvision torchaudio

# 3) Install remaining dependencies
pip install -r requirements.txt

---

Data Preparation

Download the processed MolAS dataset and trained checkpoints from:

Zenodo: https://zenodo.org/records/17760688

Then update the data_path variables in data/dataset.py.

Expected directory structure

{data_path}/
    ├── protein_graphs_esmc_600m/
    │     └── pyg_graph_{pdbid}_{ligandid}_esmc_600m.pt
    │
    ├── ligand_chemberta.pt
    │
    ├── {benchmark}_pb_ratio.csv     # PoseBusters validity (per-algorithm)
    └── {benchmark}_rmsd.csv         # RMSD targets (per-algorithm)

Notes on checkpoints

Log folder of checkpoints (named lightning_logs) should be placed inside the repository root, parallel to this README.md.

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Quick Start

5-Fold and Multi-GPU Training

Use the preset runner:

bash main.sh

Configure:

• benchmark (e.g., posex_self_docking, posex_cross_docking, astex_posex, moad, posebusters)
• relaxation (both | true | false)
• incl_columns (algorithm list; ensure count matches --num_classes)
• devices (e.g., "0,1,2,3" for torchrun)
• test=1 to run test-only mode and export reports
• version=0 to set the initial version of the 5-version bundle for testing

Version Directory

To reproduce the results using the Zenodo-released checkpoints, set version according to the target benchmark and post-processing mode. Each entry refers to a contiguous 5-checkpoint bundle (version–version+4).

Benchmark	Post-process	version	Version bundle
MOAD-curated	—	5	5–9
PoseX + Astex	Mixed	219	219–223
PoseX-SD	Mixed	5	5–9
PoseX-CD	Mixed	5	5–9
PoseBusters	Mixed	20	20–24
PoseX + Astex	No	209	209–213
PoseX + Astex	Relaxation	214	214–218
PoseX-SD	No	10	10–14
PoseX-SD	Relaxation	30	30–34
PoseX-CD	No	30	30–34
PoseX-CD	Relaxation	25	25–29
PoseBusters	No	10	10–14
PoseBusters	Energy minimisation	15	15–19

Single-GPU Example

python main.py \
    --benchmark posex_self_docking \
    --batch_size 16 \
    --lr 1e-3 \
    --max_epochs 100 \
    --devices 0 \
    --model MolAS \
    --num_node_features 1152 \
    --num_classes 24 \
    --incl_columns surfdock unimol diffdock diffdock_l gnina autodock glide alphafold3 moe dynamicbind rfaa fabind equibind boltz1x tankbind neuralplexer chai deepdock boltz ifd protenix diffdock_pocket DS interformer

Testing With a Checkpoint

python main.py \
    --benchmark posex_self_docking \
    --devices 0 \
    --model MolAS \
    --num_node_features 1152 \
    --num_classes 24 \
    --incl_columns <algorithm list> \
    --test \
    --ckpt_path path/to/checkpoint.ckpt

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Important Arguments

• --model MolAS (default) or multi-GNN variants (e.g., MolAS_GCN_GAT_GINE, MolAS_EGNN_GAT_GINE, MolASGT). Multi-GNN variants require node features and adjacency consistent with provided graphs.

• --incl_columns Space-separated algorithm names. Must match --num_classes.

• --use_ndcg_loss, --ndcg_loss_weight, --ndcg_k Enable LambdaNDCG ranking loss.

• --use_logistic_loss, --logistic_loss_weight Pairwise logistic ranking objective.

• --k_folds, --fold_num Enable K-fold cross-validation.

• --log_root, --log_name, --log_version Control Lightning logging layout.

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Acknowledgements

MolAS uses embeddings from ESM-C and ChemBERTa, and builds on PyTorch, PyTorch Lightning, and PyTorch Geometric.