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EVA-Flow
Learning Shared Chemistry for Unified Environment-Aware Molecular Conformation Generation
Molecules adopt different 3D conformations depending on their surroundings: a drug molecule in vacuum, in solvent, in a crystal lattice, or bound to a protein pocket will look very different. Existing conformer generation methods typically train separate models for each setting.
EVA-Flow is a unified generative model that produces 3D molecular conformations conditioned on the environment. It introduces environment-aware variational encoding and multi-environment flow matching, enabling a single model to handle vacuum, protein-ligand docking, solvation, and crystal packing simultaneously. The model is pretrained on 445K molecules across five datasets and finetuned on four environments with weighted sampling.
EVA-Flow builds on the equivariant flow matching framework of ETFlow (Hassan et al., NeurIPS 2024), extending it with environment graph conditioning, a variational prior over conformer distributions, and a multi-dataset training pipeline.
Setup
conda env create -n evaflow -f env.yml
conda activate evaflow
pip install -e .
Data Preparation
Set the base data directory:
export DATA_DIR=/path/to/data
GEOM (QM9 + DRUGS)
Used by: drugs.yaml, pretrain.yaml, unify.yaml
# Download raw GEOM data
wget https://dataverse.harvard.edu/api/access/datafile/4327252 -O $DATA_DIR/rdkit_folder.tar
tar -zxvf $DATA_DIR/rdkit_folder.tar -C $DATA_DIR
# Preprocess with chemical cleaning (default)
python scripts/prepare_data.py -p $DATA_DIR/rdkit_folder -o $DATA_DIR/processed_drugs
# Or skip cleaning to reproduce the original (uncleaned) dataset
python scripts/prepare_data.py -p $DATA_DIR/rdkit_folder -o $DATA_DIR/processed_drugs --no-clean
Chemical cleaning follows GEOM-Drugs Revisited (Nikitin et al., 2025): RDKit sanitization + kekulization, multi-fragment rejection, and bond-length topology validation (40% covalent-radii tolerance). This removes ~0.01% of molecules with chemically unreasonable geometries.
SPICE + CREST
Used by: pretrain.yaml
Requires SPICE HDF5 files and pre-computed CREST conformers.
python scripts/prepare_data_spice.py \
--hdf5_dir $DATA_DIR/spice \
--crest_dir $DATA_DIR/crest_xyz \
--output_dir $DATA_DIR/processed_spice
OE62
Used by: pretrain.yaml
Requires the OE62 JSON file (df_62k.json).
python scripts/prepare_data_oe62.py \
--json_path $DATA_DIR/OE62/df_62k.json \
--output_dir $DATA_DIR/processed_oe62
COD (Crystallography Open Database)
Used by: unify.yaml (as cod_env)
Requires COD molecular structures in Parquet format with space group and lattice parameters.
# Step 1: Process parquet files into .pt
python scripts/prepare_data_cod.py \
--data_dir $DATA_DIR/cod_molecules \
--output_dir $DATA_DIR/processed_cod
# Step 2: Add environment graphs with crystal info
python scripts/prepare_data_cod.py \
--pt_dir $DATA_DIR/processed_cod/cod_env/train
python scripts/prepare_data_cod.py \
--pt_dir $DATA_DIR/processed_cod/cod_env/val
python scripts/prepare_data_cod.py \
--pt_dir $DATA_DIR/processed_cod/cod_env/test
Aquamarine Solvation
Used by: unify.yaml (as solv)
Requires the Aquamarine HDF5 file and metadata CSV.
python scripts/prepare_data_solv.py \
--hdf5_path $DATA_DIR/aquamarine/AQM-sol.hdf5 \
--csv_path $DATA_DIR/aquamarine/info-final-AQM.csv \
--output_dir $DATA_DIR/processed_solv
PDBBind
Used by: unify.yaml (as pdb)
Requires PDBBind with ligand SDF and pocket PDB files, plus train/val/test split files.
python scripts/prepare_data_pdb.py \
--data_dir $DATA_DIR/v2020-other-PL \
--splits_dir $DATA_DIR/splits \
--output_dir $DATA_DIR/processed_pdb
After processing, your DATA_DIR should look like:
$DATA_DIR/
├── processed_drugs/drugs/{train,val,test}/*.pt # Vacuum (GEOM-DRUGS)
├── processed_drugs/qm9/{train,val,test}/*.pt # Vacuum (GEOM-QM9)
├── processed_spice/spice/{train,val,test}/*.pt # SPICE conformers
├── processed_oe62/oe62/{train,val,test}/*.pt # OE62 conformers
├── processed_crest/crest/{train,val,test}/*.pt # CREST conformers
├── processed_cod/cod_env/{train,val,test}/*.pt # Crystal packing (COD)
├── processed_solv/solv/{train,val,test}/*.pt # Solvation (Aquamarine)
└── processed_pdb/pdb/{train,val,test}/*.pt # Docking (PDBBind)
Config files reference ${DATA_DIR} which is expanded from the environment variable at load time.
Training
# Single-dataset training on GEOM-DRUGS
python scripts/train.py -c configs/drugs.yaml
# Multi-dataset pretraining
python scripts/train.py -c configs/pretrain.yaml
# Multi-dataset unified training
python scripts/train.py -c configs/unify.yaml
Override any config value from the command line:
python scripts/train.py -c configs/drugs.yaml --trainer_args.devices=1 --no_logger
Evaluation
Single-dataset evaluation
python scripts/eval.py --config=configs/drugs.yaml --checkpoint=path/to/checkpoint.ckpt
Unified checkpoint (vacuum + packing + solvation + docking)
Evaluate across all four environments with a single unified checkpoint:
python scripts/eval.py -c configs/eval/drugs.yaml -k path/to/checkpoint.ckpt # vacuum
python scripts/eval.py -c configs/eval/cod.yaml -k path/to/checkpoint.ckpt # crystal packing
python scripts/eval.py -c configs/eval/solv.yaml -k path/to/checkpoint.ckpt # solvation
python scripts/eval.py -c configs/eval/pdb.yaml -k path/to/checkpoint.ckpt # docking
Or run all four at once:
CKPT=path/to/checkpoint.ckpt bash scripts/run_eval_unify.sh
Compute RMSD coverage metrics
# For molecules (vacuum, solvation, docking)
python scripts/eval_cov_mat.py --path=logs/samples/<task_name>/<datetime>/generated_files.pkl
# For crystals (packing)
python scripts/eval_cov_mat.py --path=logs/samples/<task_name>/<datetime>/generated_files.pkl --tgt_type=crystal
Model Architecture
EvaFlow uses a small equivariant transformer (TorchMD-NET backbone):
| Parameter | Value |
|---|---|
| Hidden channels | 160 |
| Layers | 20 |
| Attention heads | 8 |
| CV latent dim | 128 |
| Environment dim | 128 |
License
FAIR Noncommercial Research License
Citation
If you use this code, please cite:
@misc{yan2026evaflowlearningsharedchemistry,
title={EVA-Flow: Learning Shared Chemistry for Unified Environment-Aware Molecular Conformation Generation},
author={Bing Yan and Anuroop Sriram and Zachary Ward Ulissi and Kyunghyun Cho and Ricky T. Q. Chen and Benjamin Kurt Miller},
year={2026},
eprint={2610.36280},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={https://arxiv.org/abs/2610.36280},
}