Lyrebird: 3D Molecular Conformer Generation

This repository contains the Lyrebird neural network for 3D conformer generation, developed by Rowan based on the ETFlow architecture. You can use the pretrained model weights locally or run predictions directly via the Rowan web platform.

For questions or issues, please open a GitHub issue or contact the Rowan team at contact@rowansci.com.

Overview

Lyrebird is trained on diverse molecular geometries and generates 3D molecular conformers from SMILES strings using an equivariant flow-based generative model.

Example Usage

import torch
from lyrebird import LyrebirdCalculator

# Initialize the calculator
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
calculator = LyrebirdCalculator("lyrebird.yaml", "lyrebird.ckpt", device=device)

# Generate conformers from SMILES
smiles = "CC(C)CC1=CC=C(C=C1)C(C)C(=O)O"  # ibuprofen
conformers = calculator.predict(smiles, num_initial_confs=5)

print(f"Generated {len(conformers)} conformers")
print(f"First conformer shape: {conformers[0].shape}")  # (N_atoms, 3)

Local Usage

Install the required packages using:

conda env create -f environment.yml
conda activate lyrebird-env

To run the example script:

python example.py

This model can run on either CPU or GPU. GPU is recommended for faster generation of multiple conformers.

License

This model is released under the MIT License. See LICENSE for details.