Membrizard/ml_conformer_generator

ML Conformer Generator

ML Conformer Generator is a shape-constrained molecule generation model that combines an Equivariant Diffusion Model (EDM) and Graph Convolutional Network (GCN). It generates 3D conformations that are chemically valid and geometrically aligned with a reference shape.

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📦 Model Summary

• Architecture: Equivariant Diffusion Model (EDM) + Graph Convolutional Network (GCN)
• Training Data: 1.6 million ChEMBL compounds, filtered for molecules with 15–39 heavy atoms
• Post-Processing: Deterministic standardization pipeline using RDKit with constrained MMFF94 geometry optimization
• Primary Metric: Shape Tanimoto Similarity
• Developed by: Denis Sapegin

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🚀 Intended Use

• Non-Commercial Research in 3D molecular generation
• Academic/educational use
• Generation of molecules similar to a reference conformer
• Generation of molecules similar to a reference arbitrary shape

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🚫 Out of Scope / Limitations

• Commercial Use: Not licensed for commercial use without explicit permission.
• Training Bias: Trained on ChEMBL data — results may be biased toward drug-like molecules and chemistries.
• Elements Supported: Only the following elements are supported for generation: H, C, N, O, F, P, S, Cl, Br.
• Molecular Size Limitations:
  • Trained on molecules containing 15–39 heavy atoms.
  • By architectural design, the model can only generate molecules with up to 42 heavy atoms.

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🧪 Evaluation Metrics (100,000 requested samples, 100 denoising steps)

• ✅ Valid molecules (post-standardization, % from requested): 48% - 93%
• 🧬 Chemical novelty: 99.84%
• 📐 Avg Shape Tanimoto: 53.32% - 69.97% (IFM)
• 🎯 Max Shape Tanimoto: 99.69%
• 🔁 Unique molecules: 99.94%
• ⚡ Generation speed: 4.18 valid molecules/sec (NVIDIA H100)
• 💾 Memory (per thread): up to 4.0 GB
• 🧬 Fréchet Fingerprint Distance (to ChEMBL): 4.13

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🧠 How It Works

Core Components:

• EDM generates atom coordinates and types under shape constraints
• GCN predicts adjacency matrices (bonding)
• RDKit pipeline enforces valence, performs sanitization, and optimizes geometry

Shape Alignment:

Evaluated using Gaussian molecular volume overlap and Shape Tanimoto Similarity.

Hydrogens are excluded from similarity computation.

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💾 Access & Licensing

The Python package and inference code are available on GitHub under Apache 2.0 License

https://github.com/Membrizard/ml_conformer_generator

The trained model Weights are available at

https://huggingface.co/Membrizard/ml_conformer_generator

And are licensed under CC BY-NC-ND 4.0

The usage of the trained weights for any profit-generating activity is restricted.

For commercial licensing and inference-as-a-service, contact: Denis Sapegin

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Citation

If you use MLConfGen in your research, please cite:

Denis Sapegin, Fedor Bakharev, Dmitry Krupenya, Azamat Gafurov, Konstantin Pildish, and Joseph C. Bear.
Moment of inertia as a simple shape descriptor for diffusion-based shape-constrained molecular generation.
Digital Discovery, 2025. DOI: 10.1039/D5DD00318K

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Installation

1. Install the package:

pip install mlconfgen

2. Load the weights from Huggingface

   https://huggingface.co/Membrizard/ml_conformer_generator

PyTorch

edm_moi_chembl_15_39.pt

adj_mat_seer_chembl_15_39.pt

ONNX

edm_moi_chembl_15_39.onnx

adj_mat_seer_chembl_15_39.onnx

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🐍 Python API

PyTorch

from rdkit import Chem
from mlconfgen import MLConformerGenerator, evaluate_samples

model = MLConformerGenerator(
                              edm_weights="./edm_moi_chembl_15_39.pt",
                              adj_mat_seer_weights="./adj_mat_seer_chembl_15_39.pt",
                              diffusion_steps=100,
                            )

reference = Chem.MolFromMolFile('ceyyag.mol')

samples = model.generate_conformers(reference_conformer=reference, n_samples=20, variance=2)

aligned_reference, std_samples = evaluate_samples(reference, samples)

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ONNX

from mlconfgen import MLConformerGeneratorONNX
from rdkit import Chem

model = MLConformerGeneratorONNX(
                                 egnn_onnx="./egnn_chembl_15_39.onnx",
                                 adj_mat_seer_onnx="./adj_mat_seer_chembl_15_39.onnx",
                                 diffusion_steps=100,
                                )

reference = Chem.MolFromMolFile('ceyyag.mol')
samples = model.generate_conformers(reference_conformer=reference, n_samples=20, variance=2)