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license: cc-by-nc-nd-4.0
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tags:
  - molecular-generation
  - diffusion-models
  - cheminformatics
  - 3D-conformer
  - rdkit
  - non-commercial
language: en
library_name: mlconfgen
datasets:
  - ChEMBL
metrics:
  - shape-tanimoto
  - validity
  - uniqueness
  - novelty
  - Fréchet Distance
model-index:
  - name: ML Conformer Generator
    results:
      - task:
          type: molecular-generation
          name: 3D Conformer Generation
        dataset:
          name: ChEMBL (filtered)
          type: molecules
        metrics:
          - name: Valid molecules
            type: validity
            value: 48%
          - name: Chemical novelty
            type: novelty
            value: 99.84%
          - name: Shape Tanimoto Similarity (avg)
            type: shape-tanimoto
            value: 53.32%
          - name: Shape Tanimoto Similarity (max)
            type: shape-tanimoto
            value: 99.69%
          - name: Unique molecules
            type: uniqueness
            value: 99.94%
          - name: Fréchet Fingerprint Distance
            type: Fréchet Distance
            value: 4.13

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.

📦 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

🚀 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

🚫 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.

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

✅ Valid molecules (post-standardization, % from requested): 48%
🧬 Chemical novelty: 99.84%
📐 Avg Shape Tanimoto: 53.32%
🎯 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

🧠 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.

💾 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

Installation

Install the package:

pip install mlconfgen

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

🐍 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)

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)