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GeoDiff is a geometric diffusion model for molecular conformation generation, with official code for GEOM-based training, checkpoint sampling, and conformation and property evaluation.

Molecular Generation · Computational Chemistry

GraphAF is a flow-based autoregressive model for molecular graph generation, with a reference-code link and a README update pointing to an implementation in TorchDrug.

Molecular Generation

JT-VAE is the official Junction Tree Variational Autoencoder implementation for molecular graph generation, with VAE training code and scripts for Bayesian optimization and joint property-predictor training.

Molecular Generation

MoFlow is an invertible molecular graph generation model with documented workflows for QM9 and zinc250k training, reconstruction, latent-space sampling, interpolation and property optimization.

Molecular Generation

MolGPT trains a small custom GPT with next-token prediction on MOSES and Guacamol for unconditional and conditional molecular generation, with trained weights and Ecco-based saliency analysis linked.

Molecular Generation

ChemBERTa provides BERT-like models for chemical SMILES, with RoBERTa masked-language modelling checkpoints and notebooks for pre-training, fine-tuning and molecular property prediction research.

Molecular Property Prediction

GraphMVP is a molecular representation-learning research implementation that combines 2D topology and 3D geometry during pre-training, then uses 2D graphs for downstream classification and regression.

Molecular Property Prediction

GROVER provides pretrained molecular graph transformers and a PyTorch workflow for self-supervised pretraining, property-prediction finetuning, inference, evaluation and molecular fingerprint generation.

Molecular Property Prediction

MolCLR is a molecular contrastive-learning framework that pre-trains graph neural networks on unlabelled molecules and supports fine-tuning for downstream molecular property prediction.

Molecular Property Prediction

Uni-Mol is a 3D molecular representation learning framework with molecular and protein-pocket models, supported by related tools for property prediction, conformation modeling and docking.

Molecular Property Prediction · Molecular Generation

GemNet is a PyTorch reference implementation of a geometric message-passing model for molecular energies and atomic forces, with notebooks for training, ASE-based prediction and molecular dynamics.

Molecular Property Prediction · Computational Chemistry

DimeNet provides reference implementations of DimeNet and DimeNet++ for directional message passing on molecular graphs, with training notebooks, test-set prediction workflows and pretrained models.

Molecular Property Prediction · Quantum Chemistry

SpookyNet is a Python research package described as learning force fields with electronic degrees of freedom and nonlocal effects, with a cited publication and MIT-licensed repository code.

Molecular Property Prediction · Quantum Chemistry

PhysNet is a TensorFlow implementation of a neural network for predicting molecular energies, forces, dipole moments and partial charges, with a configurable training workflow and an example dataset.

Molecular Property Prediction · Quantum Chemistry

TorchMD-Net implements trainable neural network potentials as PyTorch modules, with TensorNet architectures, custom molecular datasets, pretrained checkpoint loading and molecular dynamics integrations.

Molecular Property Prediction · Computational Chemistry

DeePMD-kit is a toolkit for training and fine-tuning Deep Potential interatomic models from quantum-mechanical reference data, then exporting them for inference and molecular dynamics.

Computational Chemistry · Materials Discovery

TorchANI is a PyTorch library for developing, training and using ANI-style neural network interatomic potentials, with optional C++ and CUDA extensions for descriptors and inference.

Molecular Property Prediction · Quantum Chemistry

Allegro implements an E(3)-equivariant interatomic potential as a NequIP extension, with documented GPU acceleration options and a separate plugin for LAMMPS simulations.

Computational Chemistry · Materials Discovery

NequIP is an open-source framework for building E(3)-equivariant interatomic potentials, with training, pre-trained foundation potentials, and integration with ASE and LAMMPS.

Computational Chemistry · Materials Discovery

MACE provides higher-order equivariant machine-learning interatomic potentials, with workflows for training on atomistic data, evaluating configurations, and using or fine-tuning pretrained models.

Computational Chemistry · Materials Discovery