Molecular Property Prediction

PyTorch Geometric (PyG) is a general graph neural network library with an official QM9 example for molecular property learning.

Molecular Property Prediction

DGL-LifeSci is a DGL/PyTorch toolkit providing graph learning components and examples for chemistry and biology.

Molecular Property Prediction

DrugAgent is a research multi-agent framework that combines LLM planning with domain-guided code generation to build and evaluate machine-learning workflows for drug discovery.

Molecular Property Prediction · Drug Discovery

ChemML is a Python suite for chemical and materials data analysis, mining, and modeling, with a modular design and documented work on graph neural networks, AutoML, and explainability.

Molecular Property Prediction · Materials Discovery

DeepMol is a Python toolkit for molecular machine learning, connecting compound standardization and featurization with model training, evaluation, interpretation and reusable pipelines.

Molecular Property Prediction

QML is an archived Python toolkit for quantum machine learning with Fortran-backed representation, kernel and solver modules. Further development has moved to qmllib.

Molecular Property Prediction · Quantum Chemistry

DScribe is a Python library that converts atomic structures into numerical descriptors for machine learning, visualization and similarity analysis, with batch processing and atomic-position derivatives.

Molecular Property Prediction · Materials Discovery

MEGNet is a deprecated TensorFlow graph-network implementation with pretrained molecular and crystal property models, training utilities, and transferable elemental embeddings.

Molecular Property Prediction · Materials Discovery

Mordred is a Python molecular descriptor calculator with command-line and RDKit-based library workflows, producing descriptor tables for cheminformatics analysis and downstream model evaluation.

Molecular Property Prediction · Cheminformatics

Molfeat is a Python toolkit for small-molecule featurization, combining molecular fingerprints, descriptors and pretrained embeddings with parallel transformation, caching and extensible plugins.

Molecular Property Prediction · Cheminformatics

TorchDrug is a PyTorch-based toolkit for graph and molecular machine learning, with SMILES input, graph operations, property-prediction building blocks and multi-CPU/GPU execution.

Molecular Property Prediction · Drug Discovery

Therapeutics Data Commons provides therapeutic machine learning datasets, Python loaders, data splits, evaluation metrics and benchmarks for prediction and molecule-generation research.

Molecular Property Prediction · Drug Discovery

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