A bleeding edge framework for molecular modeling. This framework combines the most powerful open-source libaries for machine learning, geometric deep learning, and chemoinformatics with full featured molecular datasets while remaining light weight, modular and extendable.
From Machine Learning for Molecular Simulation
In 1929 Paul Dirac stated that: “The underlying physical laws necessary for the mathematical theory of a large part of physics and the whole of chemistry are thus completely known, and the difficulty is only that the exact application of these laws leads to equations much too complicated to be soluble. It therefore becomes desirable that approximate practical methods of applying quantum mechanics should be developed, which can lead to an explanation of the main features of complex atomic systems without too much computation.” Ninety years later, this quote is still state of the art. However, in the last decade, new tools from the rapidly developing field of machine learning (ML) have started to make significant impact on the development of approximate methods for complex atomic systems, bypassing the direct solution of “equations much too complicated to be soluble”.
- light weight, modular, extendable
- pytorch=2.2.2, pyg=2.5.2, cuda=12.1, python=3.12.2, rdkit=2024.03.1, scikit-learn=1.4
- uses the icanswim/cosmosis datascience repo for rapid prototyping
- see setup_notes.txt for implementation details
- see experiment.ipynb for examples
ICLR 2021 Keynote: Geometric Deep Learning
https://youtu.be/w6Pw4MOzMuo
Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges
https://arxiv.org/abs/2104.13478
GeoMol: Torsional Geometric Generation of Molecular 3D Conformer Ensembles
https://arxiv.org/abs/2106.07802
https://github.com/PattanaikL/GeoMol
Learning Neural Generative Dynamics for Molecular Conformation Generation
https://arxiv.org/abs/2102.10240
Equivariant message passing for the prediction of tensorial properties and molecular spectra
https://arxiv.org/abs/2102.03150
Graph Neural Networks with Learnable Structural and Positional Representations
https://arxiv.org/abs/2110.07875
https://github.com/vijaydwivedi75/gnn-lspe
Physics-based Deep Learning
https://arxiv.org/abs/2109.05237
Everything is Connected: Graph Neural Networks
https://arxiv.org/abs/2301.08210
AlphaFold
https://www.nature.com/articles/s41586-021-03819-2
https://github.com/deepmind/alphafold
Deep learning for molecular design - a review of the state of the art
https://arxiv.org/abs/1903.04388
Machine learning for molecular simulation
https://arxiv.org/abs/1911.02792
Machine learning for molecular and materials science
https://www.researchgate.net/publication/326608140_Machine_learning_for_molecular_and_materials_science
Deep learning methods in protein structure prediction
https://www.sciencedirect.com/science/article/pii/S2001037019304441
Machine learning for protein folding and dynamics
https://arxiv.org/abs/1911.09811
Graph Attention Networks
https://arxiv.org/abs/1710.10903
https://youtu.be/8owQBFAHw7E
Learning to Simulate Complex Physics with Graph Networks
https://arxiv.org/abs/2002.09405
https://github.com/deepmind/deepmind-research/tree/master/learning_to_simulate
Set Transformer: A Framework for Attention-based Permutation-Invariant Neural Networks
https://arxiv.org/abs/1810.00825
https://github.com/juho-lee/set_transformer
Learning continuous and data-driven molecular descriptors by translating equivalent chemical representations
https://pubs.rsc.org/en/content/articlehtml/2019/sc/c8sc04175j
Machine Learning Force Fields
https://arxiv.org/abs/2010.07067
Unifying machine learning and quantum chemistry -- a deep neural network for molecular wavefunctions
https://arxiv.org/abs/1906.10033
Integrating Machine Learning with Physics-Based Modeling
https://arxiv.org/abs/2006.02619
Bypassing the Kohn-Sham equations with machine learning
https://www.nature.com/articles/s41467-017-00839-3
RDKit
https://www.rdkit.org/
PyG
https://pytorch-geometric.readthedocs.io/en/latest/
SchNetPack: A Deep Learning Toolbox For Atomistic Systems
https://arxiv.org/abs/1809.01072
https://github.com/atomistic-machine-learning/schnetpack
Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals
https://arxiv.org/abs/1812.05055
https://github.com/materialsvirtuallab/megnet
MoleculeNet: A Benchmark for Molecular Machine Learning
https://arxiv.org/abs/1703.00564
https://github.com/deepchem/deepchem
Quantum-Machine.org
http://quantum-machine.org/datasets/
GDB Databases
https://gdb.unibe.ch/downloads/
Alchemy: A Quantum Chemistry Dataset for Benchmarking AI Models
https://arxiv.org/abs/1906.09427
https://github.com/tencent-alchemy/Alchemy
QM7-X: A comprehensive dataset of quantum-mechanical properties spanning the chemical space of small organic molecules
https://arxiv.org/abs/2006.15139
https://zenodo.org/record/3905361
The ANI-1ccx and ANI-1x data sets, coupled-cluster and density functional theory properties for molecules
https://www.nature.com/articles/s41597-020-0473-z#Sec11
PyG Datasets
https://pytorch-geometric.readthedocs.io/en/latest/modules/datasets.html
Kaggle
https://www.kaggle.com/c/champs-scalar-coupling
QMugs: Quantum Mechanical Properties of Drug-like Molecules
https://arxiv.org/abs/2107.00367
https://doi.org/10.3929/ethz-b-000482129