gmh14 / data_efficient_grammarLinks
[ICLR 2022] Data-Efficient Graph Grammar Learning for Molecular Generation
☆96Updated last year
Alternatives and similar repositories for data_efficient_grammar
Users that are interested in data_efficient_grammar are comparing it to the libraries listed below
Sorting:
- The OGB-LSC is the Large Scale Competition by Open Graph Benchmark to help accelerate research into machine learning on graph structured …☆78Updated 11 months ago
- Reinforcement Learning for Molecular Design Guided by Quantum Mechanics☆127Updated last year
- ☆161Updated last year
- A short and easy PyTorch implementation of E(n) Equivariant Graph Neural Networks☆131Updated 3 years ago
- FS-Mol is A Few-Shot Learning Dataset of Molecules, containing molecular compounds with measurements of activity against a variety of pr…☆168Updated 2 years ago
- ☆123Updated last year
- ☆167Updated 3 years ago
- An End-to-End Framework for Molecular Conformation Generation via Bilevel Programming (ICML'21)☆51Updated 3 years ago
- Implementation of Learning Gradient Fields for Molecular Conformation Generation (ICML 2021).☆169Updated 3 years ago
- ICML2024: Equivariant Graph Neural Operator for Modeling 3D Dynamics☆56Updated last year
- ☆216Updated last year
- Chemical-Reaction-Aware Molecule Representation Learning☆77Updated 3 years ago
- GEOM: Energy-annotated molecular conformations☆229Updated 3 years ago
- Baselines models for GuacaMol benchmarks☆141Updated last year
- Implementation of Torsional Diffusion for Molecular Conformer Generation (NeurIPS 2022)☆264Updated last year
- Rapid experimentation and scaling of deep learning models on molecular and crystal graphs.☆72Updated last year
- Code for the paper Enhancing Activity Prediction Models in Drug Discovery with the Ability to Understand Human Language☆98Updated 10 months ago
- Official implementation of "Generating 3D Molecules for Target Protein Binding" [ICML2022 Long Presentation]☆108Updated last year
- A new retrieval-based framework for controllable molecule generation.☆49Updated 2 years ago
- Making self-supervised learning work on molecules by using their 3D geometry to pre-train GNNs. Implemented in DGL and Pytorch Geometric.☆168Updated last year
- MiDi: Mixed Graph and 3D Denoising Diffusion for Molecule Generation☆103Updated 9 months ago
- Collection of data sets of molecules for a validation of properties inference☆107Updated 7 years ago
- Synthetic coordinates for GNNs, as proposed in "Directional Message Passing on Molecular Graphs via Synthetic Coordinates" (NeurIPS 2021)☆30Updated 2 years ago
- Graphium: Scaling molecular GNNs to infinity.☆231Updated 2 months ago
- Official implementation of pre-training via denoising for TorchMD-NET☆92Updated 2 years ago
- Code to reproduce experiments in "Accelerating Bayesian Optimization for Protein Design with Denoising Autoencoders" (Stanton et al 2022)☆71Updated last year
- ☆63Updated 6 years ago
- Code for "Molecular Hypergraph Grammar with Its Application to Molecular Optimization"☆39Updated 7 months ago
- Lagrangian formulation of Doob's h-transform allowing for efficient rare event sampling☆49Updated 3 months ago
- MoFlow: an invertible flow model for generating molecular graphs☆138Updated 2 years ago