meilerlab / MolKGNNLinks
MolKGNN is a deep learning model for predicting biological activity or molecular properties. It features in 1. SE(3)-invariance 2. conformation-invariance 3. interpretability. MolKGNN uses a novel molecular convolution to leverage the similarity of molecular neighborhood and kernels. It shows superior results in realistic drug discovery dataset…
☆20Updated 2 years ago
Alternatives and similar repositories for MolKGNN
Users that are interested in MolKGNN are comparing it to the libraries listed below
Sorting:
- A simple molecule fragmentation method.☆41Updated 2 years ago
- ☆94Updated 3 years ago
- Geometry Deep Learning for Drug Discovery and Life Science☆71Updated last year
- Chemical representation learning paper in Digital Discovery☆63Updated last year
- Code for "HiGNN: A Hierarchical Informative Graph Neural Network for Molecular Property Prediction Equipped with Feature-Wise Attention"☆52Updated 3 years ago
- Recursion's molecular foundation model☆65Updated 7 months ago
- Atom-in-SMILES tokenizer for SMILES strings.☆42Updated last year
- Graph neural networks for molecular machine learning: Implemented and compatible with TensorFlow and Keras.☆61Updated 4 months ago
- Official implementation of "Equivariant Shape-Conditioned Generation of 3D Molecules for Ligand-Based Drug Design"☆67Updated last year
- Multi-Scale Representation Learning on Proteins (NeurIPS 2021)☆49Updated 2 years ago
- Code and data for QMO https://arxiv.org/abs/2011.01921☆35Updated 4 years ago
- Molecule Optimization via Fragment-based Generative Models☆43Updated 2 years ago
- ☆21Updated 3 years ago
- SELFormer: Molecular Representation Learning via SELFIES Language Models☆105Updated last year
- ☆26Updated last year
- generative model for drug discovery☆64Updated 2 months ago
- Awesome De novo drugs design papers☆90Updated 2 years ago
- maxsmi: a guide to SMILES augmentation. Find the optimal SMILES augmentation for accurate molecular prediction.☆34Updated last year
- Kinase–drug binding prediction with calibrated uncertainty quantification☆23Updated last year
- pre-training BERT with molecular data☆50Updated 4 years ago
- ☆41Updated 5 years ago
- An E(3) Equivariant Variational Autoencoder for Molecular Linker Design☆51Updated 3 years ago
- Utilities for working with SMILES based encodings of molecules for deep learning (PyTorch oriented)☆83Updated last year
- Retrosynthetic prediction with Atom Environments☆38Updated 2 years ago
- A large benchmark dataset, Blood-Brain Barrier Database (B3DB), complied from 50 published resources.☆72Updated 3 months ago
- Retrosynthetic Accessibility (RA) score learned from computer aided synthesis planning☆91Updated 4 years ago
- pythonic interface to virtual screening software☆91Updated 4 months ago
- ☆76Updated 3 years ago
- Supporting code for the paper "Bidirectional Molecule Generation with Recurrent Neural Networks" (J. Chem. Inf. 2020, 60, 3).☆53Updated 5 years ago
- Diffusion-based molecule conformer generation☆45Updated last year