ELA
Source code for "A novel methodology on distributed representations of proteins using their interacting ligands"
Robust representation of semantically constrained graphs, in particular for molecules in chemistry
MoleculeKit: Your favorite molecule manipulation kit
Mol2vec - an unsupervised machine learning approach to learn vector representations of molecular substructures
A full spaCy pipeline and models for scientific/biomedical documents.
Deep networks for protein functional inference
Fast and accurate tool for the functional annotation of protein complex queries built upon hybrid unsupervised and supervised machine learning on PubMed Central full-text word embeddings.
Junction Tree Variational Autoencoder for Molecular Graph Generation (ICML 2018)
Proof of the concept implementation of smiles2vec paper
Fréchet ChemNet Distance: A quality measure for generative models for molecules
Molecular Sets (MOSES): A Benchmarking Platform for Molecular Generation Models
A Deep Learning Toolkit for DTI, Drug Property, PPI, DDI, Protein Function Prediction (Bioinformatics)
A PyTorch and TorchDrug based deep learning library for drug pair scoring. (KDD 2022)
Text generation using Variational Auto Encoders based on IMDB review dataset
A powerful and flexible machine learning platform for drug discovery
Graph neural networks for molecular design.
Moleculenet.ai Datasets And Splits
Variational Autoencoder for Molecules
Unifying Variational Autoencoder (VAE) implementations in Pytorch (NeurIPS 2022)
Tasks Assessing Protein Embeddings (TAPE), a set of five biologically relevant semi-supervised learning tasks spread across different domains of protein biology.
Check out the dash visualization at https://dash-drug-explorer.plot.ly/out
MolTrans: Molecular Interaction Transformer for Drug Target Interaction Prediction (Bioinformatics)