Generation of molecular structures using Graph Neural Networks with Python and PyTorch

Graph autoencoder for molecular generation

答题类微信小程序完整源码

Thermodynamics and Phase Equilibrium component of Chemical Engineering Design Library (ChEDL)

A tensorflow.keras generative neural network for de novo drug design, first-authored in Nature Machine Intelligence while working at AstraZeneca.

A light-weight transformer model for Kaggle House Prices Regression Competition

machine learning, molecular descriptor

stock predict with MLP,CNN,RNN,LSTM,Transformer and Transformer-LSTM

Transformer seq2seq model, program that can build a language translator from parallel corpus

Get chemical SMILES strings (structures) based on the CAS numbers or the names of the chemicals.

A smile is all you need: Predicting limiting activity coefficients from SMILES with natural language processing

机器学习集成模型之Stacking各类模型及工具源码

This is a Pytorch implementation of the paper: Self-Supervised Graph Transformer on Large-Scale Molecular Data

pre-training BERT with molecular data

MCMG_V1

Implementation of MolCLR: "Molecular Contrastive Learning of Representations via Graph Neural Networks" in PyG.

Code for the paper "A Deep Generative Model for Fragment-Based Molecule Generation" (AISTATS 2020)

This repository contains the training routines and the experiments presented in the paper "Graph Neural Networks for the prediction of infinite dilution activity coefficients"

Python package for graph neural networks in chemistry and biology

Implementation of Sequence Generative Adversarial Nets with Policy Gradient

Tensorflow implementation of MolGAN: An implicit generative model for small molecular graphs