Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
---|---|---|---|---|---|---|---|---|---|---|
Dgl Lifesci | 545 | 1 | a month ago | 15 | January 17, 2022 | 25 | apache-2.0 | Python | ||
Python package for graph neural networks in chemistry and biology | ||||||||||
Ferminet | 532 | 25 days ago | apache-2.0 | Python | ||||||
An implementation of the Fermionic Neural Network for ab-initio electronic structure calculations | ||||||||||
Graphinvent | 312 | 21 days ago | 10 | mit | Python | |||||
Graph neural networks for molecular design. | ||||||||||
Tensormol | 190 | 5 years ago | 1 | November 08, 2017 | 16 | gpl-3.0 | Python | |||
Tensorflow + Molecules = TensorMol | ||||||||||
Moleculargnn_smiles | 182 | 2 years ago | 5 | apache-2.0 | Python | |||||
The code of a graph neural network (GNN) for molecules, which is based on learning representations of r-radius subgraphs (i.e., fingerprints) in molecules. | ||||||||||
Deep Drug Coder | 75 | 2 years ago | 1 | mit | Python | |||||
A tensorflow.keras generative neural network for de novo drug design, published in Nature Machine Intelligence while working at AstraZeneca. | ||||||||||
Neuralogic | 72 | 21 days ago | 2 | February 23, 2022 | mit | Java | ||||
Deep relational learning through differentiable logic programming. | ||||||||||
Drugai | 56 | 4 years ago | Python | |||||||
Generation and Classification of Drug Like molecule usings Neural Networks | ||||||||||
Sars Cov Inhibitors Chemai | 36 | 3 years ago | mit | |||||||
Large-scale ligand-based virtual screening for potential SARS-Cov-2 inhibitors using a deep neural network | ||||||||||
Aimnet | 31 | 3 years ago | 3 | mit | Python | |||||
Atoms In Molecules Neural Network Potential |
Classification of Drug Like molecule using Neural Networks.
more about DrugAI.. http://gananath.github.io/drugai.html
Python 2.7
Keras(Theano/Tensorflow)
Pandas
Scikit-Learn
Generator script for creating drug like molecule using LSTM model. Read more from here http://gananath.github.io/drugai-gen.html
This is my own experiments with Generative Adverserial Network (GAN) for drug like molecule generation. Teaching GAN in discrete dataset is hard and also I learned to code GAN from internet so would not gurantee any acurracy of the results or the code. Read more from here http://gananath.github.io/drugai-gan.html
A Wasserstein GAN model with CNN; this model currently trains the fastest and probably gives the best result.
# Samples Generated
['CC1=C(C(C(=O)O)(=CC=N2[S]CCCCCC(C(Cl)C1C4)[+])C2=C4=O|||||||||||||||||||||||||||' 'CC1=C(C(C(=O)OO(=CC=N2[N]CCCCCC(C(Cl)C1C3)[+])C2=CC=O)||||||||||||||||||||||||||' 'CC1=C(C(C(=O)O)(=CC=N2[N]CCC=CC(C(CO)C1C3)[+])C2=CC=O)||||||||||||||||||||||||||']
Because I seen a increase in interest for DrugAI-Gen.py; for programmers I have added another dataset sms.tsv. It contains SMS spams. Try to use it for generating Spam's and Ham's.
@misc{gananath2016,
author = {Gananath, R.},
title = {DrugAI},
year = {2016},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/Gananath/DrugAI}}
}