Sample test for quantum ML #pytorch #psikit #RDKit

Recently I have many opportunities to read exciting articles about quantum-machine learning which means some models are trained with quantum chemistry based data such as ANI. I’m interested in the area and fortunately we can use really cool code named torchani. It provides model zoo includes ani-2x ;) Regarding the original article about ANI2x, ANI

Compound Generator with Graph Networks, GraphINVENT take2 #chemoinformatics #RDKit #PyTorch

I posted about graph based compound generator named ‘GraphINVENT’ some days ago. https://wordpress.com/block-editor/post/iwatobipen.wordpress.com/3450 Fortunately I could get response from author and could get useful information about their model. The hyperparameter of the model is very important and difficult to optimize but I could get suitable learning rate to train the GDB-13 small set. I changed

Compound Generator with Graph Networks, GraphINVENT #chemoinformatics #RDKit #PyTorch

Here is a new article from Esben et. al. about de novo compound generator with graph network which is named GraphINVENT. Graph based approach has advantage for compound generation compared to SMILES based approach. It doesn’t need to learn grammar of SMILES. Graph approach represents molecule as graph, atom is node and bond is edge.

Use pytorch for QSAR model building more simply like scikit-learn #pytorch #chemoinformatics #RDKit

I often use pytorch for deep learning framework. I like pytorch because it is very flexible and many recent articles are used it for their implementation. But to build model and train the model, I need to define training method. So it seems nice if I can train pytorch model just calling fit like scikit-learn

Scaffold growing with RNN #RDKit #Pytorch #Chemoinformatics

My favorite molecular generator is REINVENT which is SMILES RNN based generator. Because it is very flexible and easy to modify. And recently same group in Astrazeneca published new version of REINVENT, its title is ‘SMILES-Based Deep Generative Scaffold Decorator for De-Novo Drug Design‘ It seems very exciting for me! Because there are many molecular

Try GCN QSPR with pytorch based graph library #RDKit #Pytorch #dgl

Recently many machine learning articles use pytorch for their implementation. And I found very attractive package for graph based deep learning, named ‘DGL;Deep Graph Library’. The package supports pytorch and mxnet for backend. The author provides not only package but also very nice documentation. I read the document and try GCN for QSPR with DGL.

Build QSAR model with pytorch and rdkit #RDKit

There are many frameworks in python deeplearning. For example chainer, Keras, Theano, Tensorflow and pytorch. I have tried Keras, Chainer and Tensorflow for QSAR modeling. And I tried to build QSAR model by using pytorch and RDKit. You know, pytorch has Dynamic Neural Networks “Define-by-Run” like chainer. I used solubility data that is provided from