RDKit UGM 2019 in Hambrug! #RDKit #Chemoinformatics

Last week, I participated RDKitUGM 2019! In this year, there were more than a hundred participants in the UGM. The meeting is growing year by year. The meeting repo is below and I think some presentation will be uploaded in few days ~ weeks I think. https://github.com/rdkit/UGM_2019 And twitter has tag is #RDKitUGM2019. Following document

Knime setting for ubuntu18.04 #Knime

I’m waiting my fright in Hamburg Air Port. I will post about UGM after back to Japan. I would like to say thank all participants. BTW, yesterday I participated Knime workshop and learned knime basics. Recently I started to use not only python but also knime. New version of knime has lots of nodes for

Python package of machine learning for imbalanced data #machine_learning #chemoinformatics

Recently I’m struggling with imbalanced data. I didn’t have any idea to handle it. So my predictive model showed poor performance. Some days ago, I found useful package for imbalanced data learning which name is ‘imbalanced learn‘. It can be installed from conda. The package provides methods for over sampling and under sampling. I had

Transfer learning of DGMG for focused library gegneration #DGL #Chemoinformatics

Transfer learning is very useful method in deeplearning. Because it can use pre trained model and can re train with few parameters. I think it is useful for molecular generator too. If it is useful for the generator, it can use for focused library generation. I posted about DGL molecular generation. So I tried to

Graph based generative model of molecule #DGL #RDKit #chemoinformatics

Recently Graph based predictive model and generative model are attractive topic in chemoinformatics area. Because, Graph based model is not need learn grammar such like a SMILES based model. It seems more primitive representation of molecule. Of course to use Graph based model, used need to convert molecule to graph object. Pytorch_geometric(PyG) and Deep Graph