FPSim2 for fast compound search #fpsim2 #rdkit #chemoinformatics

In the previous posts, I described various way to search compounds from data source such as ChEMBL. For example… using rdkit postgre cartridge, GPUsim which is developed by schrodinger and rdSubstructLibrary which is implemented in RDKit. All methods are very useful. And today I tried to use FPSim2 which was described in ChEMBL-OG. The packageContinue reading “FPSim2 for fast compound search #fpsim2 #rdkit #chemoinformatics”

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Fast substructure search module of RDKit #rdkit #memo

Recently I posted an example of substructure search with razi, rdkit postgres cartridge. It works well but sometime I would like to conduct SSS faster. And I could get useful information from Greg, yamasakit. rdSubstructLibrary module can perform fast substructure search! The details are described in rdkit official blog post. https://rdkit.blogspot.com/2018/02/introducing-substructlibrary.html I never tried toContinue reading “Fast substructure search module of RDKit #rdkit #memo”

Substructure search with SMARTS query against ChEMBLDB #rdkit #razi #pychembldb

Recently I often use razi for making structure search because it is very easy to integrate many workflow written in python. Today I would like to show how to perform substructure search with SMARTS query in ChEMBL. Because I’m modifying pychembldb to integrate razi for enabling structure search in pychembldb. To perform substructure seach withContinue reading “Substructure search with SMARTS query against ChEMBLDB #rdkit #razi #pychembldb”

Make pandas dataframe with r-group information #memo

I often forget many things …. So there are same topics will be posted in my blog. Sometime it’s updated due to change of package version or some reasons. And I posted very similar code previously. But I posted again to remember the procedure for myself. It’s just memo… PandasTools of RDKit makes easy toContinue reading “Make pandas dataframe with r-group information #memo”

Does the fastest computer find drug more efficiently?

Recently I read good news that the latest supercomputer developed by Japan named ‘Fugaku’ has the world’s fastest computing speed. Th e ULR of the japan times is below. https://www.japantimes.co.jp/news/2020/06/23/national/fugaku-supercomputer-ranked-fastest/ Recently computational chemists need to handle huge amount of virtual compounds and data (medicinal chemist too ;)). So many computer resources are required to drugContinue reading “Does the fastest computer find drug more efficiently?”

Optimize ML model with optuna and visualize the result with MLFlow #informatics #machine learning

As you know Optuna is very useful and powerful package for machine learning. I often use the package in my own task. And MLFLOW is also useful package. I posted about mlflow before. MLflow has many functions for visualize experiment results and manage models. https://iwatobipen.wordpress.com/2018/11/14/tracking-progress-of-machine-learning-machinelearning/ I think it will be useful if models can beContinue reading “Optimize ML model with optuna and visualize the result with MLFlow #informatics #machine learning”

Simple chemistry generates novel compounds! #memo #paper #chemoinformatics

If you are chemist, amide formation seems one of easy reaction. It’s really simple, can the reaction make novel compounds? Recently I found very interesting article in ChemRxiv written by researchers of AZ. ‘Can “easy” chemistry produce complex, diverse and novel molecules?’https://chemrxiv.org/articles/Can_Easy_Chemistry_Produce_Complex_Diverse_and_Novel_Molecules_/12563231 The author used in-house ELN dataset for analysis, compare compounds which were madeContinue reading “Simple chemistry generates novel compounds! #memo #paper #chemoinformatics”