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Showing posts from January, 2020

New ChEMBL ligand-based target predictions docker image available

One year ago we published a new version of our target prediction models and since then we've been working on its implementation for the upcoming ChEMBL 26 release. What did we do? First of all we re-trained the models with the LightGBM library instead of using scikit-learn. By doing this and tuning a bit the parameters our prediction timing improved by 2 orders of magnitude while keeping comparable prediction power. Having quicker models allowed us to easily implement a simple web service providing real time predictions. Since we are currently migration to a more sustainable Kubernetes infrastructure it made sense to us to directly write the small target prediction web service as a cloud native app. We then decided to give OpenFaaS a try as a platform to deploy machine learning models. OpenFaaS is a framework for building serverless functions with Docker and Kubernetes. It provides templates for deploying functions as REST endpoints in many different programming lang