Industry-scale application and evaluation of deep learning for drug target prediction | Journal of Cheminformatics | Full Text

Industry-scale application and evaluation of deep learning for drug target prediction | Journal of Cheminformatics | Full Text

Artificial intelligence (AI) is undergoing a revolution thanks to the breakthroughs of machine learning algorithms in computer vision, speech recognition, natural language processing and generative modelling. Recent works on publicly available pharmaceutical data showed that AI methods are highly promising for Drug Target prediction. However, the quality of public data might be different than that of industry data due to different labs reporting measurements, different measurement techniques, fewer samples and less diverse and specialized assays. As part of a European funded project (ExCAPE), that brought together expertise from pharmaceutical industry, machine learning, and high-performance computing, we investigated how well machine learning models obtained from public data can be transferred to internal pharmaceutical industry data. Our results show that machine learning models trained on public data can indeed maintain their predictive power to a large degree when applied to indust

1 mentions: @hirokaneko226
Keywords: deep learning
Date: 2020/05/02 20:21

Referring Tweets

@hirokaneko226 ChEMBLとPubChemのデータを用いて構築したQSARモデルで、AstraZenecaとJanssenにおける社内データの活性を予測する論文。記述子はECFPを使用。Matrix factorization, XGBoost, deep neural network(DNN)で比較したところDNNで最も社内データに対しても精度低下が起きなかった t.co/qgMh8hWdFV

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