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In Journal of cheminformatics

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 industry data. Moreover, we observed that deep learning derived machine learning models outperformed comparable models, which were trained by other machine learning algorithms, when applied to internal pharmaceutical company datasets. To our knowledge, this is the first large-scale study evaluating the potential of machine learning and especially deep learning directly at the level of industry-scale settings and moreover investigating the transferability of publicly learned target prediction models towards industrial bioactivity prediction pipelines.

Sturm Noé, Mayr Andreas, Le Van Thanh, Chupakhin Vladimir, Ceulemans Hugo, Wegner Joerg, Golib-Dzib Jose-Felipe, Jeliazkova Nina, Vandriessche Yves, Böhm Stanislav, Cima Vojtech, Martinovic Jan, Greene Nigel, Vander Aa Tom, Ashby Thomas J, Hochreiter Sepp, Engkvist Ola, Klambauer Günter, Chen Hongming

2020-Apr-19

Big data, ChEMBL, Cheminformatics, Deep learning, Machine learning, Prospective evaluation, PubChem, QSAR, Retrospective evaluation, Structure-based virtual screening