Industry-scale application and evaluation of deep learning for drug target prediction

• Published in: Journal of cheminformatics 2020-04, Vol.12 (1), p.26-13, Article 26
• Main Authors: 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
• Format: Article
• Language: English
• Subjects: Algorithms; Artificial intelligence; Big data; Biological activity; Cheminformatics; Chemistry; Chemistry and Materials Science; Computational Biology/Bioinformatics; Computer Applications in Chemistry; Computer vision; Data mining; Deep learning; Documentation and Information in Chemistry; Industry forecasts; Learning algorithms; Machine learning; Measurement techniques; Natural language processing; Observational learning; Pharmaceutical industry; Pharmaceuticals; Prediction models; QSAR; Research Article; Speech recognition; Structure-based virtual screening; Theoretical and Computational Chemistry; Therapeutic targets
• ISSN: 1758-2946; 1758-2946
• DOI: 10.1186/s13321-020-00428-5

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.