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This paper was printed in the Journal of Chemical Information and Modeling.

Imputation of Assay Bioactivity Data Using Deep Learning

We describe a novel deep learning neural network method and its application to impute assay pIC50 values. Unlike conventional machine learning approaches, this method is trained on sparse bioactivity data as input, typical of that found in public and commercial databases, enabling it to learn directly from correlations between activities measured in different assays.

In two case studies on public domain data sets we show that the neural network method outperforms traditional quantitative structure-activity relationship (QSAR) models and other leading approaches. Furthermore, by focussing on only the most confident predictions the accuracy is increased to R2 > 0.9 using our method, as compared to R2 = 0.44 when reporting all predictions. You can download this paper here...

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We will be attending UK QSAR Autumn Meeting, Nottingham, UK. 26 September 2019 More events...

Next webinar

Our next webinar, in October, will be "Predicting pKa Using a Combination of Quantum and Machine Learning Methods" presented by Dr Peter Hunt... (date and details will follow shortly)

Most recent publication

Tom Whitehead, Ben Irwin, Matthew Segall, Peter Hunt, Garath Conduit - Imputation of Assay Bioactivity Data using Deep Learning, J. Chem. Inf. Model. (2019) DOI: 10.1021/acs.jcim.8b00747. More publications...

Most recent presentation

Ben Irwin presented "Practical Applications of Deep Learning to Imputation of Drug Discovery Data" at the ACS National Meeting and Exposition in San Diego, USA, August 2019. More presentations...

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