Publications and Presentations

Imputation of Assay Bioactivity Data using Deep Learning

This article describes a novel deep learning neural network method and its application for the imputation of assay pIC50 values. These deep learning methods underpin our Cerella™ technology, part of our Augmented Chemistry® suite.



In this study, the team applied a deep learning neural network (the foundations of our Cerella platform) to impute assay pIC50 values. It was shown to outperform other approaches such as traditional QSAR models, and provide predictions with excellent accuracy.


Citation details

T. M. Whitehead, B. W. J. Irwin, P. Hunt, M. D. Segall, G. J. Conduit, J. Chem. Inf. Model., 2019, 59(3) pp. 1197-1204
DOI: 10.1021/acs.jcim.8b00768


Find out more

Visit the journal webpage to read the full article, or see other examples of Cerella in action on our videos webpage or by watching our webinar on predicting PK using limited ADME data.


Discover Cerella™

Cerella is a unique artificial intelligence platform which supports medicinal chemists and other discovery scientists. It escalates success rates and advances small molecule drug discovery, from working with early hits to nominating preclinical candidates.

Cerella’s AI platform is proven to overcome limitations in drug discovery data, confidently delivering results and seamlessly integrating with your med chem software platforms.

Cerella is part of our Augmented Chemistry suite of software and services, which bring ground-breaking artificial intelligence technologies that continuously learn from all available data to supplement your experience and skills.