Transforming Drug Discovery with Deep Learning Imputation

Jan 14, 2023

Transforming Drug Discovery with Deep Learning Imputation

In European Biopharmaceutical Review, Optibrium’s CEO Dr Matthew Segall discusses how we can elevate drug discovery with deep learning imputation. He shares insights on the method’s key benefits, including how they help to unlock a world of pharmacokinetics knowledge.



Making effective decisions based on the data available in drug discovery is challenging. Researchers can’t measure all the activities and properties of every single compound of interest for a drug discovery project – that would be too time-consuming and expensive. In a typical pharma company, only about 1% of the possible data will be available. Even within a project, commonly, only 10-20% of the potential data will have been measured in practice. So, decisions must be made based on incomplete or ‘sparse’ data. 

Furthermore, the experimental data that have been measured are noisy. Experiments performed by drug discovery scientists are variable and data will have experimental errors and artefacts. False negatives can lead to missed opportunities, and false positives can mean that time and effort is wasted pursuing hypotheses that later turn out to be based on faulty data. These errors exist, but it’s very difficult to spot them. 

Imputation using deep learning is a recent approach applied to drug discovery that addresses these challenges, and is the process of ‘filling in’ missing data based on limited available measurements [1]



About the author

Dr Matt Segall, CEO, Optibrium.

Matt has a Master of Science in Computation from the University of Oxford and a PhD in Theoretical Physics from the University of Cambridge. As Associate Director at Camitro (UK), ArQule Inc. and then Inpharmatica, he led a team developing predictive ADME models and state-of-the-art intuitive decision-support and visualization tools for drug discovery. In January 2006, he became responsible for management of Inpharmatica’s ADME business, including experimental ADME services and the StarDrop software platform. Following acquisition of Inpharmatica, Matt became Senior Director responsible for BioFocus DPI’s ADMET division and in 2009 led a management buyout of the StarDrop business to found Optibrium.


[1] ​T. Whitehead, B. Irwin, P. S. M. Hunt and G. Conduit, “Imputation of Assay Bioactivity Data Using Deep Learning,” J. Chem. Inf. Model., vol. 59, no. 3, pp. 1197-1204, 2019.