This Open Access paper outlines practical applications of deep imputation on large-scale drug discovery data. It compares deep learning to traditional QSAR methods. Find out more about deep imputation by visiting our Cerella webpage.
Summary
Accurately predicting biological properties of potential drug compounds is challenging. This is particularly due to limited amounts of quality data; experiments are time-consuming, expensive and can inevitably include some errors and uncertainties.
This article outlines the deep learning imputation methods underpinning our Cerella platform, applied to large data sets. It demonstrates significant improvements over commonplace quantitative structure-activity relationship (QSAR) machine learning models, in several use cases. These include compiling target activity data from a range of drug discovery projects, assessing ADME properties and looking at model performance on early-stage sparse, noisy high-throughput screening data.
Citation details
B. W. J. Irwin, T. M. Whitehead, S. Rowland, S. Y. Mahmoud, G. J. Conduit, M. D. Segall, Applied AI Lett., 2021 2(3) p. e31
DOI: 10.1002/ail2.31
Find out more
Read the full publication on the journal webpage via the button below.
INTERESTED IN AI FOR DRUG DISCOVERY?
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.