More accurate predictions of the biological properties of chemical
compounds would guide the selection and design of new compounds in drug
discovery and help to address the enormous cost and low success-rate of
pharmaceutical R&D. However this domain presents a significant
challenge for AI methods due to the sparsity of compound data and the
noise inherent in results from biological experiments. In this paper, we
demonstrate how data imputation using deep learning provides substantial
improvements over quantitative structure-activity relationship (QSAR)
machine learning models that are widely applied in drug discovery. We
present the largest-to-date successful application of deep-learning
imputation to datasets which are comparable in size to the corporate
data repository of a pharmaceutical company (678,994 compounds by 1166
endpoints). We demonstrate this improvement for three areas of practical
application linked to distinct use cases; i) target activity data
compiled from a range of drug discovery projects, ii) a high value and
heterogeneous dataset covering complex absorption, distribution,
metabolism and elimination properties and, iii) high throughput
screening data, testing the algorithm’s limits on early-stage noisy and
very sparse data. Achieving median coefficients of determination,
R, of 0.69, 0.36 and 0.43 respectively across these
applications, the deep learning imputation method offers an unambiguous
improvement over random forest QSAR methods, which achieve median
R values of 0.28, 0.19 and 0.23 respectively. We also
demonstrate that robust estimates of the uncertainties in the predicted
values correlate strongly with the accuracies in prediction, enabling
greater confidence in decision-making based on the imputed values.
Early-stage structure-based drug discovery for small GTPases by NMR spectroscopy
