59 citations · 59 across the 2 of their papers we have counts for
6 papers
A semi-supervised learning framework for quantitative structure-activity regression modelling
Oliver P Watson, Isidro Cortes-Ciriano, James A Watson
Supervised learning models, also known as quantitative structure-activity regression (QSAR) models, are increasingly used in assisting the process of preclinical, small molecule dr…
Concepts and Applications of Conformal Prediction in Computational Drug Discovery
Isidro Cortés-Ciriano, Andreas Bender
Estimating the reliability of individual predictions is key to increase the adoption of computational models and artificial intelligence in preclinical drug discovery, as well as t…
Reliable Prediction Errors for Deep Neural Networks Using Test-Time Dropout
Isidro Cortes-Ciriano, Andreas Bender
While the use of deep learning in drug discovery is gaining increasing attention, the lack of methods to compute reliable errors in prediction for Neural Networks prevents their ap…
KekuleScope: prediction of cancer cell line sensitivity and compound potency using convolutional neural networks trained on compound images
Isidro Cortes Ciriano, Andreas Bender
The application of convolutional neural networks (ConvNets) to harness high-content screening images or 2D compound representations is gaining increasing attention in drug discover…
Deep Confidence: A Computationally Efficient Framework for Calculating Reliable Errors for Deep Neural Networks
Isidro Cortes-Ciriano, Andreas Bender
Deep learning architectures have proved versatile in a number of drug discovery applications, including the modelling of in vitro compound activity. While controlling for predictio…
A decision theoretic approach to model evaluation in computational drug discovery
Oliver Watson, Isidro Cortes-Ciriano, Aimee Taylor +1
Artificial intelligence, trained via machine learning or computational statistics algorithms, holds much promise for the improvement of small molecule drug discovery. However, stru…