143 citations · 420 across the 9 of their papers we have counts for
9 papers
Re-evaluating sample efficiency in de novo molecule generation
Morgan Thomas, Noel M. O'Boyle, Andreas Bender +1
De novo molecule generation can suffer from data inefficiency; requiring large amounts of training data or many sampled data points to conduct objective optimization. The latter is…
Conditional Neural Processes for Molecules
Miguel Garcia-Ortegon, Andreas Bender, Sergio Bacallado
Neural processes (NPs) are models for transfer learning with properties reminiscent of Gaussian Processes (GPs). They are adept at modelling data consisting of few observations of…
DOCKSTRING: easy molecular docking yields better benchmarks for ligand design
Miguel García-Ortegón, Gregor N. C. Simm, Austin J. Tripp +3
The field of machine learning for drug discovery is witnessing an explosion of novel methods. These methods are often benchmarked on simple physicochemical properties such as solub…
A Review of Biomedical Datasets Relating to Drug Discovery: A Knowledge Graph Perspective
Stephen Bonner, Ian P Barrett, Cheng Ye +5
Drug discovery and development is a complex and costly process. Machine learning approaches are being investigated to help improve the effectiveness and speed of multiple stages of…
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…