Author

Nathan Brown

BenevolentAI (United Kingdom)ORCID 0000-0001-9243-8699Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

Click to filter author papers

Signature Assays, Organisms & Methods

Click to filter · or open tag

Research Focus:drug response / drug discovery· 3 Research Focus:benchmarking· 2 Assay:protein structure· 2 Approach:benchmark study· 1 Approach:contrastive learning· 1 Approach:deep learning· 1 Approach:machine learning (classical)· 1 Research Focus:software / tool· 1

Frequent Co-Authors

Charlotte Mary DeaneiD· 2 shared F. von DelftiD· 2 shared Jack ScantleburyiD· 2 shared Anna CarberyiD· 1 shared Barbara ZdraziliD· 1 shared Harold GrosjeaniD· 1 shared Lars Richter· 1 shared Lucy VostiD· 1 shared

Activity by Year

2019-2022 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 29 mutual co-author links

Click any collaborator node to inspect or filter shared papers

Focal author (center)Selected co-authorORCID co-authorUnlinked name

Curved chords = mutual co-authorship

2 shared papersBioinformatics

Charlotte Mary DeaneORCID

Mutual co-authors in cluster

Author profile →

3 papers

Recent·Most cited

A Step Towards Generalisability: Training a Machine Learning Scoring Function for Structure-Based Virtual Screening

Jack Scantlebury, Lucy Vost, Anna Carbery, Thomas E. Hadfield, Oliver M. Turnbull, Nathan Brown, Vijil Chenthamarakshan, Payel Das, Harold Grosjean, F. von Delft, Charlotte Mary Deane

The paper develops a machine learning scoring function for virtual screening that avoids dataset biases and demonstrates its ability to identify important binding interactions through input attribution.

bioRxiv · Oct 28, 2022 · PreprintBioinformatics

Dataset Augmentation Allows Deep Learning-Based Virtual Screening To Better Generalize To Unseen Target Classes, And Highlight Important Binding Interactions

Jack Scantlebury, Nathan Brown, F. von Delft, Charlotte Mary Deane

This paper shows that dataset augmentation improves deep learning models for virtual screening by making them better at generalizing to new protein targets and highlighting important binding interactions.

Journal of Chemical Information and Modeling · Jul 23, 2020Peer-reviewedMolecular Biology

Moving Targets: Monitoring Target Trends in Drug Discovery by Mapping Targets, GO Terms, and Diseases

Barbara Zdrazil, Lars Richter, Nathan Brown, Rajarshi Guha

This paper analyzes trends in drug discovery targets over 22 years by integrating data on protein families, Gene Ontology terms, and diseases to identify emerging research opportunities and potential drug repurposing candidates.

bioRxiv · Jul 3, 2019 · PreprintBioinformatics