John Bradshaw

Publication Activity (10 Years)

Years Active: 1993-2024
Publications (10 Years): 13

Top Topics

Frequent Itemsets

Machine Learning Models

Top Venues

Publications

Joonyoung F. Joung, Mun Hong Fong, Jihye Roh, Zhengkai Tu, John Bradshaw, Connor W. Coley
Beyond Major Product Prediction: Reproducing Reaction Mechanisms with Machine Learning Models Trained on a Large-Scale Mechanistic Dataset. CoRR (2024)
Samuel Goldman, John Bradshaw, Jiayi Xin, Connor W. Coley
Prefix-Tree Decoding for Predicting Mass Spectra from Molecules. NeurIPS (2023)
Samuel Goldman, John Bradshaw, Jiayi Xin, Connor W. Coley
Prefix-tree Decoding for Predicting Mass Spectra from Molecules. CoRR (2023)
Natalie Maus, Haydn T. Jones, Juston S. Moore, Matt J. Kusner, John Bradshaw, Jacob R. Gardner
Local Latent Space Bayesian Optimization over Structured Inputs. CoRR (2022)
Natalie Maus, Haydn Jones, Juston Moore, Matt J. Kusner, John Bradshaw, Jacob R. Gardner
Local Latent Space Bayesian Optimization over Structured Inputs. NeurIPS (2022)
John Bradshaw, Brooks Paige, Matt J. Kusner, Marwin H. S. Segler, José Miguel Hernández-Lobato
Barking up the right tree: an approach to search over molecule synthesis DAGs. CoRR (2020)
John Bradshaw, Brooks Paige, Matt J. Kusner, Marwin H. S. Segler, José Miguel Hernández-Lobato
Barking up the right tree: an approach to search over molecule synthesis DAGs. NeurIPS (2020)
John Bradshaw, Matt J. Kusner, Brooks Paige, Marwin H. S. Segler, José Miguel Hernández-Lobato
Generating Molecules via Chemical Reactions. DGS@ICLR (2019)
John Bradshaw, Brooks Paige, Matt J. Kusner, Marwin H. S. Segler, José Miguel Hernández-Lobato
A Model to Search for Synthesizable Molecules. CoRR (2019)
John Bradshaw, Brooks Paige, Matt J. Kusner, Marwin H. S. Segler, José Miguel Hernández-Lobato
A Model to Search for Synthesizable Molecules. NeurIPS (2019)
Yingzhen Li, John Bradshaw, Yash Sharma
Are Generative Classifiers More Robust to Adversarial Attacks? ICML (2019)
John Bradshaw, Matt J. Kusner, Brooks Paige, Marwin H. S. Segler, José Miguel Hernández-Lobato
A Generative Model For Electron Paths. ICLR (Poster) (2019)
John Bradshaw, Matt J. Kusner, Brooks Paige, Marwin H. S. Segler, José Miguel Hernández-Lobato
Predicting Electron Paths. CoRR (2018)
James Chen, John Bradshaw, Paul G. Nagy
Has the Picture Archiving and Communication System (PACS) Become a Commodity? J. Digit. Imaging 24 (1) (2011)
Jenny Chen, John D. Holliday, John Bradshaw
A Machine Learning Approach to Weighting Schemes in the Data Fusion of Similarity Coefficients. J. Chem. Inf. Model. 49 (2) (2009)
Valerie J. Gillet, Peter Willett, John Bradshaw
Similarity Searching Using Reduced Graphs. J. Chem. Inf. Comput. Sci. 43 (2) (2003)
Gavin Harper, John Bradshaw, John C. Gittins, Darren V. S. Green, Andrew R. Leach
Prediction of Biological Activity for High-Throughput Screening Using Binary Kernel Discrimination. J. Chem. Inf. Comput. Sci. 41 (5) (2001)
Valerie J. Gillet, Peter Willett, John Bradshaw, Darren V. S. Green
Selecting Combinatorial Libraries to Optimize Diversity and Physical Properties. J. Chem. Inf. Comput. Sci. 39 (1) (1999)
Martin J. Bayley, Valerie J. Gillet, Peter Willett, John Bradshaw, Darren V. S. Green
Computational analysis of molecular diversity for drug discovery. RECOMB (1999)
Andrew R. Leach, John Bradshaw, Darren V. S. Green, Michael M. Hann, John J. Delany III
Implementation of a System for Reagent Selection and Library Enumeration, Profiling, and Design. J. Chem. Inf. Comput. Sci. 39 (6) (1999)
Valerie J. Gillet, David J. Wild, Peter Willett, John Bradshaw
Similarity and Dissimilarity Methods for Processing Chemical Structure Databases. Comput. J. 41 (8) (1998)
Valerie J. Gillet, Peter Willett, John Bradshaw
Identification of Biological Activity Profiles Using Substructural Analysis and Genetic Algorithms. J. Chem. Inf. Comput. Sci. 38 (2) (1998)
Valerie J. Gillet, Peter Willett, John Bradshaw
The Effectiveness of Reactant Pools for Generating Structurally-Diverse Combinatorial Libraries. J. Chem. Inf. Comput. Sci. 37 (4) (1997)
Shaun N. Jordan, Andrew R. Leach, John Bradshaw
The Application of Neural Networks in Conformational Analysis, 1. Prediction of Minimum and Maximum Interatomic Distances. J. Chem. Inf. Comput. Sci. 35 (3) (1995)
Eleanor M. Ricketts, John Bradshaw, Mike Hann, Fiona Hayes, Neil Tanna, David M. Ricketts
Comparison of conformations of small molecule structures from the Protein Data Bank with those generated by Concord, Cobra, ChemDBS-3D, and Converter and those extracted from the Cambridge Structural Database. J. Chem. Inf. Comput. Sci. 33 (6) (1993)