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. - Eleanor M. Ricketts; John Bradshaw; Mike Hann; Fiona Hayes; Neil Tanna; David M. Ricketts (J. Chem. Inf. Comput. Sci., 1993)

The Application of Neural Networks in Conformational Analysis, 1. Prediction of Minimum and Maximum Interatomic Distances. - Shaun N. Jordan; Andrew R. Leach; John Bradshaw (J. Chem. Inf. Comput. Sci., 1995)

The Effectiveness of Reactant Pools for Generating Structurally-Diverse Combinatorial Libraries. - Valerie J. Gillet; Peter Willett 002; John Bradshaw (J. Chem. Inf. Comput. Sci., 1997)

Similarity and Dissimilarity Methods for Processing Chemical Structure Databases. - Valerie J. Gillet; David J. Wild 002; Peter Willett 002; John Bradshaw (Comput. J., 1998)

Identification of Biological Activity Profiles Using Substructural Analysis and Genetic Algorithms. - Valerie J. Gillet; Peter Willett 002; John Bradshaw (J. Chem. Inf. Comput. Sci., 1998)

Computational analysis of molecular diversity for drug discovery. - Martin J. Bayley; Valerie J. Gillet; Peter Willett 002; John Bradshaw; Darren V. S. Green (RECOMB, 1999)

Selecting Combinatorial Libraries to Optimize Diversity and Physical Properties. - Valerie J. Gillet; Peter Willett 002; John Bradshaw; Darren V. S. Green (J. Chem. Inf. Comput. Sci., 1999)

Implementation of a System for Reagent Selection and Library Enumeration, Profiling, and Design. - Andrew R. Leach; John Bradshaw; Darren V. S. Green; Michael M. Hann; John J. Delany III (J. Chem. Inf. Comput. Sci., 1999)

Prediction of Biological Activity for High-Throughput Screening Using Binary Kernel Discrimination. - Gavin Harper; John Bradshaw; John C. Gittins; Darren V. S. Green; Andrew R. Leach (J. Chem. Inf. Comput. Sci., 2001)

Similarity Searching Using Reduced Graphs. - Valerie J. Gillet; Peter Willett 002; John Bradshaw (J. Chem. Inf. Comput. Sci., 2003)

A Machine Learning Approach to Weighting Schemes in the Data Fusion of Similarity Coefficients. - Jenny Chen; John D. Holliday; John Bradshaw (J. Chem. Inf. Model., 2009)

Has the Picture Archiving and Communication System (PACS) Become a Commodity? - James Chen; John Bradshaw; Paul G. Nagy (J. Digit. Imaging, 2011)

Predicting Electron Paths. - John Bradshaw; Matt J. Kusner; Brooks Paige; Marwin H. S. Segler; José Miguel Hernández-Lobato (CoRR, 2018)

A Generative Model For Electron Paths. - John Bradshaw; Matt J. Kusner; Brooks Paige; Marwin H. S. Segler; José Miguel Hernández-Lobato (ICLR (Poster), 2019)

Generating Molecules via Chemical Reactions. - John Bradshaw; Matt J. Kusner; Brooks Paige; Marwin H. S. Segler; José Miguel Hernández-Lobato (DGS@ICLR, 2019)

Are Generative Classifiers More Robust to Adversarial Attacks? - Yingzhen Li; John Bradshaw; Yash Sharma 001 (ICML, 2019)

A Model to Search for Synthesizable Molecules. - John Bradshaw; Brooks Paige; Matt J. Kusner; Marwin H. S. Segler; José Miguel Hernández-Lobato (NeurIPS, 2019)

Barking up the right tree: an approach to search over molecule synthesis DAGs. - John Bradshaw; Brooks Paige; Matt J. Kusner; Marwin H. S. Segler; José Miguel Hernández-Lobato (NeurIPS, 2020)

Local Latent Space Bayesian Optimization over Structured Inputs. - Natalie Maus; Haydn Thomas Jones; Juston Moore; Matt J. Kusner; John Bradshaw; Jacob R. Gardner (NeurIPS, 2022)

Prefix-Tree Decoding for Predicting Mass Spectra from Molecules. - Samuel Goldman; John Bradshaw; Jiayi Xin; Connor W. Coley (NeurIPS, 2023)

Beyond Major Product Prediction: Reproducing Reaction Mechanisms with Machine Learning Models Trained on a Large-Scale Mechanistic Dataset. - Joonyoung F. Joung; Mun Hong Fong; Jihye Roh; Zhengkai Tu; John Bradshaw; Connor W. Coley (CoRR, 2024)

Challenging reaction prediction models to generalize to novel chemistry. - John Bradshaw; Anji Zhang; Babak A. Mahjour; David E. Graff; Marwin H. S. Segler; Connor W. Coley (CoRR, 2025)