Gareth John Conduit

According to our database, Gareth John Conduit authored at least 16 papers between 2018 and 2026.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2026

Comparing massively-multitask regression algorithms for drug discovery.

Eric J. Martin, Xiang-Wei Zhu, Patrick Riley, Steven Kearnes, Ekaterina A. Sosnina, Li Tian, Chi-Ming Che, Zijian Wang, Ying Wei, Thomas M. Whitehead, Gareth John Conduit, Matthew D. Segall

J. Comput. Aided Mol. Des., December, 2026

Talk Freely, Execute Strictly: Schema-Gated Agentic AI for Flexible and Reproducible Scientific Workflows.

Joel Strickland, Arjun Vijeta, Chris Moores, Oliwia Bodek, Bogdan Nenchev, Thomas M. Whitehead, Charles Phillips, Karl Tassenberg, Gareth John Conduit, Ben Pellegrini

CoRR, March, 2026

2024

A compact yet flexible design space for large-scale nonperiodic 3D woven composites based on a weighted game for generating candidate tow architectures.

Zhenpei Wang, Brian N. Cox, Shemuel Joash Kuehsamy, Mark Hyunpong Jhon, Olivier Sudre, N. Sridhar, Gareth John Conduit

Comput. Aided Des., February, 2024

Quantifying the Benefits of Imputation over QSAR Methods in Toxicology Data Modeling.

Thomas M. Whitehead, Joel Strickland, Gareth John Conduit, Alexandre Borrel, Daniel Mucs, Irene Baskerville-Abraham

J. Chem. Inf. Model., 2024

Artificial intelligence (AI) futures: India-UK collaborations emerging from the 4th Royal Society Yusuf Hamied workshop.

Yogesh K. Dwivedi, Laurie Hughes, Harshad K. D. H. Bhadeshia, Sophia Ananiadou, Anthony G. Cohn, Jacqueline M. Cole, Gareth John Conduit, Maunendra Sankar Desarkar, Xinwei Wang

Int. J. Inf. Manag., 2024

Modelling the nicotine pharmacokinetic profile for e-cigarettes using real time monitoring of consumers' physiological measurements and mouth level exposure.

Krishna Prasad, Allen Griffiths, Kavya Agrawal, Michael McEwan, Flavio Macci, Marco Ghisoni, Matthew Stopher, Matthew Napleton, Joel Strickland, David Keating, Thomas M. Whitehead, Gareth John Conduit, Stacey Murray, Lauren Edward

BioData Min., 2024

2023

Unveil the unseen: Exploit information hidden in noise.

Bahdan Zviazhynski, Gareth John Conduit

Appl. Intell., May, 2023

Probabilistic selection and design of concrete using machine learning.

Jessica C. Forsdyke, Bahdan Zviazhynski, Janet M. Lees, Gareth John Conduit

CoRR, 2023

2021

Imputation of sensory properties using deep learning.

Samar Mahmoud, Benedict W. J. Irwin, Dmitriy Chekmarev, Shyam Vyas, Jeff Kattas, Thomas M. Whitehead, Tamsin Mansley, Jack Bikker, Gareth John Conduit, Matthew D. Segall

J. Comput. Aided Mol. Des., 2021

2020

Machine learning to predict mesenchymal stem cell efficacy for cartilage repair.

Yu Yang Fredrik Liu, Yin Lu, Steve Oh, Gareth John Conduit

PLoS Comput. Biol., 2020

Predicting the state of charge and health of batteries using data-driven machine learning.

Man-Fai Ng, Jin Zhao, Qingyu Yan, Gareth John Conduit, Zhi Wei Seh

Nat. Mach. Intell., 2020

Practical Applications of Deep Learning To Impute Heterogeneous Drug Discovery Data.

Benedict W. J. Irwin, Julian R. Levell, Thomas M. Whitehead, Matthew D. Segall, Gareth John Conduit

J. Chem. Inf. Model., 2020

2019

Imputation of Assay Bioactivity Data Using Deep Learning.

Thomas M. Whitehead, Benedict W. J. Irwin, Peter A. Hunt, Matthew D. Segall, Gareth John Conduit

J. Chem. Inf. Model., 2019

Fragment Graphical Variational AutoEncoding for Screening Molecules with Small Data.

John Armitage, Leszek J. Spalek, Malgorzata Nguyen, Mark Nikolka, Ian E. Jacobs, Lorena Marañón, Iyad Nasrallah, Guillaume Schweicher, Ivan Dimov, Dimitrios Simatos, Iain McCulloch, Christian B. Nielsen, Gareth John Conduit, Henning Sirringhaus

CoRR, 2019

2018

Design of a nickel-base superalloy using a neural network.

Bryce D. Conduit, Nick G. Jones, Howard J. Stone, Gareth John Conduit

CoRR, 2018

Probabilistic design of a molybdenum-base alloy using a neural network.

Bryce D. Conduit, Nick G. Jones, Howard J. Stone, Gareth John Conduit

CoRR, 2018