Professor in Chemistry
We use chemical informatics, computational chemistry, machine learning and AI to investigate molecules and to discover more about their structure, reactivity, analytical data and properties.
(Full list of publications)
Professor Goodman discusses his research
Publications
In silico prediction of protodeboronation by a mechanistic DFT-aided algorithm
– AIChE Annual Meeting, Conference Proceedings
(2023)
2023-November,
MolE8: finding DFT potential energy surface minima values from force-field optimised organic molecules with new machine learning representations
– Chemical science
(2022)
13,
7204
(doi: 10.1039/d1sc06324c)
Analysing a Billion Reactions with the RInChI
– Pure and Applied Chemistry
(2022)
94,
643
(doi: 10.1515/pac-2021-2008)
Towards quantifying the uncertainty in in silico predictions using Bayesian learning
– Computational Toxicology
(2022)
23,
100228
(doi: 10.1016/j.comtox.2022.100228)
RSC CICAG Open Chemical Science meeting: integrating chemical data from two symposia and a series of workshops
– Pure and Applied Chemistry
(2022)
94,
677
(doi: 10.1515/pac-2021-1003)
The DP5 probability, quantification and visualisation of structural uncertainty in single molecules.
– Chem Sci
(2022)
13,
3507
(doi: 10.1039/d1sc04406k)
The DP5 Probability, Quantification and Visualisation of Structural Uncertainty in Single Molecules
(2022)
A review of molecular representation in the age of machine learning
– WIREs Computational Molecular Science
(2022)
12,
(doi: 10.1002/wcms.1603)
A Review of Molecular Representation in the Age of Machine Learning
– Wiley Interdisciplinary Reviews Computational Molecular Science
(2022)
12,
ARTN e1603
(doi: 10.1002/wcms.1603)
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