Summary
Ian Gould joined the Department of Chemistry in March 1995. Prior to his appointment he was an advanced research fellow in the Chemistry Department at the University of Manchester, 1992-1995. He had the good fortune to be a PDRA at the University of California at San Francisco (UCSF) 1989-1992 under the guidance of Peter Kollman.
Ian is co-founder of the Institute of Chemical Biology (https://www.imperial.ac.uk/chemical-biology ), which co-ordinates teaching and research activities in Chemical Biology. His research interests are in the broad area of computational chemical biology, this involves the development and application of a wide variety of techniques from Quantum Chemistry, Molecular Mechanics, Molecular Dynamics, Machine Learning and Artificial Intelligence.
Since 1989 Ian’s professional career has been associated with the Molecular Mechanics/ Molecular Dynamics package AMBER (http://ambermd.org), both the software package and the force fields, he is an author of the 1995 Cornell et all AMBER force field and the Lipid 14 force field. Ian’s research group have sort to leavarge the absolute peak performance from “State of the Art” computing. This included the early adoption of Beowoulf Linux clusters.Today our research interests are still at the forefront of what is possible using a large number of GPU's
Publications
Journals
Yang Z, Marston SB, Gould IR, 2023, Modulation of Structure and Dynamics of Cardiac Troponin by Phosphorylation and Mutations Revealed by Molecular Dynamics Simulations, Journal of Physical Chemistry B, Vol:127, ISSN:1520-6106, Pages:8736-8748
Abdel-Aty H, Gould IR, 2022, Large-Scale Distributed Training of Transformers for Chemical Fingerprinting, Journal of Chemical Information and Modeling, Vol:62, ISSN:1549-9596, Pages:4852-4862
Pavadai E, Rynkiewicz MJ, Yang Z, et al., 2022, Modulation of cardiac thin filament structure by phosphorylated troponin-I analyzed by protein-protein docking and molecular dynamics simulation., Archives of Biochemistry and Biophysics, Vol:725, ISSN:0003-9861, Pages:109282-109282
Abdel Aty H, Strutt R, Mcintyre N, et al., 2022, Machine learning platform for determining experimental lipid phase behaviour from small angle X-ray scattering patterns by pre-training on synthetic data, Digital Discovery, Vol:1, Pages:98-107
Conference
Pavadai E, Rynkiewicz MJ, Yang Z, et al., 2022, N-Terminal domains of cardiac troponin-I modulate thin filament structure, CELL PRESS, Pages:37-37, ISSN:0006-3495