Drug discovery could be significantly accelerated thanks to a new high precision machine-learning model, developed by an international collaboration of researchers, including the University of Warwick. The algorithm - partly devised by Dr James Kermode from WCPM - can accurately predict the interactions between a protein and a drug molecule based on a handful of reference experiments or simulations. Using just a few training references, it can predict whether or not a candidate drug molecule will bind to a target protein with 99% accuracy.
A. P. Bartok, S. De, C. Poelking, N. Bernstein, J. R. Kermode, G. Csányi and M. Ceriotti, Machine learning unifies the modeling of materials and molecules. Science Advances 3, e1701816 (2017).
The third Uncertainty Quantification and Mangagement Study Group with Industry will take place at Warwick Centre for Predictive Modelling from 13-15 December 2017.
The event is supported by the Uncertainty Quantification and Management in High Value Manufacturing special interest group of the Knowledge Transfer Network and by the School of Engineering at Warwick.
Dr Berk Onat has joined WCPM as a postdoc working with Dr James Kermode within the Horizon 2020 NOMAD Center of Excellence on materials informatics. He will be extending the capabilities of the NOMAD repository to a range of classical molecular dynamics codes as well as developing machine learning tools to exploit the resulting data. Welcome Berk!