About the Project
The biophysical methods used for screening are very effective in the identification of hit compounds, but their deployment require significant investments both in terms of equipment, logistic and resources reducing their applicability. In Silico approaches can help in addressing those issues and reduce the costs associated to the identification of hits. Several structure-based in silico screening methodologies have been developed in the past to evaluate drug-like molecules but they do not perform well when used to screen molecular fragments. The failure of conventional Molecular Mechanics (MM) scoring functions in assessing molecular interactions in low molecular complexity space being the most critical issue. Capitalising on the research already in progress at the Drug Discovery Unit, the student will combine advanced Computational Chemistry methodologies like Fragment Molecular Orbitals Quantum Mechanics (FMO-QM), Machine Learning and Deep generative modelling to develop a structure-based computational platform for the in silico screening and optimisation of fragments.
Deadline for applications is Friday 31st October 2020.
Please apply via our new Jisc Online Surveys application form which can be accessed here https://www.lifesci.dundee.ac.uk/apply-now
Daniel A. Erlanson, Stephen W. Fesik, Roderick E. Hubbard, Wolfgang Jahnke & Harren Jhoti
Nature Reviews Drug Discovery volume 15, pages 605–619 (2016)
Heifetz A. et al. (2020) Analyzing GPCR-Ligand Interactions with the Fragment Molecular Orbital (FMO) Method. Quantum Mechanics in Drug Discovery. Methods in Molecular Biology, vol 2114.
Marcus Olivecrona, Thomas Blaschke, Ola Engkvist, Hongming Chen
Molecular De Novo Design through Deep Reinforcement Learning
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