We are offering a 4 year PhD studentship on the topic Developing in silico Fragment Screening methodologies.
Deadline for submissions is 31st October.
Applications must be made through School of Life Sciences application form. Please contact Dr Zuccotto (firstname.lastname@example.org) for further information.
The process to develop new medicines stars with the identification of small molecules that can modulate a biological target or pathway associated to the disease. In the last twenty years Fragment Based Drug Discovery (FBDD) emerged as a successful alternative to conventional high throughput screening for the generation of chemical hits for drug targets. FBDD offers the advantage of covering larger amounts of chemical space with a relatively small number of different compounds. Due to their smaller size, the molecular fragments are characterized by a limited number of interaction and surface complementarity resulting in a lower affinity to the biological target. Bespoke biophysical detection methods (like Nuclear Magnetic Resonance and Surface Plasma Resonance) are required to identify molecular fragments that bind to the drug target. Structural biology techniques are employed to establish their binding mode and to facilitate their optimization.
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.
Daniel A. Erlanson, Stephen W. Fesik, Roderick E. Hubbard, Wolfgang Jahnke & Harren Jhoti. Twenty years on: the impact of fragments on drug discovery
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. https://arxiv.org/abs/1704.07555