Efficient calculation of binding free-energies and druggability in challenging drug targets

Recent estimates put the development cost of a new medicine at over 2 billion dollars.1 Of the enormous number of compounds entering the drug discovery pipeline, only a tiny fraction reach the market. Failures in the later (clinical) stages are extremely costly. It is therefore of great importance to identify early on lead compounds that interact with biologically validated target and exert their mechanism without inducing adverse effects (toxicity). In this respect, computational approaches are expected to play an increasing role. Fast docking algorithms can now reliably predict binding modes.2 But the ranking of compounds based on affinity still requires a more in-depth understanding of the association mechanisms.
Francesco's group contributed to understand binding mechanisms and developed methods for accurate binding free energy calculations. Here we plan to build on our extensive experience in method development and drug discovery and develop a computational platform to achieve the required accuracy for semi-quantitative and quantitative scoring on cheap graphic processing units (GPUs). The proposed techniques will be experimentally validated by UCB by biophysical (microcalorimetry, SPR) and structural approaches (crystallography).