Bayesian optimization for accelerated drug discovery

The space of potential drug-like molecules is vast, precluding 'random-walk'-like searches from achieving any reasonable effectiveness. Active search techniques have been increasing in popularity in recent years as a method for accelerating the discovery of novel pharmaceutical molecules. By providing an effective method for prioritizing molecules within the discovery process, the efficiency of the discovery process can be dramatically improved. In this paper, we describe the use of Bayesian optimization, a method for iterative optimization of black-box functions for achieving this end, balancing the exploitation of current knowledge acquired from data, with the acquisition of new knowledge about which little is known.