The primary aim of drug discovery is to find novel molecules that are active against a target of therapeutic relevance and that are not covered by any existing patents (1).  Due to the increasing cost of research and development in the later stages of drug discovery, and the increase in drug candidates failing at these stages, there is a desire to select the most diverse set of active molecules at the earliest stage of drug discovery, to maximise the chance of finding a molecule that can be optimised into a successful drug (2,3). Computational methods that are both accurate and efficient are one approach to this problem and can augment experiment approaches in deciding which molecules to take forward.

But what do we mean by a “novel” compound? When prioritising molecules for synthesis which characteristics do we want to be different?  It was once common to select subsets of hits to maximise chemical diversity in order to cover as much chemical space as possible (4).  These novel lead molecules could subsequently be optimised, the idea that maximising the coverage of chemical space would maximise the chance of finding a molecule that could be optimised successfully. More recently however, the focus has shifted to “biodiversity”: diversity in terms of how the molecule interacts with the protein (1). Activity cliffs, pairs of molecules that are structurally and chemically similar but have a large difference in potency, indicate that chemical diversity may not be the best descriptor to identify molecules that interact with the target in sufficiently diverse ways. The molecules to be taken forward should be both active against the target and diverse in terms of how they interact with the target, and the parts of the binding site the molecule interacts with.