Abstract
Virtual screening (VS) methods, which became a common complement to the in vitro approaches in drug discovery projects, are naturally restricted by the compound libraries at hand while ignoring the wealth of compounds hidden in general chemical space. To close this gap, various methods for the exploration of chemical space have been proposed.
One such approach is Molpher, a software framework that uses the technique of molecular morphing. Molecular morphing generates a series of compounds called morphs that represent a gradual structural transition between two given compounds.
Because the exploration is driven solely by structural information, it disregards structurally diverse but possibly active compounds. Thus, we introduce the improvement of the algorithm where the exploration is driven by ligand biological activity rather than by its structure.
On its input, the method takes a set of known active and inactive compounds. In the preparatory phase, feature selection is applied to choose descriptors that likely discriminate between active and inactive compounds.
These features are then used to define a reference point towards which the exploration is directed. In the exploration phase, morphs are generated from all active compounds and Pareto-ranking scheme is applied to accept morphs for the next generation of molecular morphing.
This iterative process results in structurally diverse molecules that share characteristic features of actives that separate them from inactives. The method was tested on four datasets from the PubChem BioAssay database.
The results indicate that an activity-based exploration technique is able to generate structurally diverse compounds close to the selected point in the activity space. Thus, this technique is suitable for the generation of virtual libraries that can be further optimized and subsequently screened.