Abstract
Virtual screening (VS) of databases of chemical compounds has become a common step in the drug discovery process. Ligand-based virtual screening is a variant of VS where similarity to known active compounds is utilized in the discovery of new bioactive molecules.
The cornerstone, which determines success of virtual screening, is the used molecular similarity measure. Currently, there is no superior approach to modeling molecular similarity and design of new similarity approaches is an active research field in cheminformatics.
Therefore, proper benchmarking is of utter importance. In this paper, we describe common pitfalls of current approach to benchmarking of new methods.
We focus on the importance of reproducibility and design of benchmarking datasets. Moreover, we identify the dataset difficulty as an important, yet not wildly utilized, property of the benchmarking data.
To solve the identified issues we present a new benchmarking platform. The platform implements most commonly used molecular representations and includes datasets of varying difficulty levels as well as scripts which make the platform easy to use and extend.
The existing representations are benchmarked using the proposed platform and results are presented. The benchmarking platform is available at https:// github.com/skodapetr/ lbvs-environment.