Large-Scale Systematic Analysis of 2D Fingerprint Methods and Parameters to Improve Virtual Screening Enrichments

TLDR

The authors conducted 157,872 virtual screens across 11 pharmaceutically relevant targets using Canvas to systematically evaluate 8 two‑dimensional fingerprinting methods, 13 atom‑typing schemes, 13 bit‑scaling rules, and 12 similarity metrics. The study found that fingerprint methods encoding local environment (MOLPRINT2D, Radial, Dendritic) and specific atom‑typing schemes (Daylight, Mol2, Carhart) generally yielded higher enrichment, with optimal settings varying by target and larger bit spaces reducing collisions and improving performance.

Abstract

A systematic virtual screening study on 11 pharmaceutically relevant targets has been conducted to investigate the interrelation between 8 two-dimensional (2D) fingerprinting methods, 13 atom-typing schemes, 13 bit scaling rules, and 12 similarity metrics using the new cheminformatics package Canvas. In total, 157 872 virtual screens were performed to assess the ability of each combination of parameters to identify actives in a database screen. In general, fingerprint methods, such as MOLPRINT2D, Radial, and Dendritic that encode information about local environment beyond simple linear paths outperformed other fingerprint methods. Atom-typing schemes with more specific information, such as Daylight, Mol2, and Carhart were generally superior to more generic atom-typing schemes. Enrichment factors across all targets were improved considerably with the best settings, although no single set of parameters performed optimally on all targets. The size of the addressable bit space for the fingerprints was also explored, and it was found to have a substantial impact on enrichments. Small bit spaces, such as 1024, resulted in many collisions and in a significant degradation in enrichments compared to larger bit spaces that avoid collisions.