Surrogate docking: structure-based virtual screening at high throughput speed

Abstract

Structure-based screening using fully flexible docking is still too slow for large molecular libraries. High quality docking of a million molecule library can take days even on a cluster with hundreds of CPUs. This performance issue prohibits the use of fully flexible docking in the design of large combinatorial libraries. We have developed a fast structure-based screening method, which utilizes docking of a limited number of compounds to build a 2D QSAR model used to rapidly score the rest of the database. We compare here a model based on radial basis functions and a Bayesian categorization model. The number of compounds that need to be actually docked depends on the number of docking hits found. In our case studies reasonable quality models are built after docking of the number of molecules containing approximately 50 docking hits. The rest of the library is screened by the QSAR model. Optionally a fraction of the QSAR-prioritized library can be docked in order to find the true docking hits. The quality of the model only depends on the training set size - not on the size of the library to be screened. Therefore, for larger libraries the method yields higher gain in speed no change in performance. Prioritizing a large library with these models provides a significant enrichment with docking hits: it attains the values of approximately 13 and approximately 35 at the beginning of the score-sorted libraries in our two case studies: screening of the NCI collection and a combinatorial libraries on CDK2 kinase structure. With such enrichments, only a fraction of the database must actually be docked to find many of the true hits. The throughput of the method allows its use in screening of large compound collections and in the design of large combinatorial libraries. The strategy proposed has an important effect on efficiency but does not affect retrieval of actives, the latter being determined by the quality of the docking method itself.

Combined Virtual Screening Strategies

“…In preceding investigations it was shown that the use of the Bayesian classifiers trained on docking scores of experimentally known binders can be used for virtual screening [54][55][56][57]. In view of that, Filikov and coworkers also developed a surrogate docking method to pre-rank compounds to regular docking [58]. In this case the target protein was the cyclin-dependent kinase 2 (CDK2) and two sources of compounds were used: the NCI library and a combinatorial library composed by a set of 2,6,9-trisubstituted purines, a chemical family known to inhibit CDK2 as well as other kinases.…”

Neighborhood Behavior: Validation of Two‐Dimensional Molecular Similarity as a Predictor of Similar Biological Activities and Docking Scores

“…A similar concept of "surrogate docking" has been introduced recently by Filikov and co-workers [30]. They have developed a fast screening method, which utilizes docking of a limited number of compounds to build a 2D Quantitative Structure -Activity Relationships (QSAR) model used to rapidly score the rest of the database.…”

Virtual Screening: Are We There Yet?

“…Treating explicitly the receptor flexibility is computationally expensive and could worsen the result [45], and hence would make this approach a less desirable method for high throughput virtual screening. Despite this recent methods such as fast structure-based screening [61], that utilizes docking and 2D QSAR for rapid scoring is reportedly thousand times faster than regular docking and still demonstrates a comparable performance [62][63][64][65][66][67][68].…”