Abstract
BackgroundLigand-based virtual screening experiments are an important task in the early drug discovery stage. An ambitious aim in each experiment is to disclose active structures based on new scaffolds. To perform these "scaffold-hoppings" for individual problems and targets, a plethora of different similarity methods based on diverse techniques were published in the last years. The optimal assignment approach on molecular graphs, a successful method in the field of quantitative structure-activity relationships, has not been tested as a ligand-based virtual screening method so far.ResultsWe evaluated two already published and two new optimal assignment methods on various data sets. To emphasize the "scaffold-hopping" ability, we used the information of chemotype clustering analyses in our evaluation metrics. Comparisons with literature results show an improved early recognition performance and comparable results over the complete data set. A new method based on two different assignment steps shows an increased "scaffold-hopping" behavior together with a good early recognition performance.ConclusionThe presented methods show a good combination of chemotype discovery and enrichment of active structures. Additionally, the optimal assignment on molecular graphs has the advantage to investigate and interpret the mappings, allowing precise modifications of internal parameters of the similarity measure for specific targets. All methods have low computation times which make them applicable to screen large data sets.
Highlights
Ligand-based virtual screening experiments are an important task in the early drug discovery stage
The presented methods show a good combination of chemotype discovery and enrichment of active structures
Evaluation of Virtual screening (VS) Performance we describe the metrics for the evaluation of VS experiments used in our work
Summary
Ligand-based virtual screening experiments are an important task in the early drug discovery stage. An ambitious aim in each experiment is to disclose active structures based on new scaffolds To perform these "scaffold-hoppings" for individual problems and targets, a plethora of different similarity methods based on diverse techniques were published in the last years. The top-ranked structures of VS experiments are further analysed in biological assays to elucidate their activities The methods for this purpose can be divided into structure-based and ligandbased VS techniques. There are doubts about the ability of docking approaches to predict the affinity or even the rank of the structures [6] In spite of these negative examples, there exists a remarkable list of successful structure-based VS stories [7]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
