Abstract
MotivationNowadays, virtual screening (VS) plays a major role in the process of drug development. Nonetheless, an accurate estimation of binding affinities, which is crucial at all stages, is not trivial and may require target-specific fine-tuning. Furthermore, drug design also requires improved predictions for putative secondary targets among which is Estrogen Receptor alpha (ERα).ResultsVS based on combinations of Structure-Based VS (SBVS) and Ligand-Based VS (LBVS) is gaining momentum to improve VS performances. In this study, we propose an integrated approach using ligand docking on multiple structural ensembles to reflect receptor flexibility. Then, we investigate the impact of the two different types of features (structure-based and ligand molecular descriptors) on affinity predictions using a random forest algorithm. We find that ligand-based features have lower predictive power (rP = 0.69, R2 = 0.47) than structure-based features (rP = 0.78, R2 = 0.60). Their combination maintains high accuracy (rP = 0.73, R2 = 0.50) on the internal test set, but it shows superior robustness on external datasets. Further improvement and extending the training dataset to include xenobiotics, leads to a novel high-throughput affinity prediction method for ERα ligands (rP = 0.85, R2 = 0.71). The presented prediction tool is provided to the community as a dedicated satellite of the @TOME server in which one can upload a ligand dataset in mol2 format and get ligand docked and affinity predicted.Availability and implementation http://edmon.cbs.cnrs.fr.Supplementary information Supplementary data are available at Bioinformatics online.
Highlights
Despite the fact that the efforts invested in drug development have constantly increased during the last decades, the number of drug approvals stays almost constant (Munos, 2009)
In order to set up and evaluate this development, we focused on a well-known therapeutic target—the estrogen receptor Estrogen Receptor alpha (ERa)
The predictive power of the four different scoring functions implemented in the @TOME server was assessed
Summary
Despite the fact that the efforts invested in drug development have constantly increased during the last decades, the number of drug approvals stays almost constant (Munos, 2009). About 81% of all new drug candidates fail (DiMasi et al, 2010), mainly due to a lack of drug efficiency and/or side effects associated with off-target binding. In order to reduce time and cost of drug development process, various computer aided methods have been implemented. Namely structure-based and ligand-based virtual screening, are widely used (Lavecchia, 2015; Lionta et al, 2014). They are routinely used for hit identification in order to prioritize compounds for experimental assays and they are gaining interest for lead optimization.
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