vSDC: a method to improve early recognition in virtual screening when limited experimental resources are available

Abstract

BackgroundIn drug design, one may be confronted to the problem of finding hits for targets for which no small inhibiting molecules are known and only low-throughput experiments are available (like ITC or NMR studies), two common difficulties encountered in a typical academic setting. Using a virtual screening strategy like docking can alleviate some of the problems and save a considerable amount of time by selecting only top-ranking molecules, but only if the method is very efficient, i.e. when a good proportion of actives are found in the 1–10 % best ranked molecules.ResultsThe use of several programs (in our study, Gold, Surflex, FlexX and Glide were considered) shows a divergence of the results, which presents a difficulty in guiding the experiments. To overcome this divergence and increase the yield of the virtual screening, we created the standard deviation consensus (SDC) and variable SDC (vSDC) methods, consisting of the intersection of molecule sets from several virtual screening programs, based on the standard deviations of their ranking distributions.ConclusionsSDC allowed us to find hits for two new protein targets by testing only 9 and 11 small molecules from a chemical library of circa 15,000 compounds. Furthermore, vSDC, when applied to the 102 proteins of the DUD-E benchmarking database, succeeded in finding more hits than any of the four isolated programs for 13–60 % of the targets. In addition, when only 10 molecules of each of the 102 chemical libraries were considered, vSDC performed better in the number of hits found, with an improvement of 6–24 % over the 10 best-ranked molecules given by the individual docking programs.Graphical abstractIn drug design, for a given target and a given chemical library, the results obtained with different virtual screening programs are divergent. So how to rationally guide the experimental tests, especially when only a few number of experiments can be made? The variable Standard Deviation Consensus (vSDC) method was developed to answer this issue. Left panel the vSDC principle consists of intersecting molecule sets, chosen on the basis of the standard deviations of their ranking distributions, obtained from various virtual screening programs. In this study Glide, Gold, FlexX and Surflex were used and tested on the 102 targets of the DUD-E database. Right panel Comparison of the average percentage of hits found with vSDC and each of the four programs, when only 10 molecules from each of the 102 chemical libraries of the DUD-E database were considered. On average, vSDC was capable of finding 38 % of the findable hits, against 34 % for Glide, 32 % for Gold, 16 % for FlexX and 14 % for Surflex, showing that with vSDC, it was possible to overcome the unpredictability of the virtual screening results and to improve themElectronic supplementary materialThe online version of this article (doi:10.1186/s13321-016-0112-z) contains supplementary material, which is available to authorized users.

Experimental and In-Silico Investigation of Anti-Microbial Activity of 1-Chloro-2-Isocyanatoethane Derivatives of Thiomorpholine, Piperazine and Morpholine

“…However, prioritizing according to their ligand efficiencies shows that CTC, CPC and CMC will more efficiently inhibit the activities of the enzyme than ciprofloxacin and chlorobiocin, two approved drug whose mechanism of actions are by the inhibition of DNA gyrase [46]. …”

PBMDA: A novel and effective path-based computational model for miRNA-disease association prediction

“…For eliminating the node sets with the weak interaction, we set the threshold variable T to 0.5 based on the previous literature research [45], which means we did not take such links into consideration if the similarity between these nodes was less than 0.5. So that three weighted matrixes can be represented as:…”

“…For saving time, we set a parameter L to limit the maximum length of paths. According to previous literature research [45] and comparison experiments, we found that it was suitable to set L = 3 after trying different values from 2 to 4 increasingly, i.e. one path was not allowed to include more than three edges.…”

“…the contributions from the longer path should be cut down more sharply. So the decay function F decay ( p ) can be calculated as follows: where parameter α is a decay factor, which was set 2.26, according to previous literature research [45], and len ( p ) is the length of path p . After traversing all paths in the graph, each miRNA-disease pair could obtain a final score representing the association confidence between this miRNA and disease, i.e.…”

Epimetabolites: discovering metabolism beyond building and burning

“…Albeit, none of these virtual metabolites has a validated MS/MS spectrum, making it difficult to rank the best structure to the experimental MS/MS data. Several research groups have generated tools to predict MS/MS spectra from molecular structures, using chemical bond energies in the improved MetFrag tool [42], known dissociation rules in MS2Analyzer [43], hydrogen bond rearrangements in MS-FINDER [38], fragmentation tree calculations in CSI:FingerID [44] or machine learning strategies in CFM-ID [45 • ]. These tools are tested in regular competitions, the CASMI contests [46].…”