http://Mcule.com-a web service providing you a fast and cost-effective way to identify and order new drug candidates has been recently launched. The service is available for the public and it provides a comprehensive, carefully curated database of molecules immediately available for virtual screening. Several screening tools have been already implemented and more will be added on a weekly/monthly basis. Screening tools can be seamlessly integrated into a virtual screening workflow. Calculations are running on cloud machines providing a practically infinite number of CPUs and thus fast access to the screening results. Hits from the virtual screens can be ordered.

The docking accuracy of Glide was evaluated using 16 different docking protocols on 190 protein-fragment complexes representing 78 targets. Standard precision docking (Glide SP) based protocols showed the best performance. The average root-mean-square deviation (rmsd) between the docked and cocrystallized poses achieved by Glide SP with pre- and postprocessing was 1.17 A, and an acceptable binding mode with rmsd < 2 A could be found in 80% of the cases. Comparison of the docking results produced by different protocols suggests that the sampling efficacy of Glide is adequate for fragment docking. The docking accuracy seems to be limited by the performance of scoring schemes, which is supported by the weak correlation between experimental binding affinities and GlideScores. Cross-docking experiments performed on 8 targets represented by 63 complexes revealed that Glide SP gave similar results to that of the computationally more intensive Glide XP. The average rmsd achieved by Glide SP with pre- and postprocessing was 2.06 A, and an acceptable binding mode with rmsd < 2 A could be found in 63% of the cases. These cross-docking results were improved significantly selecting the optimal X-ray structure for each target (average rmsd = 1.3 A, success rate = 77%), indicating the importance of enrichment studies and the use of multiple X-ray structures in virtual fragment screening.

Intrinsically disordered/unstructured proteins exist in a highly flexible conformational state largely devoid of secondary structural elements and tertiary contacts. Despite their lack of a well defined structure, these proteins often fulfill essential regulatory functions. The intrinsic lack of structure confers functional advantages on these proteins, allowing them to adopt multiple conformations and to bind to different binding partners. The structural flexibility of disordered regions hampers efforts solving structures at high resolution by X-ray crystallography and/or NMR. Removing such proteins/regions from high-throughput structural genomics pipelines would be of significant benefit in terms of cost and success rate. In this paper we outline the theoretical background of structural disorder, and review bioinformatic predictors that can be used to delineate regions most likely to be amenable for structure determination. The primary focus of our review is the interpretation of prediction results in a way that enables segmentation of proteins to separate ordered domains from disordered regions.

Ligand-based approaches are particularly important in the hit identification process of drug discovery when no structural information on the target is available. Pharmacophore descriptors that use a topological representation of the ligands are usually fast enough to screen large compound libraries effectively when seeking novel lead candidates. One example of this kind is the Feature Tree descriptor, a reduced graph representation implemented in the FTrees software. In this study, we tested the screening efficiency of FTrees by both retrospective and prospective screens using known histamine H4 antagonists and serotonin transporter (SERT) inhibitors as query molecules. Our results demonstrate that FTrees can effectively find actives. Particularly when combined with a subsequent 2D fingerprint-based diversity selection, FTrees was found to be extremely effective at discovering a diverse set of scaffolds. Prospective screening of our in-house compound deck provided several novel H4 and SERT ligands that could serve as suitable starting points for further optimization.

Physicochemical properties are fundamental to predict the pharmacokinetic and pharmacodynamic behavior of drug candidates. Easily calculated descriptors such as molecular weight and logP have been found to correlate with the success rate of clinical trials. These properties have been previously shown to highlight a sweet-spot in the chemical space associated with favorable pharmacokinetics, which is superior against other regions during hit identification and optimization. In this study, we applied self-organizing maps (SOMs) trained on sixteen calculated properties of a subset of known drugs for the analysis of commercially available compound databases, as well as public biological and chemical databases frequently used for drug discovery. Interestingly, several regions of the property space have been identified that are highly overrepresented by commercially available chemical libraries, while we found almost completely unoccupied regions of the maps (commercially neglected chemical space resembling the properties of known drugs). Moreover, these underrepresented portions of the chemical space are compatible with most rigorous property filters applied by the pharma industry in medicinal chemistry optimization programs. Our results suggest that SOMs may be directly utilized in the strategy of library design for drug discovery to sample previously unexplored parts of the chemical space to aim at yet-undruggable targets. Graphic abstract