The application of molecular benchmarking sets helps to assess the actual performance of virtual screening (VS) workflows. To improve the efficiency of structure-based VS approaches, the selection and optimization of various parameters can be guided by benchmarking. With the DEKOIS 2.0 library, we aim to further extend and complement the collection of publicly available decoy sets. Based on BindingDB bioactivity data, we provide 81 new and structurally diverse benchmark sets for a wide variety of different target classes. To ensure a meaningful selection of ligands, we address several issues that can be found in bioactivity data. We have improved our previously introduced DEKOIS methodology with enhanced physicochemical matching, now including the consideration of molecular charges, as well as a more sophisticated elimination of latent actives in the decoy set (LADS). We evaluate the docking performance of Glide, GOLD, and AutoDock Vina with our data sets and highlight existing challenges for VS tools. All DEKOIS 2.0 benchmark sets will be made accessible at http://www.dekois.com.

Two series of 4,6-diaryl-2-imino-1,2-dihydropyridine-3-carbonitriles and their isosteric 4,6-diaryl-2- oxo-1,2-dihydropyridine-3-carbonitriles were synthesized through a combinatorial approach. The prepared analogues were evaluated for their in vitro capacity to inhibit PDE3A and the growth of the human HT-29 colon adenocarcinoma tumor cell line. Compound 6-(4-bromophenyl)-4-(2-ethoxyphenyl)-2- imino-1,2-dihydropyridine-3-carbonitrile (Id) exhibited the strongest PDE3 inhibition when cGMP but not cAMP is the substrate with a IC50of 27 μM, which indicates a highly selective mechanism of enzyme inhibition. On the other hand, compound 6-(1,3-benzodioxol-5-yl)-4-(2-ethoxyphenyl)-2-imino-1,2- dihydropyridine-3-carbonitrile (Ii) was the most active in inhibiting colon tumor cell growth with a IC50 of 3 μM. The electronic effects, steric effects and conformational aspects of Id seem to be the most crucial for the PDE3 inhibition. Meanwhile, steric factors and the H-bonding capability seem to be the most important factors for tumor cell growth inhibitory activity. Conversely, there is no direct correlation between PDE3 inhibition and anticancer activity for the prepared compounds. An in silico docking experiment indicates the potential involvement of other potential molecular targets such as PIM-1 kinase to explain its tumor cell growth inhibitory activity.

The huge global expansion of the COVID-19 pandemic caused by the novel SARS-corona virus-2 is an extraordinary public health emergency. The unavailability of specific treatment against SARS-CoV-2 infection necessitates the focus of all scientists in this direction. The reported antiviral activities of guanidine alkaloids encouraged us to run a comprehensive in silico binding affinity of fifteen guanidine alkaloids against five different proteins of SARS-CoV-2, which we investigated. The investigated proteins are COVID-19 main protease (Mpro) (PDB ID: 6lu7), spike glycoprotein (PDB ID: 6VYB), nucleocapsid phosphoprotein (PDB ID: 6VYO), membrane glycoprotein (PDB ID: 6M17), and a non-structural protein (nsp10) (PDB ID: 6W4H). The binding energies for all tested compounds indicated promising binding affinities. A noticeable superiority for the pentacyclic alkaloids particularly, crambescidin 786 (5) and crambescidin 826 (13) has been observed. Compound 5 exhibited very good binding affinities against Mpro (ΔG = −8.05 kcal/mol), nucleocapsid phosphoprotein (ΔG = −6.49 kcal/mol), and nsp10 (ΔG = −9.06 kcal/mol). Compound 13 showed promising binding affinities against Mpro (ΔG = −7.99 kcal/mol), spike glycoproteins (ΔG = −6.95 kcal/mol), and nucleocapsid phosphoprotein (ΔG = −8.01 kcal/mol). Such promising activities might be attributed to the long ω-fatty acid chain, which may play a vital role in binding within the active sites. The correlation of c Log P with free binding energies has been calculated. Furthermore, the SAR of the active compounds has been clarified. The Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) studies were carried out in silico for the 15 compounds; most examined compounds showed optimal to good range levels of ADMET aqueous solubility, intestinal absorption and being unable to pass blood brain barrier (BBB), non-inhibitors of CYP2D6, non-hepatotoxic, and bind plasma protein with a percentage less than 90%. The toxicity of the tested compounds was screened in silico against five models (FDA rodent carcinogenicity, carcinogenic potency TD50, rat maximum tolerated dose, rat oral LD50, and rat chronic lowest observed adverse effect level (LOAEL)). All compounds showed expected low toxicity against the tested models. Molecular dynamic (MD) simulations were also carried out to confirm the stable binding interactions of the most promising compounds, 5 and 13, with their targets. In conclusion, the examined 15 alkaloids specially 5 and 13 showed promising docking, ADMET, toxicity and MD results which open the door for further investigations for them against SARS-CoV-2.

Leishmaniasis affects over 150 million people all over the world, especially in subtropical regions. Currently used antileishmanial synthesized drugs are associated with some drawbacks such as resistance and cytotoxicity, which hamper the chances of treatment. Furthermore, effective leishmanial vaccines are not well developed. Promising chemotherapy, either from natural or synthetic compounds, was or still is the most promising treatment. This review focuses on recent findings in drugs used for the treatment of leishmaniasis including; chemical and natural antileishmanial moieties, different potential targets, as well as various trials of vaccination development. Special emphasis has been paid to the mechanisms of the drugs, their safety and where possible, the structure-activity relationship to enable guided future drug discovery.

BackgroundStructure-based virtual screening techniques can help to identify new lead structures and complement other screening approaches in drug discovery. Prior to docking, the data (protein crystal structures and ligands) should be prepared with great attention to molecular and chemical details.ResultsUsing a subset of 18 diverse targets from the recently introduced DEKOIS 2.0 benchmark set library, we found differences in the virtual screening performance of two popular docking tools (GOLD and Glide) when employing two different commercial packages (e.g. MOE and Maestro) for preparing input data. We systematically investigated the possible factors that can be responsible for the found differences in selected sets. For the Angiotensin-I-converting enzyme dataset, preparation of the bioactive molecules clearly exerted the highest influence on VS performance compared to preparation of the decoys or the target structure. The major contributing factors were different protonation states, molecular flexibility, and differences in the input conformation (particularly for cyclic moieties) of bioactives. In addition, score normalization strategies eliminated the biased docking scores shown by GOLD (ChemPLP) for the larger bioactives and produced a better performance. Generalizing these normalization strategies on the 18 DEKOIS 2.0 sets, improved the performances for the majority of GOLD (ChemPLP) docking, while it showed detrimental performances for the majority of Glide (SP) docking.ConclusionsIn conclusion, we exemplify herein possible issues particularly during the preparation stage of molecular data and demonstrate to which extent these issues can cause perturbations in the virtual screening performance. We provide insights into what problems can occur and should be avoided, when generating benchmarks to characterize the virtual screening performance. Particularly, careful selection of an appropriate molecular preparation setup for the bioactive set and the use of score normalization for docking with GOLD (ChemPLP) appear to have a great importance for the screening performance. For virtual screening campaigns, we recommend to invest time and effort into including alternative preparation workflows into the generation of the master library, even at the cost of including multiple representations of each molecule.Graphical AbstractUsing DEKOIS 2.0 benchmark sets in structure-based virtual screening to probe the impact of molecular preparation and score normalization.Electronic supplementary materialThe online version of this article (doi:10.1186/s13321-015-0074-6) contains supplementary material, which is available to authorized users.