The design of compounds with desirable properties - The anti-HIV case study.

Efficacy and safety are among the most desirable characteristics of an ideal drug. The tremendous increase in computing power and the entry of artificial intelligence into the field of computational drug design are accelerating the process of identifying, developing, and optimizing potential drugs. Here, we present novel approach to design new molecules with desired properties. We combined various neural networks and linear regression algorithms to build models for cytotoxicity and anti-HIV activity based on Continual Molecular Interior analysis (CoMIn) and Cinderella's Shoe (CiS) derived molecular descriptors. After validating the reliability of the models, a genetic algorithm was coupled with the Des-Pot Grid algorithm to generate new molecules from a predefined pool of molecular fragments and predict their bioactivity and cytotoxicity. This combination led to the proposal of 16 hit molecules with high anti-HIV activity and low cytotoxicity. The anti-SARS-CoV-2 activity of the hits was predicted.

A new glimpse on the active site of SARS-CoV-2 3CLpro, coupled with drug repurposing study.

Coronavirus disease 2019 (COVID-19) is caused by novel severe acute respiratory syndrome coronavirus (SARS-CoV-2). Its main protease, 3C-like protease (3CLpro), is an attractive target for drug design, due to its importance in virus replication. The analysis of the radial distribution function of 159 3CLpro structures reveals a high similarity index. A study of the catalytic pocket of 3CLpro with bound inhibitors reveals that the influence of the inhibitors is local, perturbing dominantly only residues in the active pocket. A machine learning based model with high predictive ability against SARS-CoV-2 3CLpro is designed and validated. The model is used to perform a drug-repurposing study, with the main aim to identify existing drugs with the highest 3CLpro inhibition power. Among antiviral agents, lopinavir, idoxuridine, paritaprevir, and favipiravir showed the highest inhibition potential. Enzyme - ligand interactions as a key ingredient for successful drug design.

Probing Adsorption of Dipeptides on Anatase in H2 O and D2 O: Thermodynamics and Molecular Geometry.

Considering the vast importance of peptide and protein interactions with inorganic surfaces, probing hydrogen bonding during their adsorption on metal oxide surfaces is a relevant task that could shed light on the essential features of their interplay. This work is devoted to studying the dipeptides' adsorption on anatase nanoparticles (ANs) in light and heavy water to reveal differences arising upon the change of the major hydrogen bonding carrier. Thermodynamic study of six native dipeptides' adsorption on ANs in both media shows a strong influence of the solvent on the Gibbs free energy and the effect of side-chain mobile protons on the entropy of the process. The adsorption is endothermic irrespective of the medium and is entropy-driven. Computer simulations of peptide adsorption in both media shows similarity in binding via an amino group and demonstrates structural features of protonated and deuterated peptides in obtained complexes. Calculated peptide- anatase nanoparticle (AN) descriptors indicate surface oxygens as points of peptide-nanoparticle contacts.

Use of the Complementarity Principle in Docking Procedures: A New Approach for Evaluating the Correctness of Binding Poses.

Even though the first docking procedures were developed almost 40 years ago, they are still under intense development, alongside with their validation. In this article, we are proposing the use of the quantum free-orbital AlteQ method in evaluating the correctness of ligand binding poses and their ranking. The AlteQ method calculates the electron density in the interspace between the ligand and the receptor, and since their interactions follow the maximum complementarity principle, an equation can be obtained, which describes these interactions. In this way, the AlteQ method evaluates the quality of contacts between the ligand and the receptor, bypasses the drawbacks of using ligand RMSD as a measure of docking quality, and can be considered as an improvement of the "fraction of recovered ligand-receptor contacts" method. Free Windows and Linux versions of the AlteQ program for assessing complementarity between the ligand and the receptor are available for download at www.chemosophia.com.

Design, synthesis, antibacterial evaluation, and computational studies of hybrid oxothiazolidin-1,2,4-triazole scaffolds.

Bacterial infections are a serious threat to human health due to the development of resistance against the presently used antibiotics. The problem of growing and widespread antibiotic resistance is only getting worse with the shortage of new classes of antibiotics, creating a substantial unmet medical need in the treatment of serious bacterial infections. Therefore, in the present work, we report 18 novel hybrid thiazolidine-1,2,4-triazole derivatives as DNA gyrase inhibitors. The derivatives were synthesized by multistep organic synthesis and characterized by spectroscopic methods (1 H and 13 C nuclear magnetic resonance and mass spectroscopy). The derivatives were tested for DNA gyrase inhibition, and the result emphasized that the synthesized derivatives have a tendency to inhibit the function of DNA gyrase. Furthermore, the compounds were also tested for antibacterial activity against three Gram-positive (Bacillus subtilis [NCIM 2063], Bacillus cereus [NCIM 2156], Staphylococcus aureus [NCIM 2079]) and two Gram-negative (Escherichia coli [NCIM 2065], Proteus vulgaris [NCIM 2027]) bacteria. The derivatives showed a significant-to-moderate antibacterial activity with noticeable antibiofilm efficacy. Quantitative structure-activity relationship (QSAR), ADME (absorption, distribution, metabolism, elimination) calculation, molecular docking, radial distribution function, and 2D fingerprinting were also performed to elucidate fundamental structural fragments essential for their bioactivity. These studies suggest that the derivatives 10b and 10n have lead antibacterial properties with significant DNA gyrase inhibitory efficacy, and they can serve as a starting scaffold for the further development of new broad-spectrum antibacterial agents.

Electron Density Analysis of SARS-CoV-2 RNA-Dependent RNA Polymerase Complexes.

The work is devoted to the study of the complementarity of the electronic structures of the ligands and SARS-CoV-2 RNA-dependent RNA polymerase. The research methodology was based on determining of 3D maps of electron densities of complexes using an original quantum free-orbital AlteQ approach. We observed a positive relationship between the parameters of the electronic structure of the enzyme and ligands. A complementarity factor of the enzyme-ligand complexes has been proposed. The console applications of the AlteQ complementarity assessment for Windows and Linux (alteq_map_enzyme_ligand_4_win.exe and alteq_map_enzyme_ligand_4_linux) are available for free at the ChemoSophia webpage.

Industrial and wildfire aerosol pollution over world heritage Lake Baikal.

Lake Baikal is the biggest reservoir of fresh water with unique flora and fauna; presently it is negatively affected by climate change, water warming, industrial emissions, shipping, touristic activities, and Siberian forest fires. The assessment of air pollution - related Baikal's ecosystem damage is an unsolved problem. Ship, based expedition exploring the Baikal atmospheric aerosol loading, was performed over the lake area in July 2018. We combine the aerosol near - water and vertical distributions over the Lake Baikal basin with meteorological observations and air mass transportation simulations. Lidar sounding of aerosol fields in the troposphere assesses the atmospheric background in the pristine areas and the pollution during fire-affected periods. Aerosol optical properties (scattering and spectral absorption) converted to the particle number size, black carbon (BC) mass, and Absorption Angstrom Exponent (AAE) provide the inside into aerosol characterization. Transport of industrial emissions from Krasnoyarsk and Irkutsk regions, and wildfire plumes from Republic of Yakutia relates the pollution sources to the increased concentrations of fine particle numbers, PM10 and BC mass over Southern and Northern/Central Baikal, respectively. The highest PM10 and BC are associated to the harbor and touristic areas of intensive shipping and residential biomass burning. Deposition estimates applied to aerosol data exhibit the pollution fluxes to water surface over the whole Baikal area. AAE marks the impact of coal combustion, residential biomass burning, and wildfires indicating the high pollution level of the Lake Baikal ecological system .

1,2,4-Triazole-conjugated 1,3,4-thiadiazole hybrid scaffolds: A potent ameliorant of carrageenan-induced inflammation by lessening proinflammatory mediators.

Inflammation acts as an alarming signal for the progression of various biological complications. Various reports in the literature have revealed that heterocycle-containing synthetic compounds have a restorative capability against acute and chronic inflammatory stages. In the current study, we synthesized a series of 1,2,4-triazole-conjugated 1,3,4-thiadiazole hybrid scaffolds and evaluated their impacts against carrageenan-induced paw edema and proinflammatory markers in Wistar rats. Further, 3D QSAR study (three-dimensional quantitative structure-activity relationships), ADMET (absorption, distribution, metabolism, and excretion) profiling, and docking studies were performed to determine the possible mechanism of the action of the derivatives. The study shows that the most active derivatives, 13f and 13g, have optimal logP, a higher anti-inflammatory activity score, and poor metabolism at various sites of cytochrome P450. The docking studies recommended that the synthesized compounds have a similar affinity as the ligands A307, 63X, and S58 to interact with tumor necrosis factor-α, COX-1, and COX-2. So, these molecules will definitely hold a promise for the future drug development initiative.

Adsorption of Native Amino Acids on Nanocrystalline TiO2: Physical Chemistry, QSPR, and Theoretical Modeling.

The affinity of biomolecules, such as peptides and proteins, with inorganic surfaces, is a fundamental topic in biotechnology and bionanotechnology. Amino acids are often used as "model" bits of peptides or proteins for studying their properties in different environments and/or developing functional surfaces. Despite great demand for knowledge about amino acid interactions with metal oxide surfaces, studies on the issue represent a fragmentary picture. In this paper, we describe amino acid adsorption on nanocrystalline anatase systematically at uniform conditions. Analysis of the Gibbs free adsorption energy indicated how the aliphatic, aromatic, polar, and charged side chain groups affect the binding affinity of the amino acids. Thermodynamic features of the l-amino acid adsorption receive thorough interpretation with calculated molecular descriptors. Theoretical modeling shows that amino acids complex with TiO2 nanoparticles as zwitterions via ammonium group.

Nonparametric chemical descriptors for the calculation of ligand-biopolymer affinities with machine-learning scoring functions.

The computational prediction of ligand-biopolymer affinities is a crucial endeavor in modern drug discovery and one that still poses major challenges. The choice of the appropriate computational method often reveals itself as a trade-off between accuracy and speed, with mathematical devices referred to as scoring functions being the fastest. Among the many shortcomings of scoring functions there is the lack of universal applicability to every molecular system. This is so largely due to their reliance on atom type perception and/or parametrization. This article proposes the use of nonparametric Model of Effective Radii of Atoms descriptors that can be readily computed for the entire Periodic Table and demonstrate that, in combination with machine learning algorithms, they can yield competitive performances and chemically meaningful insights.

Quinazoline based 1,3,5-triazine derivatives as cancer inhibitors by impeding the phosphorylated RET tyrosine kinase pathway: Design, synthesis, docking, and QSAR study.

The present research focused on designing a quinazoline skeleton, framed via 1,3,5-triazine derivatives (QBT) through field mapping and alignment studies. The QBT derivatives were synthesized via time- and cost-effective protocol. The 3D-QSAR study, computational physicochemical properties, and ADME calculation of the derivatives were performed to establish the affinity towards the biological system. Molecular docking in the adenosine triphosphate binding site of the RET tyrosine kinase domain (PDB ID: 7IVU) was studied to elucidate vital structural residues necessary for bioactivity. The derivatives were evaluated for anticancer potency against TPC-1 cells (thyroid cancer), MCF-7 cells (breast cancer), and one normal cell line (human foreskin fibroblasts) via 3-(4,5-dimethylthiazol-2-y1)-2,5-diphenyltetrazolium bromide assay followed by an in ovo CAM assay. The entire series of derivatives (8a-o) showed mild to significant anticancer potency against the selected cancer cell lines.

Antiplasmodial activity of hydroxyethylamine analogs: Synthesis, biological activity and structure activity relationship of plasmepsin inhibitors.

Malaria, particularly in endemic countries remains a threat to the human health and is the leading the cause of mortality in the tropical and sub-tropical areas. Herein, we explored new C2 symmetric hydroxyethylamine analogs as the potential inhibitors of Plasmodium falciparum (P. falciparum; 3D7) in in-vitro cultures. All the listed compounds were also evaluated against crucial drug targets, plasmepsin II (Plm II) and IV (Plm IV), enzymes found in the digestive vacuole of the P. falciparum. Analog 10f showed inhibitory activities against both the enzymes Plm II and Plm IV (Ki, 1.93 ±â€¯0.29 µM for Plm II; Ki, 1.99 ±â€¯0.05 µM for Plm IV). Among all these analogs, compounds 10g selectively inhibited the activity of Plm IV (Ki, 0.84 ±â€¯0.08 µM). In the in vitro screening assay, the growth inhibition of P. falciparum by both the analogs (IC50, 2.27 ±â€¯0.95 µM for 10f; IC50, 3.11 ±â€¯0.65 µM for 10g) displayed marked killing effect. A significant growth inhibition of the P. falciparum was displayed by analog 12c with IC50 value of 1.35 ±â€¯0.85 µM, however, it did not show inhibitory activity against either Plms. The hemolytic assay suggested that the active compounds selectively inhibit the growth of the parasite. Further, potent analogs (10f and 12c) were evaluated for their cytotoxicity towards mammalian HepG2 and vero cells. The selectivity index (SI) values were noticed greater than 10 for both the analogs that suggested their poor toxicity. The present study indicates these analogs as putative lead structures and could serve as crucial for the development of new drug molecules.

An approximation of the Cioslowski­Mixon bond order indexes using the AlteQ approach.

Fast and reliable prediction of bond orders in organic systems based upon experimentally measured quantities can be performed using electron density features at bond critical points (J Am Chem Soc 105:5061­5068, 1983; J Phys Org Chem 16:133­141, 2003; Acta Cryst B 61:418­428, 2005; Acta Cryst B 63:142­150, 2007). These features are outcomes of low-temperature high-resolution X-ray diffraction experiments. However, a time-consuming procedure of gaining these quantities makes the prediction limited. In the present work we have employed an empirical approach AlteQ (J Comput Aided Mol Des 22:489­505, 2008) for evaluation of electron density properties. This approach uses a simple exponential function derived from comparison of electron density, gained from high-resolution X-ray crystallography, and distance to atomic nucleus what allows calculating density distribution in time-saving manner and gives results which are very close to experimental ones. As input data AlteQ accepts atomic coordinates of isolated molecules or molecular ensembles (for instance, protein­protein complexes or complexes of small molecules with proteins, etc.). Using AlteQ characteristics we have developed regression models predicting Cioslowski­Mixon bond order (CMBO) indexes (J Am Chem Soc 113(42):4142­4145, 1991). The models are characterized by high correlation coefficients lying in the range from 0.844 to 0.988 dependently on the type of covalent bond, thereby providing a bonding quantification that is in reasonable agreement with that obtained by orbital theory. Comparative analysis of CMBOs approximated using topological properties of AlteQ and experimental electron densities has shown that the models can be used for fast determination of bond orders directly from X-ray crystallography data and confirmed that AlteQ characteristics can replace experimental ones with satisfactory extent of accuracy.

AlteQ: a new complementarity principle-centered method for the evaluation of docking poses.

Molecular docking is the most popular and widely used method for identifying novel molecules against a target of interest. However, docking procedures and their validation are still under intense development. In the present investigation, we evaluate a quantum free-orbital AlteQ method for evaluating docking complexes generated by taking EGFR complexes as an example. The AlteQ method calculates the electron density using Slater's type atomic contributions in the interspace between the receptor and the ligand. Since the interactions are determined by the overlap of electron clouds, they follow the complementarity principle, and an equation can be obtained that describes these interactions. The AlteQ method evaluates the quality of the interaction between the receptor and the ligand, how complementary the interactions are, and due to this, it is used to reject less realistic structures obtained by docking methods. Here, three different equations were used to determine the quality of the interactions in experimental complexes and docked complexes obtained using AutoDock Vina and AutoDock 4.2.6.Communicated by Ramaswamy H. Sarma.

Platinum(IV) compounds as potential drugs: a quantitative structure-activity relationship study.

Introduction: Machine learning methods, coupled with a tremendous increase in computer power in recent years, are promising tools in modern drug design and drug repurposing. Methods: Machine learning predictive models, publicly available at chemosophia.com, were used to predict the bioactivity of recently synthesized platinum(IV) complexes against different kinds of diseases and medical conditions. Two novel QSAR models based on the BiS algorithm are developed and validated, capable to predict activities against the SARS-CoV virus and its RNA dependent RNA polymerase. Results: The internal predictive power of the QSAR models was tested by 10-fold cross-validation, giving cross-R2 from 0.863 to 0.903. 38 different activities, ranging from antioxidant, antibacterial, and antiviral activities, to potential anti-inflammatory, anti-arrhythmic and anti-malarial activity were predicted for a series of eighteen platinum(IV) complexes. Conclusion: Complexes 1, 3 and 13 have high generalized optimality criteria and are predicted as potential SARS-CoV RNA dependent RNA polymerase inhibitors.

ToxAlerts: a Web server of structural alerts for toxic chemicals and compounds with potential adverse reactions.

The article presents a Web-based platform for collecting and storing toxicological structural alerts from literature and for virtual screening of chemical libraries to flag potentially toxic chemicals and compounds that can cause adverse side effects. An alert is uniquely identified by a SMARTS template, a toxicological endpoint, and a publication where the alert was described. Additionally, the system allows storing complementary information such as name, comments, and mechanism of action, as well as other data. Most importantly, the platform can be easily used for fast virtual screening of large chemical datasets, focused libraries, or newly designed compounds against the toxicological alerts, providing a detailed profile of the chemicals grouped by structural alerts and endpoints. Such a facility can be used for decision making regarding whether a compound should be tested experimentally, validated with available QSAR models, or eliminated from consideration altogether. The alert-based screening can also be helpful for an easier interpretation of more complex QSAR models. The system is publicly accessible and tightly integrated with the Online Chemical Modeling Environment (OCHEM, http://ochem.eu). The system is open and expandable: any registered OCHEM user can introduce new alerts, browse, edit alerts introduced by other users, and virtually screen his/her data sets against all or selected alerts. The user sets being passed through the structural alerts can be used at OCHEM for other typical tasks: exporting in a wide variety of formats, development of QSAR models, additional filtering by other criteria, etc. The database already contains almost 600 structural alerts for such endpoints as mutagenicity, carcinogenicity, skin sensitization, compounds that undergo metabolic activation, and compounds that form reactive metabolites and, thus, can cause adverse reactions. The ToxAlerts platform is accessible on the Web at http://ochem.eu/alerts, and it is constantly growing.

Establishment of models for reliability evaluation of 3CLpro ligand-receptor complexes with different binding sites.

Aim: Recent research shows that 3CLpro enzyme of SARS-CoV-2 is a significant target against COVID-19. Drug modeling allows the design of inhibitors of 3CLpro, but the accuracy of those methods remains unclear. Therefore, it is important to determine the trustworthiness of the designed ligand-receptor complexes. Method & materials: The authors built models for the reliability evaluation of 3CLpro complexes with ligands using an in-house developed AlteQ approach and complementarity principles. The models were based on 145 experimentally found 3CLpro complexes with ligands for five different binding sites. Result & conclusion: The obtained models correspond to linear regression with high values of correlation coefficients and can be successfully used to determine the reliability of the docked 3CLpro complexes with ligands.

COVID-19 is currently one of the most significant problems in the world. Research shows that the enzyme 3CLpro of SARS-CoV-2 is a main target for the development of potential anti-COVID-19 therapeutics. Drug modeling methods, especially docking studies, are utilized to design new compounds, however, the approaches have questionable precision. The authors present new models based on calculated electronic characteristics that can be used in drug design for the reliability assessment of SARS-CoV-2 3CLpro receptor­ligand complexes.

The study of EGFR-ligand complex electron property relationship with biological activity.

The present investigation grounded on estimation of electron properties of the structures of EGFR proteins-ligand complexes using our laboratory-developed methodology AlteQ approach, which describes the molecular electron density of the complex in space for a certain point in three-dimensional coordinates. Briefly, the system embodies molecular electron density as a sum of Slater's type atomic increments of the molecular system. Further, using this methodology, we calculated different electron characteristics of selected EGFR protein-ligand complexes and established the relationship between different electron properties with their experimental pharmacological activity value (pIC50). The study suggested that EGFR inhibitory activity has higher correlation with intermolecular contacts of H with pi-system of aromatic ring between protein and ligands. Therefore, this created model has impact to identify and design potential ligands against EGFR in anticancer drug discovery.Communicated by Ramaswamy H. Sarma.

Withasomniferol C, a new potential SARS-CoV-2 main protease inhibitor from the Withania somnifera plant proposed by in silico approaches.

Exploring potent herbal medicine candidates is a promising strategy for combating a pandemic in the present global health crisis. In Ayurveda (a traditional medicine system in India), Withania somnifera (WS) is one of the most important herbs and it has been used for millennia as Rasayana (a type of juice) for its wide-ranging health benefits. WS phytocompounds display a broad spectrum of biological activities (such as antioxidant, anticancer and antimicrobial) modulate detoxifying enzymes, and enhance immunity. Inspired by the numerous biological actions of WS phytocompounds, the present investigation explored the potential of the WS phytocompounds against the SARS-CoV-2 main protease (3CLpro). We selected 11 specific withanolide compounds, such as withaphysalin, withasomniferol, and withafastuosin, through manual literature curation against 3CLpro. A molecular similarity analysis showed their similarity with compounds that have an established inhibitory activity against the SARS-CoV-2. In silico molecular docking and molecular dynamics simulations elucidated withasomniferol C (WS11) as a potential candidate against SARS-CoV-2 3CLpro. Additionally, the present work also presents a new method of validating docking poses using the AlteQ method.

Proposition of a new allosteric binding site for potential SARS-CoV-2 3CL protease inhibitors by utilizing molecular dynamics simulations and ensemble docking.

The SARS-CoV-2 3CL protease (3CLpro) shows a high similarity with 3CL proteases of other beta-coronaviruses, such as SARS and MERS. It is the main enzyme involved in generating various non-structural proteins that are important for viral replication and is one of the most important proteins responsible for SARS-CoV-2 virulence. In this study, we have conducted an ensemble docking of molecules from the DrugBank database using both the crystallographic structure of the SARS-CoV-2 3CLpro, as well as five conformations obtained after performing a cluster analysis of a 300 ns molecular dynamics (MD) simulation. This procedure elucidated the inappropriateness of the active site for non-covalent inhibitors, but it has also shown that there exists an additional, more favorable, allosteric binding site, which could be a better target for non-covalent inhibitors, as it could prevent dimerization and activation of SARS-CoV-2 3CLpro. Two such examples are radotinib and nilotinib, tyrosine kinase inhibitors already in use for treatment of leukemia and which binding to the newly found allosteric binding site was also confirmed using MD simulations. Communicated by Ramaswamy H. Sarma.