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.

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.

Exploring potential inhibitors against Kyasanur forest disease by utilizing molecular dynamics simulations and ensemble docking.

Kyasanur forest disease (KFD) is a tick-borne, neglected tropical disease, caused by KFD virus (KFDV) which belongs to Flavivirus (Flaviviridae family). This emerging viral disease is a major threat to humans. Currently, vaccination is the only controlling method against the KFDV, and its effectiveness is very low. An effective control strategy is required to combat this emerging tropical disease using the existing resources. In this regard, in silico drug repurposing method offers an effective strategy to find suitable antiviral drugs against KFDV proteins. Drug repurposing is an effective strategy to identify new use for approved or investigational drugs that are outside the scope of their initial usage and the repurposed drugs have lower risk and higher safety compared to de novo developed drugs, because their toxicity and safety issues are profoundly investigated during the preclinical trials in human/other models. In the present work, we evaluated the effectiveness of the FDA approved and natural compounds against KFDV proteins using in silico molecular docking and molecular simulations. At present, no experimentally solved 3D structures for the KFD viral proteins are available in Protein Data Bank and hence their homology model was developed and used for the analysis. The present analysis successfully developed the reliable homology model of NS3 of KFDV, in terms of geometry and energy contour. Further, in silico molecular docking and molecular dynamics simulations successfully presented four FDA approved drugs and one natural compound against the NS3 homology model of KFDV. Communicated by Ramaswamy H. Sarma.

Complementarity principle in terms of electron density for the study of EGFR complexes.

The complementarity principle is a well-established concept in the field of chemistry and biology. This concept is widely studied as the lock-and-key relationship between two structures, such as enzyme and ligand interactions. These interactions are based on the overlap of electron clouds between two structures. In this study, a mathematical relation determining complementarity of intermolecular contacts in terms of overlaps of electron clouds was examined using a quantum orbital-free AlteQ method developed in-house for 64 EGFR-ligand complexes with experimentally measured binding affinity data. A very high correlation was found between the overlap of ligand and enzyme electron clouds and the calculated terms, providing a good basis for prognosis of bioactivity and for molecular docking studies.

Can natural products stop the SARS-CoV-2 virus? A docking and molecular dynamics study of a natural product database.

Background: The SARS-CoV-2 3CLpro is one of the primary targets for designing new and repurposing known drugs. Methodology: A virtual screening of molecules from the Natural Product Atlas was performed, followed by molecular dynamics simulations of the most potent inhibitor bound to two conformations of the protease and into two binding sites. Conclusion: Eight molecules with appropriate ADMET properties are suggested as potential inhibitors. The greatest benefit of this study is the demonstration that these ligands can bind in the catalytic site but also to the groove between domains II and III, where they interact with a series of residues which have an important role in the dimerization and the maturation process of the enzyme.

The Influence of Hydrogen Atoms on the Performance of Radial Distribution Function-Based Descriptors in the Chemoinformatic Studies of HIV-1 Protease Complexes with Inhibitors.

AIMS: The aim of this letter is to explore the influence of adding hydrogen atoms to the crystallographic structures of HIV-1 protease complexes with a series of inhibitors on the performance of radial distribution function based descriptors recently introduced in chemoinformatic studies. BACKGROUND: Quite recently the successful application of molecular descriptors based on a radial distribution function to correlate it with biologically interesting properties of a ligand - enzyme complex was demonstrated. Except its predictive power, the analysis of atoms with dominant contributions to the RDFs can be used to identify relevant atoms and interactions. Since original paper was published on dataset consisting of the X-ray structures of complexes without hydrogen atoms, we wonder weather addition of light atoms can provide us new piece of information. OBJECTIVE: The primarily objective is to create the model correlating the RDF based descriptors and physicochemical properties of the HIV-1 protease complexes with inhibitors with hydrogen atoms. Then, we will compare the performance of new model with previous one, where the hydrogen atoms were discarded. Information about interactions between the enzyme and the inhibitors will be extracted from the analysis of the RDF. METHODS: The radial distribution function descriptor weighted by the number of valence shell electrons has proven to be sensitive to the changes in the structure of the enzyme and enzyme-ligand complexes. For each structure in our data set, RDF will be calculated and using multiple linear regression method the mathematical model will be designed correlating RDF based descriptors and the physicochemical properties. Statistical analysis of the atom's contribution to the total RDF will reveal relevant interactions. RESULTS: The applicability of RDF based descriptor for the correlation with pKi and EC50 values is demonstrated, while simple models containing only two or three parameters are able to explain 78 and 86 % of the variance, respectively. The models with explicitly included hydrogens are of comparable quality with the previous models without hydrogens. The analysis of the atom's dominant contributions highlighted the importance of the hydroxyl groups of the inhibitor near the Asp25 and Asp25' residues when it is bounded to the protease. CONCLUSION: Models based on the RDF weighted by the number of valence shell electrons for correlating small number of molecular descriptors and physicocehmical properties for structures with and without hydrogens are of comparable quality and both can be used for identification of relevant functional groups and interactions. Other: Our approach can be integrated to the next generation virtual screening methods, because is fast, reliable with high predictability potential.

Computational insights into the binding mode of curcumin analogues against EP300 HAT domain as potent acetyltransferase inhibitors.

Acetylation plays a key role in maintaining and balancing cellular regulation and homeostasis. Acetyltransferases are an important class of enzymes which mediate this acetylation process. EP300 is a type 3 major lysine (K) acetyl transferase, and its aberrant activity is implicated in many human diseases. Hence, targeting EP300 mediated acetylation is a necessary step to control the associated diseases. Currently, a few EP300 inhibitors are known, among which curcumin is the most widely investigated molecule. However, due to its instability, chemical aggregation and reactivity, its inhibitory activity against the EP300 acetyltransferase domain is disputable. To address this curcumin problem, different curcumin analogues have been synthesized. These molecules were selected for screening against the EP300 acetyltransferase domain using in silico docking and MD analysis. We have successfully elucidated that the curcumin analogue CNB001 is a potential EP300 inhibitor with good drug-like characteristics.

Electron density analysis of CDK complexes using the AlteQ method.

Background: A principle of complementarity is a well-established concept in chemistry and biology. This concept is based on the overlap of electron clouds of the molecules in question. Materials & methods: In this article, one such approach (an in-house developed quantum free-orbital AlteQ method) was used to evaluate the complementarity of 51 CDK-ligand complexes. Results: A significant universally applicable correlation (adjusted R2 = 0.9749; p < 2.2 × 10-16) relating the product of ligand and enzyme electron densities to the product of distances between the contacting atomic centers and the type of atoms involved in the interaction was found. Conclusion: The terms calculated in this article can provide a good basis for prognosis of bioactivity and scientifically based molecular docking.

Novel radial distribution function approach in the study of point mutations: the HIV-1 protease case study.

Background: Mutations are one of the engines of evolution. Under constant stress pressure, mutations can lead to the emergence of unwanted, drug-resistant entities. Methodology: The radial distribution function weighted by the number of valence shell electrons is used to design quantitative structure-activity relationship (QSAR) model relating descriptors with the inhibition constant for a series of wild-type HIV-1 protease inhibitor complexes. The residuals of complexes with mutant HIV-1 protease were correlated with the energy of the highest occupied molecular orbitals of the residues introduced to enzyme via point mutations. Conclusion: Successful identification of residues Ile3, Asp25, Val32 and Ile50 as the one whose substitution influences the inhibition constant the most, demonstrates the potential of the proposed methodology for the study of the effects of point mutations.

Performance of radial distribution function-based descriptors in the chemoinformatic studies of HIV-1 protease.

Aim: This letter investigates the role of radial distribution function-based descriptors for in silico design of new drugs. Methodology: The multiple linear regression models for HIV-1 protease and its complexes with a series of inhibitors were constructed. A detailed analysis of major atomic contributions to the radial distribution function descriptor weighted by the number of valence shell electrons identified residues Arg8, Asp29 and residues of the catalytic triad as crucial for the correlation with the inhibition constant, together with residues Asp30 and Ile50, whose mutations are known to cause an emergence of drug resistant variants. Conclusion: This study demonstrates an easy and fast assessment of the activity of potential drugs and the derivation of structural information of their complexes with the receptor or enzyme.

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.

Topological Analysis of Electron Density in Large Biomolecular Systems.

BACKGROUND: A great step toward describing the structure of the molecular electron was made in the era of quantum chemical methods. Methods play a very important role in the prediction of molecular properties and in the description of the reactivity of compounds, which cannot be overestimated. There are many works, books, and articles on quantum methods, their applications, and comparisons. At the same time, quantum methods of a high level of theory, which give the most accurate results, are time-consuming, which makes them almost impossible to describe large complex molecular systems, such as macromolecules, enzymes, supramolecular compounds, crystal fragments, and so on. OBJECTIVES: To propose an approach that allows real-time estimation of electron density in large systems, such as macromolecules, nanosystems, proteins. METHODS: AlteQ approach was applied to the tolopogical analysis of electron density for "substrate - cytochrome" complexes. The approach is based on the use of Slater's type atomic contributions. Parameters of the atomic contributions were found using high resolution X-ray diffraction data for organic and inorganic molecules. Relationships of the parameters with atomic number, ionization potentials and electronegativities were determined. The sufficient quality of the molecular electron structure representation was shown under comparison of AlteQ predicted and observed electron densities. AlteQ algorithm was applied for evaluation of electron structure of "CYP3A4 - substrate" complexes modeled using BiS/MC restricted docking procedure. Topological analysis (similar to Atoms In Molecules (AIM) theory suggested by Richard F.W. Bader) of the AlteQ molecular electron density was carried out for each complex. The determination of (3,-1) bond, (3,+1) ring, (3,+3) cage critical points of electron density in the intermolecular "CYP3A4 - substrate" space was performed. RESULTS: Different characteristics such as electron density, Laplacian eigen values, etc. at the critical points were computed. Relationship of pKM (KM is Michaelis constant) with the maximal value of the second Laplacian eigen value of electron density at the critical points and energy of complex formation computed using MM3 force field was determined. CONCLUSION: It was shown that significant number of (3,-1) bond critical points are located in the intermolecular space between the enzyme site and groups of substrate atoms eliminating during metabolism processes.

Grid-based Continual Analysis of Molecular Interior for Drug Discovery, QSAR and QSPR.

BACKGROUND: In 1979, R.D.Cramer and M.Milne made a first realization of 3D comparison of molecules by aligning them in space and by mapping their molecular fields to a 3D grid. Further, this approach was developed as the DYLOMMS (Dynamic Lattice- Oriented Molecular Modelling System) approach. In 1984, H.Wold and S.Wold proposed the use of partial least squares (PLS) analysis, instead of principal component analysis, to correlate the field values with biological activities. Then, in 1988, the method which was called CoMFA (Comparative Molecular Field Analysis) was introduced and the appropriate software became commercially available. Since 1988, a lot of 3D QSAR methods, algorithms and their modifications are introduced for solving of virtual drug discovery problems (e.g., CoMSIA, CoMMA, HINT, HASL, GOLPE, GRID, PARM, Raptor, BiS, CiS, ConGO,). All the methods can be divided into two groups (classes):1. Methods studying the exterior of molecules; 2) Methods studying the interior of molecules. METHODS: A series of grid-based computational technologies for Continual Molecular Interior analysis (CoMIn) are invented in the current paper. The grid-based analysis is fulfilled by means of a lattice construction analogously to many other grid-based methods. The further continual elucidation of molecular structure is performed in various ways. (i) In terms of intermolecular interactions potentials. This can be represented as a superposition of Coulomb, Van der Waals interactions and hydrogen bonds. All the potentials are well known continual functions and their values can be determined in all lattice points for a molecule. (ii) In the terms of quantum functions such as electron density distribution, Laplacian and Hamiltonian of electron density distribution, potential energy distribution, the highest occupied and the lowest unoccupied molecular orbitals distribution and their superposition. To reduce time of calculations using quantum methods based on the first principles, an original quantum free-orbital approach AlteQ is proposed. All the functions can be calculated using a quantum approach at a sufficient level of theory and their values can be determined in all lattice points for a molecule. Then, the molecules of a dataset can be superimposed in the lattice for the maximal coincidence (or minimal deviations) of the potentials (i) or the quantum functions (ii). RESULTS: The methods and criteria of the superimposition are discussed. After that a functional relationship between biological activity or property and characteristics of potentials (i) or functions (ii) is created. The methods of the quantitative relationship construction are discussed. New approaches for rational virtual drug design based on the intermolecular potentials and quantum functions are invented. All the invented methods are realized at www.chemosophia.com web page. CONCLUSION: Therefore, a set of 3D QSAR approaches for continual molecular interior study giving a lot of opportunities for virtual drug discovery, virtual screening and ligand-based drug design are invented. The continual elucidation of molecular structure is performed in the terms of intermolecular interactions potentials and in the terms of quantum functions such as electron density distribution, Laplacian and Hamiltonian of electron density distribution, potential energy distribution, the highest occupied and the lowest unoccupied molecular orbitals distribution and their superposition. To reduce time of calculations using quantum methods based on the first principles, an original quantum free-orbital approach AlteQ is proposed. The methods of the quantitative relationship construction are discussed. New approaches for rational virtual drug design based on the intermolecular potentials and quantum functions are invented. All the invented methods are realized at www.chemosophia.com web page.

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.

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.

Compressibility of gas hydrates.

Experimental data on the pressure dependence of unit cell parameters for the gas hydrates of ethane (cubic structure I, pressure range 0-2 GPa), xenon (cubic structure I, pressure range 0-1.5 GPa) and the double hydrate of tetrahydrofuran+xenon (cubic structure II, pressure range 0-3 GPa) are presented. Approximation of the data using the cubic Birch-Murnaghan equation, P=1.5B(0)[(V(0)/V)(7/3)-(V(0)/V)(5/3)], gave the following results: for ethane hydrate V(0)=1781 Å(3) , B(0)=11.2 GPa; for xenon hydrate V(0)=1726 Å(3) , B(0)=9.3 GPa; for the double hydrate of tetrahydrofuran+xenon V(0)=5323 Å(3) , B(0)=8.8 GPa. In the last case, the approximation was performed within the pressure range 0-1.5 GPa; it is impossible to describe the results within a broader pressure range using the cubic Birch-Murnaghan equation. At the maximum pressure of the existence of the double hydrate of tetrahydrofuran+xenon (3.1 GPa), the unit cell volume was 86% of the unit cell volume at zero pressure. Analysis of the experimental data obtained by us and data available from the literature showed that 1) the bulk modulus of gas hydrates with classical polyhedral structures, in most cases, are close to each other and 2) the bulk modulus is mainly determined by the elasticity of the hydrogen-bonded water framework. Variable filling of the cavities with guest molecules also has a substantial effect on the bulk modulus. On the basis of the obtained results, we concluded that the bulk modulus of gas hydrates with classical polyhedral structures and existing at pressures up to 1.5 GPa was equal to (9±2) GPa. In cases when data on the equations of state for the hydrates were unavailable, the indicated values may be recommended as the most probable ones.

Technique for energy decomposition in the study of "receptor-ligand" complexes.

A new methodology to describe the interactions in "receptor-ligand" complexes is presented. The methodology is based on a combination of the 3D/4D QSAR BiS/MC and CoCon algorithms. The first algorithm performs the restricted docking of compounds to receptor pockets. The second determines the relationships between the bioactivity and the parameters of interactions in the "receptor-ligand" complexes, including a new formalism for estimating hydrogen bond energies.

A new paradigm for pattern recognition of drugs.

A new paradigm is suggested for pattern recognition of drugs. The approach is based on the combined application of the 4D/3D quantitative structure-activity relationship (QSAR) algorithms BiS and ConGO. The first algorithm, BiS/MC (multiconformational), is used for the search for the conformers interacting with a receptor. The second algorithm, ConGO, has been suggested for the detailed study of the selected conformers' electron density and for the search for the electron structure fragments that determine the pharmacophore and antipharmacophore parts of the compounds. In this work we suggest using a new AlteQ method for the evaluation of the molecular electron density. AlteQ describes the experimental electron density (determined by low-temperature highly accurate X-ray analysis) much better than a number of quantum approaches. Herein this is shown using a comparison of the computed electron density with the results of highly accurate X-ray analysis. In the present study the desirability function is used for the first time for the analysis of the effects of the electron structure in the process of pattern recognition of active and inactive compounds. The suggested method for pattern recognition has been used for the investigation of various sets of compounds such as DNA-antimetabolites, fXa inhibitors, 5-HT(1A), and alpha(1)-AR receptors inhibitors. The pharmacophore and antipharmacophore fragments have been found in the electron structures of the compounds. It has been shown that the pattern recognition cross-validation quality for the datasets is unity.

Determination of covalent bond orders and atomic valence indices using topological features of the experimental electron density.

We present an approach for the determination of covalent bond orders from the experimental electron density and its derivatives at the bond critical points. An application of this method to a series of organic compounds has shown that it provides a bonding quantification that is in reasonable agreement with that obtained by orbital theory. The 'experimental' atomic valence indices are also defined and their significance for the characterization of chemical problems is discussed.