Modeling biophysical and biological properties from the characteristics of the molecular electron density, electron localization and delocalization matrices, and the electrostatic potential

Molecular properties of linear amino acids in water

Four linear amino acids of increased separation of the carboxyl and amino groups, namely glycine (aminoacetic acid), β-alanine (3-aminopropanoic acid), GABA (4-aminobutanoic acid) and DAVA (5-aminopentanoic acid), have been studied by quantum chemical ab initio and DFT methods including the solvent effect in order to get electronic structure and molecular descriptors, such as ionisation energy, electron affinity, molecular electronegativity, chemical hardness, electrophilicity index, dipole moment, quadrupole moment and dipole polarizability. Thermodynamic functions (zero-point energy, inner energy, enthalpy, entropy, and the Gibbs energy) were evaluated after the complete vibrational analysis at the true energy minimum provided by the full geometry optimization. Reaction Gibbs energy allows evaluating the absolute redox potentials on reduction and/or oxidation. The non-local non-additive molecular descriptors were compared along the series showing which of them behave as extensive, varying in match with the molar mass and/or separation of the carboxyl and amino groups. Amino acidic forms and zwitterionic forms of the substances were studied in parallel in order to compare their relative stability and redox properties. In total, 24 species were investigated by B3LYP/def2-TZVPD method (M1) including neutral molecules, molecular cations and molecular anions. For comparison, MP2/def2-TZVPD method (M2) with full geometry optimization and vibrational analysis in water has been applied for 12 species; analogously, for 24 substances, DLPNO-CCSD(T)/aug-cc-pVTZ method (M3) has been applied in the geometry obtained by MP2 and/or B3LYP. It was found that the absolute oxidation potential correlates with the adiabatic ionisation energy; the absolute reduction potential correlates with the adiabatic electron affinity and the electrophilicity index. In order to validate the used methodology with experimental vertical ionisation energies and vibrational spectrum obtained in gas phase, calculations were done also in vacuo.

Exploring protein-ligand binding affinity prediction with electron density-based geometric deep learning

Rational structure-based drug design relies on accurate predictions of protein-ligand binding affinity from structural molecular information. Although deep learning-based methods for predicting binding affinity have shown promise in computational drug design, certain approaches have faced criticism for their potential to inadequately capture the fundamental physical interactions between ligands and their macromolecular targets or for being susceptible to dataset biases. Herein, we propose to include bond-critical points based on the electron density of a protein-ligand complex as a fundamental physical representation of protein-ligand interactions. Employing a geometric deep learning model, we explore the usefulness of these bond-critical points to predict absolute binding affinities of protein-ligand complexes, benchmark model performance against existing methods, and provide a critical analysis of this new approach. The models achieved root-mean-squared errors of 1.4-1.8 log units on the PDBbind dataset, and 1.0-1.7 log units on the PDE10A dataset, not indicating significant advantages over benchmark methods, and thus rendering the utility of electron density for deep learning models context-dependent. The relationship between intermolecular electron density and corresponding binding affinity was analyzed, and Pearson correlation coefficients r > 0.7 were obtained for several macromolecular targets.

An Isolable Radical Anion Featuring a 2-Center-3-Electron π-Bond without a Clearly Defined σ-Bond

Featuring an extra electron in the π* antibonding orbital, species with a 2-center-3-electron (2c3e) π bond without an underlying σ bond are scarcely known. Herein, we report the synthesis, isolation and characterization of a radical anion salt [K(18-C-6)]+ {[(HCNDipp)2 Si]2 P2 }⋅- (i.e. [K(18-C-6)]+ 3⋅- ) (18-C-6=18-crown-6, Dipp=2,6-diisopropylphenyl), in which 3⋅- features a perfectly planar Si2 P2 four-membered ring. This species represents the first example of a Si- and P-containing analog of a bicyclo[1.1.0]butane radical anion. The unusual bonding motif of 3⋅- was thoroughly investigated via X-ray diffraction crystallography, electron paramagnetic resonance spectroscopy (EPR), and calculations by density functional theory (DFT), which collectively unveiled the existence of a 2c3e π bond between the bridgehead P atoms and no clearly defined supporting P-P σ bond.

Utilizing bis(imino)dihydroacridanide pincer ligands in p-block chemistry: synthesis and catalysis of an antimony monocation salt

We present the synthesis and characterization of an Sb(III) monocation salt stabilized by a bulky bis(imino)dihydroacridanide pincer ligand. The Lewis acidity of the Sb cation is quantified using the Guttmann-Beckett method and confirmed by its reaction with 4-dimethylaminopyridine, which forms a Lewis acid-base adduct. This Sb cation exhibits catalytic activity in the cyanosilylation of arylketones. The electronic structure of the Sb cation as well as the mechanism of the catalytic transformation are explored by density functional theory computations.

Toward Universal Substituent Constants: Relating QTAIM Functional Group Descriptors to Substituent Effect Proxies

Substituents modulate reactions, but their effects are commonly described by using proxies to their functional group properties. Substituent descriptors from the quantum theory of atoms in molecules, which are true functional group properties, are related here to these proxies, which have historically had chemically relevant meaning. Due to the large number of descriptors, multivariate analysis is used to intuit their significance. Multiple linear regression, principal component, and partial least squares regression analyses highlight that these substituent descriptors contain similar information to the proxies while being intrinsic, predictable substituent properties. Sources of error limiting quantitative reproduction of the proxy data include transferability, experimental accuracy, and solvation issues.

Implementation of machine learning protocols to predict the hydrolysis reaction properties of organophosphorus substrates using descriptors of electron density topology

Prediction of catalytic reaction efficiency is one of the most intriguing and challenging applications of machine learning (ML) algorithms in chemistry. In this study, we demonstrated a strategy for utilizing ML protocols applied to Quantum Theory of Atoms In Molecules (QTAIM) parameters to predict the ability of the A17 L47K catalytic antibody to covalently capture organophosphate pesticides. We found that the novel "composite" DFT functional B97-3c could be effectively employed for fast and accurate initial geometry optimization, aligning well with the input dataset creation. QTAIM descriptors proved to be well-established in describing the examined dataset using density-based and hierarchical clustering algorithms. The obtained clusters exhibited correlations with the chemical classes of the input compounds. The precise physical interpretation of the QTAIM properties simplifies the explanation of feature impact for both supervised and unsupervised ML protocols. It also enables acceleration in the search for entries with desired properties within large databases. Furthermore, our findings indicated that Ridge Regression with Laplacian kernel and CatBoost Regressor algorithms demonstrated suitable performance in handling small datasets with non-trivial dependencies. They were able to predict the actual reaction barrier values with a high level of accuracy. Additionally, the CatBoost Classifier proved reliable in discriminating between "active" and "inactive" compounds.

Making the Case for Quantum Mechanics in Predictive Toxicology─Nearly 100 Years Too Late?

The use of quantum mechanics (QM) has long been the norm to study covalent-binding phenomena in chemistry and biochemistry. The pharmaceutical industry leverages QM models explicitly in covalent drug discovery and implicitly to characterize short-range interactions in noncovalent binding. Predictive toxicology has resisted widespread adoption of QM, including in the pharmaceutical industry, despite its obvious relevance to the metabolic processes in the upstream of adverse outcome pathways and advances in both QM methods and computational resources, which support fit-for-purpose applications in reasonable timeframes. Here, we make the case for embracing QM as an indispensable part of a toxicologist's toolkit. We argue that QM provides the necessary orthogonality to alert-based expert systems and traditional QSARs, consistent with calls for animal-free integrated testing strategies for safety assessments of commercial chemicals. We outline existing roadblocks to this transition, including the need to train model developers in QM and the shift toward service-based toxicity models that utilize high-performance computing clusters. Lastly, we describe recent examples of successful implementations of QM in hazard assessments and propose how in silico toxicology can be further advanced by integrating QM with artificial intelligence.

Assessment of antidiabetic activity of three Phenylspirodrimanes from fungus Stachybotrys chartarum MUT 3308 by ADME, QSAR, molecular docking and molecular dynamics simulation studies against protein tyrosine phosphatase 1B (PTP1B)

Phenylspirodrimanes (PSD) are the sesquiterpene quinone type meroterpenoids found in nature. PSDs are found to exhibit inhibitory activity against immunocomplex diseases, and tyrosine kinase receptors. Three of the different PSDs C1, C2, and C3 that have been reported to be isolated from the sponge-associated fungus Stachybotrys chartarum MUT 3308 are selected for studying their possible inhibitory effect against type 2 diabetes mellitus. Mechanistically, blocking protein tyrosine phosphatase 1B (PTP1B) helps to reduce the insulin resistance induction caused by the high expression of PTP1B. The QSAR, ADME, toxicity (T) study was carried out to predict the pharmacokinetic properties and the biological activities of the PSDs. PASS prediction web tool was used to find and select the target proteins 1NNY, and 2HNP. According to the molecular docking simulations, C1 and C2 showed better binding affinity of -8.5 kcal/mol, and -8.1 kcal/mol respectively against 1NNY compared to the control ligand. RMSD, RMSF, Rg, and SASA analysis revealed that both C1, and C2 showed better stability, minor conformational changes, and minor fluctuation upon binding to PTP1B. Protein contact analysis was carried out to validate the residues that are in contact with the ligands according to molecular docking studies. Overall, C1, and C2 could be proposed as novel natural hits to be developed and small modifications of these PSDs could result in inducing the binding affinity significantly, although experimental validation is required for further evaluation of the work.Communicated by Ramaswamy H. Sarma.

Effect of a Substituent on the Properties of Salicylaldehyde Hydrazone Derivatives

The present study investigates the effect of the substitution of salicylaldehyde hydrazones at two selected positions, i.e., the para-position with regard to the proton-donating and proton-accepting centers forming the hydrogen bridge. A detailed analysis of structural data obtained by theoretical approaches and X-ray experiments, together with original resonance Hammett's constants, indicates that the strength of the intramolecular hydrogen bonding present in salicylaldehyde hydrazones can be selectively modulated by substitution of the parent molecular system with the chemical group of known π-electron-donating or -accepting properties. Our findings provide an insight into planning synthesis pathways for salicylaldehyde hydrazone species and predicting their result with regard to their H-bonding and related physical and chemical properties.

A Combined Study on Structures and Vibrational Spectra of the Antiviral Rimantadine using SQMFF and DFT calculations

In this research, a combined study on structures and vibrational spectra of antiviral rimantadine have been performed using hybrid B3LYP/6–311++G∗∗ calculations and the scaled quantum force field (SQMFF) procedure. Harmonic force fields and scaled force constants of Free Base (FB), Cationic (CA) and Hydrochloride (HCl) species derived from the antiviral rimantadine have been calculated in gas phase and in aqueous solution using normal internal coordinates and scaling factors. Good correlations were acquired comparing the theoretical IR, Raman, ¹H– ¹³C-NMR and UV spectra of three species with the analogous experimental ones, suggesting probably, the presence of all them in both phases. The main force constants of three species have evidenced lower values than the corresponding to antiviral amantadine. The ionic character of N1–H33⋯Cl36 bond of HCl species in aqueous solution evidence positive Mulliken charge on N1 atom indicating that this species is as CA one. Rimantadine presents higher solvation energies in water than other antiviral species, such as chloroquin, niclosamide, cidofovir and brincidofovir. The FB and HCl species of rimantadine are slightly less reactive than the corresponding to amantadine while the opposite is observed for the CA species. The predicted ECD spectra for the FB and CA species show positive Cotton effect different from the negative observed for the HCl one. These different behaviours of three species of rimantadine could probably explain the differences observed in the intensities of bands predicted in the electronic spectra of these species.

A fast approximate extension of the interacting quantum atoms energy decomposition to excited states

An approach is developed for the fast calculation of the interacting quantum atoms energy decomposition (IQA) from the information contained in the first order reduced density matrix only. The proposed methodology utilizes an approximate exchange-correlation density from Density Matrix Functional Theory without the need to evaluate the correlation-exchange contribution directly. Instead, weight factors are estimated to decompose the exact V_xc into atomic and pairwise contributions. In this way, the sum of the IQA contributions recovers the energy obtained from the electronic structure calculation. This method can, hence, be applied to obtain atomic contributions in excited states on the same footing as in their ground states using any method that delivers the reduced first-order density matrix. In this way, one can locate chromophores from first principles quantum chemical calculations. Test calculations on the ground and excited states of a set of small molecules indicate that the scaled atomic contributions reproduce vertical electronic transition energies calculated exactly. This approach may be useful to extend the applicability of the IQA approach in the study of large photochemical systems especially when the calculations of the second order reduced density matrices is prohibitive or not possible.

Toward universal substituent constants: Transferability of group descriptors from the quantum theory of atoms in molecules

Traditionally, substituents are described not by their intrinsic properties, but by their effect elsewhere in a molecule. However, the quantum theory of atoms in molecules (QTAIM) provides a route to intrinsic substituent descriptors. Ideally, these descriptors would exhibit minimal change as the local environment changes, and hence be considered transferable. Whether this is true is an open question. Here, we evaluated the transferability of QTAIM functional group descriptors for 117 functional groups in a series of 17 substrates to determine whether descriptors obtained using hydrogen as substrate are transferable. The functional group volume has a strong, consistent, linear relationship throughout. All other hydrogen-based group descriptors exhibit a relatively strong linear relationship with those in carbon-based substrates and a reasonable linear relationship with those in non-carbon-based substrates. Outliers are readily interpretable in terms of substrate induced functional group geometry changes. As expected, directional descriptors lying along the substituent-substrate axis are the least conserved.

NNAIMQ: A neural network model for predicting QTAIM charges

Atomic charges provide crucial information about the electronic structure of a molecular system. Among the different definitions of these descriptors, the one proposed by the Quantum Theory of Atoms in Molecules (QTAIM) is particularly attractive given its invariance against orbital transformations although the computational cost associated with their calculation limits its applicability. Given that Machine Learning (ML) techniques have been shown to accelerate orders of magnitude the computation of a number of quantum mechanical observables, in this work, we take advantage of ML knowledge to develop an intuitive and fast neural network model (NNAIMQ) for the computation of QTAIM charges for C, H, O, and N atoms with high accuracy. Our model has been trained and tested using data from quantum chemical calculations in more than 45 000 molecular environments of the near-equilibrium CHON chemical space. The reliability and performance of NNAIMQ have been analyzed in a variety of scenarios, from equilibrium geometries to molecular dynamics simulations. Altogether, NNAIMQ yields remarkably small prediction errors, well below the 0.03 electron limit in the general case, while accelerating the calculation of QTAIM charges by several orders of magnitude.

New insights of QTAIM and stress tensor to finding non-competitive/competitive torquoselectivity of cyclobutene

This study aims to investigate the phenomenon of torquoselectivity through three thermal cyclobutene ring-opening reactions (N1-N3). This research focuses on the nature of the chemical bond, electronic reorganization, predicting non-competitive or competitive reactions, and torquoselectivity preference within Quantum Theory of Atoms in Molecules (QTAIM) and stress tensor frameworks. Various theoretical analyses for these reactions, such as metallicity ξ(r_b), ellipticity ε, total local energy density H(r_b), stress tensor polarizability ℙ_σ, stress tensor eigenvalue λ_3σ, and bond-path length, display differently for non-competitive and competitive reactions as well as for the conrotatory preferences either it is the transition state outward conrotatory (TS_out) or transition state inward conrotatory (TS_in) directions by presenting degeneracy or non-degeneracy in their results. The ellipticity profile provides the motion of the bond critical point locations due to the different substituents of cyclobutene. In agreement with experimental results, examinations demonstrated that N1 is a competitive reaction and N2-N3 are non-competitive reactions with TS_out and TS_in preference directions, respectively. The concordant results of QTAIM and stress tensor scalar and vectors with experimental results provide a better understanding of reaction mechanisms.

Analysis of the uncharted, druglike property space by self-organizing maps

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.

Machine Learning to Predict Diels-Alder Reaction Barriers from the Reactant State Electron Density

Reaction barriers are key to our understanding of chemical reactivity and catalysis. Certain reactions are so seminal in chemistry that countless variants, with or without catalysts, have been studied, and their barriers have been computed or measured experimentally. This wealth of data represents a perfect opportunity to leverage machine learning models, which could quickly predict barriers without explicit calculations or measurement. Here, we show that the topological descriptors of the quantum mechanical charge density in the reactant state constitute a set that is both rigorous and continuous and can be used effectively for the prediction of reaction barrier energies to a high degree of accuracy. We demonstrate this on the Diels-Alder reaction, highly important in biology and medicinal chemistry, and as such, studied extensively. This reaction exhibits a range of barriers as large as 270 kJ/mol. While we trained our single-objective supervised (labeled) regression algorithms on simpler Diels-Alder reactions in solution, they predict reaction barriers also in significantly more complicated contexts, such a Diels-Alder reaction catalyzed by an artificial enzyme and its evolved variants, in agreement with experimental changes in k_cat. We expect this tool to apply broadly to a variety of reactions in solution or in the presence of a catalyst, for screening and circumventing heavily involved computations or experiments.

Anti-Ebola: an initiative to predict Ebola virus inhibitors through machine learning

Ebola virus is a deadly pathogen responsible for a frequent series of outbreaks since 1976. Despite various efforts from researchers worldwide, its mortality and fatality are quite high. For antiviral drug discovery, the computational efforts are considered highly useful. Therefore, we have developed an 'anti-Ebola' web server, through quantitative structure-activity relationship information of available molecules with experimental anti-Ebola activities. Three hundred and five unique anti-Ebola compounds with their respective IC₅₀ values were extracted from the 'DrugRepV' database. Later, the compounds were used to extract the molecular descriptors, which were subjected to regression-based model development. The robust machine learning techniques, namely support vector machine, random forest and artificial neural network, were employed using tenfold cross-validation. After a randomization approach, the best predictive model showed Pearson's correlation coefficient ranges from 0.83 to 0.98 on training/testing (T²⁷⁴) dataset. The robustness of the developed models was cross-evaluated using William's plot. The highly robust computational models are integrated into the web server. The 'anti-Ebola' web server is freely available at https://bioinfo.imtech.res.in/manojk/antiebola . We anticipate this will serve the scientific community for developing effective inhibitors against the Ebola virus.

Molecular Structure, Spectral Investigations, Hydrogen Bonding Interactions and Reactivity-Property Relationship of Caffeine-Citric Acid Cocrystal by Experimental and DFT Approach

The pharmaceutical cocrystal of caffeine-citric acid (CAF-CA, Form II) has been studied to explore the presence of hydrogen bonding interactions and structure-reactivity-property relationship between the two constituents CAF and Citric acid. The cocrystal was prepared by slurry crystallization. Powder X-ray diffraction (PXRD) analysis was done to characterize CAF-CA cocrystal. Also, differential scanning calorimetry (DSC) confirmed the existence of CAF-CA cocrystal. The vibrational spectroscopic (FT-IR and FT-Raman) signatures and quantum chemical approach have been used as a strategy to get insights into structural and spectral features of CAF-CA cocrystal. There was a good correlation among the experimental and theoretical results of dimer of cocrystal, as this model is capable of covering all nearest possible interactions present in the crystal structure of cocrystal. The spectroscopic results confirmed that (O33-H34) mode forms an intramolecular (C25 = O28∙∙∙H34-O33), while (O26-H27) (O39-H40) and (O43-H44) groups form intermolecular hydrogen bonding (O26-H27∙∙∙N24-C22, O39-H40∙∙∙O52 = C51 and O43-H44∙∙∙O86 = C83) in cocrystal due to red shifting and increment in bond length. The quantum theory of atoms in molecules (QTAIM) analysis revealed (O88-H89∙∙∙O41) as strongest intermolecular hydrogen bonding interaction with interaction energy -12.4247 kcal mol-1 in CAF-CA cocrystal. The natural bond orbital analysis of the second-order theory of the Fock matrix highlighted the presence of strong interactions (N∙∙∙H and O∙∙∙H) in cocrystal. The HOMO-LUMO energy gap value shows that the CAF-CA cocrystal is more reactive, less stable and softer than CAF active pharmaceutical ingredients. The electrophilic and nucleophilic reactivities of atomic sites involved in intermolecular hydrogen bond interactions in cocrystal have been demonstrated by mapping electron density isosurfaces over electrostatic potential i.e. plotting molecular electrostatic potential (MESP) map. The molar refractivity value of cocrystal lies within the set range by Lipinski and hence it may be used as orally active form. The results show that the physicochemical properties of CAF-CA cocrystal are enhanced in comparison to CAF (API).

Synthesis, molecular docking, and in silico ADMET studies of 4-benzyl-1-(2,4,6-trimethyl-benzyl)-piperidine: Potential Inhibitor of SARS-CoV2

Nowadays, over 200 countries face a wellbeing emergency because of epidemiological disease COVID-19 caused by the SARS-CoV-2 virus. It will cause a very high effect on the world's economy and the worldwide health sector. The present work is an investigation of the newly synthesized 4-benzyl-1-(2,4,6-trimethyl-benzyl)-piperidine (M1BZP) molecule's inhibitory potential against important protein targets of SARS-CoV-2 using computational approaches. M1BZP crystallizes in monoclinic type with P1211 space group. For the title compound M1BZP, spectroscopic characterization like ¹H NMR, ¹³C NMR, FTIR, were carried out. The geometry of the compound had been optimized by the DFT method and its results were compared with the X-ray diffraction data. The calculated energies for the Highest Occupied Molecular Orbital (HOMO) and the Lowest Unoccupied Molecular Orbital (LUMO) showed the stability and reactivity of the title compound. Intermolecular interactions in the crystal network were determined using Hirshfeld surface analyses. The molecular electrostatic potential (MEP) picture was drawn using the same level of theory to visualize the chemical reactivity and charge distribution on the molecule. Molecular docking study performed for the synthesized compound revealed an efficient interaction with the COVID-19 protease and resulted in good activities. We hope the present study would help workers in the field to develop potential vaccines and therapeutics against the novel coronavirus. Virtual ADME studies were carried out as well and a relationship between biological, electronic, and physicochemical qualifications of the target compound was determined. Toxicity prediction by computational technique for the title compound was also carried out.

Strong intramolecular hydrogen bonding in confined amino acids

Intramolecular hydrogen bonding is evaluated in three different amino acids encapsulated in C₆₀ fullerene in the context of electron density analysis. While conventional intramolecular hydrogen bonding in isolated amino acids are dominated by electrostatic character, it is shown that strong intramolecular hydrogen bonding can be formed in confined amino acids so that in two cases covalent intramolecular hydrogen bonding is appeared in the confined species. Also, results show that zwitterionic amino acids are stable in confined state, where no implicit or explicit solvation is applied. Covalent character for intramolecular hydrogen bonding in amino acids have not yet been reported.

Unraveling individual host-guest interactions in molecular recognition from first principles quantum mechanics: Insights into the nature of nicotinic acetylcholine receptor agonist binding

Drug binding to a protein target is governed by a complex pattern of noncovalent interactions between the ligand and the residues in the protein's binding pocket. Here we introduce a generally applicable, parameter-free, computational method that allows for the identification, quantification, and analysis of the key ligand-residue interactions responsible for molecular recognition. Our strategy relies on Local Energy Decomposition analysis at the "gold-standard" coupled cluster DLPNO-CCSD(T) level. In the study case shown in this paper, nicotine and imidacloprid binding to the nicotinic acetylcholine receptor, our approach provides new insights into how individual amino acids in the active site determine sensitivity and selectivity of the ligands, extending and refining classical pharmacophore hypotheses. By inference, the method is applicable to any kind of host/guest interactions with potential applications in industrial biocatalysis and protein engineering.

Similarities and Differences between Crystal and Enzyme Environmental Effects on the Electron Density of Drug Molecules

The crystal interaction density is generally assumed to be a suitable measure of the polarization of a low-molecular weight ligand inside an enzyme, but this approximation has seldomly been tested and has never been quantified before. In this study, we compare the crystal interaction density and the interaction electrostatic potential for a model compound of loxistatin acid (E64c) with those inside cathepsin B, in solution, and in vacuum. We apply QM/MM calculations and experimental quantum crystallography to show that the crystal interaction density is indeed very similar to the enzyme interaction density. Less than 0.1 e are shifted between these two environments in total. However, this difference has non-negligible consequences for derived properties.

Classification models and SAR analysis on CysLT1 receptor antagonists using machine learning algorithms

Cysteinyl leukotrienes 1 (CysLT1) receptor is a promising drug target for rhinitis or other allergic diseases. In our study, we built classification models to predict bioactivities of CysLT1 receptor antagonists. We built a dataset with 503 CysLT1 receptor antagonists which were divided into two groups: highly active molecules (IC₅₀ < 1000 nM) and weakly active molecules (IC₅₀ ≥ 1000 nM). The molecules were characterized by several descriptors including CORINA descriptors, MACCS fingerprints, Morgan fingerprint and molecular SMILES. For CORINA descriptors and two types of fingerprints, we used the random forests (RF) and deep neural networks (DNN) to build models. For molecular SMILES, we used recurrent neural networks (RNN) with the self-attention to build models. The accuracies of test sets for all models reached 85%, and the accuracy of the best model (Model 2C) was 93%. In addition, we made structure-activity relationship (SAR) analyses on CysLT1 receptor antagonists, which were based on the output from the random forest models and RNN model. It was found that highly active antagonists usually contained the common substructures such as tetrazoles, indoles and quinolines. These substructures may improve the bioactivity of the CysLT1 receptor antagonists.

Ancillary Ligand in Ternary CuII Complexes Guides Binding Selectivity toward Minor-Groove DNA

Copper-containing compounds known as Casiopeínas are biologically active molecules which show promising antineoplastic effects against several cancer types. Two possible hypotheses regarding the mode of action of the Casiopeínas have emerged from the experimental evidence: the generation of reactive oxygen species or the ability of the compounds to bind and interact with nucleic acids. Using robust molecular dynamics simulations, we investigate the interaction of four different Casiopeínas with the DNA duplex d(GCACGAACGAACGAACGC). The studied copper complexes contain either 4–7- or 5–6-substituted dimethyl phenanthroline as the primary ligand and either glycinate or acetylacetonate as the secondary ligand. For statistical significance and to reduce bias in the simulations, four molecules of each copper compound were manually placed at a distance of 10 Å away from the DNA and 20 independent molecular dynamics simulations were performed, each reaching at least 30 μs. This time scale allows us to reproduce expected DNA terminal base-pair fraying and also to observe intercalation/base-pair eversion events generated by the compounds interacting with DNA. The results reveal that the secondary ligand is the guide toward the mode of binding between the copper complex and DNA in which glycinate prefers minor-groove binding and acetylacetonate produces base-pair eversion and intercalation. The Cu^II complexes containing glycinate interact within the DNA minor groove which are stabilized principally by the hydrogen bonds formed between the amino group of the aminoacidate moiety, whereas the compounds with the acetylacetonate do not present a stable network of hydrogen bonds and the ligand interactions enhance DNA breathing dynamics that result in base-pair eversion.

A Quantum-Mechanical Looking Behind the Scene of the Classic G·C Nucleobase Pairs Tautomerization

For the first time, at the MP2/6-311++G(2df,pd)//B3LYP/6-311++G(d,p) level of theory, a comprehensive quantum-mechanical investigation of the physico-chemical mechanism of the tautomeric wobblization of the four biologically-important G·C nucleobase pairs by the participation of the monomers in rare, in particular mutagenic, tautomeric forms (marked with an asterisk) was provided. These novel tautomeric transformations (wobblization or shifting of the bases within the pair) are intrinsically inherent properties of the G·C nucleobase pairs. In this study, we have obtained intriguing results, lying far beyond the existing representations. Thus, it was shown that Löwdin's G*·C*(WC) base pair does not tautomerize according to the wobblization mechanism. Tautomeric wobblization of the G*·C*(rWC) (relative Gibbs free energy ΔG = 0.00/relative electronic energy ΔE = 0.00 kcal·mol-1) ("r"-means the configuration of the base pair in reverse position; "WC"-the classic Watson-Crick configuration) and G*t·C*(H) (ΔG = -0.19/ΔE = 0.29 kcal·mol-1) ("H"-Hoogsteen configuration;"t" denotes the O6H hydroxyl group in the trans position) base pairs are preceded by the stages of the base pairs tautomerization by the single proton transfer (SPT). It was established that the G*t·C*(rH) (ΔG = 2.21/ΔE = 2.81 kcal·mol-1) base pair can be wobbled through two different pathways via the traditional one-stage mechanism through the TSs, which are tight G+·C- ion pairs, stabilized by the participation of only two intermolecular H-bonds. It was found out that the G·C base pair is most likely incorporated into the DNA/RNA double helix with parallel strands in the G*·C*(rWC), G·C*(rwwc), and G*·C(rwwc) ("w"-wobble configuration of the pair) tautomeric forms, which are in rapid tautomeric equilibrium with each other. It was proven that the G*·C*(rWC) nucleobase pair is also in rapid tautomeric equilibrium with the eight tautomeric forms of the so-called Levitt base pair. It was revealed that a few cases of tautomerization via the DPT of the nucleobase pairs by the participation of the C8H group of the guanine had occurred. The biological role of the obtained results was also made apparent.

Bonding and metastability for Group 12 dications

Electronic structure and bonding properties of the Group 12 dications M₂ ²⁺ (M = Zn, Cd, Hg) are investigated and electron density-derived quantities are used to characterize the metastability of these species. Of particular interest are the complementary descriptions afforded by the Laplacian of the electron density ∇² ρ(r) and the one-electron Bohm quantum potential (Q =  ∇ 2 ρ r / 2 ρ r ) along the bond path. Further, properties derived from the pair density including the localization-delocalization matrices (LDMs) and the interacting quantum atoms (IQA) energies are analyzed within the framework of the quantum theory of atoms in molecules (QTAIM). From the crossing points of the singlet (ground) and triplet (excited) potential energy curves, the barriers for dissociation (BFD) are estimated to be 25.2 kcal/mol (1.09 eV) for Zn₂ ²⁺ , 22.8 kcal/mol (0.99 eV) for Cd₂ ²⁺ , and 26.4 kcal/mol (1.14 eV) for Hg₂ ²⁺ . For comparison and benchmarking purposes, the case of N₂ ²⁺ is considered as a texbook example of metastability. At the equilibrium geometries, LDMs, which are used here as an electronic fingerprinting tool, discriminate and group together Group 12 M₂ ²⁺ from its isoelectronic Group 11 M₂ . While "classical" bonding indices are inconclusive in establishing regions of metastability in the bonding, it is shown that the one-electron Bohm quantum potential is promising in this regard.

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.

Manipulation of Diatomic Molecules with Oriented External Electric Fields: Linear Correlations in Atomic Properties Lead to Nonlinear Molecular Responses

Oriented external electric fields (OEEFs) have been shown to have great potential in being able to provide unprecedented control of chemical reactions, catalysis, and selectivity with applications ranging from H₂ storage to molecular machines. We report a theoretical study of the atomic origins of molecular changes because of OEEFs since understanding the characteristics of OEEF-induced couplings between atomic and molecular properties is an important step toward comprehensive understanding of the effects of strong external fields on the molecular structure, stability, and reactivity. We focus on the atomic and molecular (bond) properties of a set of homo- (H₂, N₂, O₂, F₂, and Cl₂) and heterodiatomic (HF, HCl, CO, and NO) molecules under intense external electric fields in the context of quantum theory of atoms in molecules (QTAIM). It is shown that the atomic properties (atomic charges, energies, and localization indices) correlate linearly with the field strengths, but molecular properties (bond length, electron density at the bond critical point, and electron delocalization index) exhibit nonlinear responses to the imposed fields. In particular, the changes in the electron density distribution alter the shapes and locations of the zero-flux surfaces, atomic volumes, atomic electron population, and localization/delocalization indices. The topography and topology of the molecular electrostatic potential undergo dramatic changes. External fields also perturb the covalent-polar-ionic characteristic of the studied chemical bonds, hallmarking the impact of electric fields on the stability and reactivity of chemical compounds. The findings are well-rationalized within the framework of the QTAIM and form a coherent conceptual understanding of these effects in prototypical diatomic molecules.

Drug design by machine-trained elastic networks: predicting Ser/Thr-protein kinase inhibitors' activities

An elastic network model (ENM) represents a molecule as a matrix of pairwise atomic interactions. Rich in coded information, ENMs are hereby proposed as a novel tool for the prediction of the activity of series of molecules, with widely different chemical structures, but a common biological activity. The new approach is developed and tested using a set of 183 inhibitors of serine/threonine-protein kinase enzyme (Plk3) which is an enzyme implicated in the regulation of cell cycle and tumorigenesis. The elastic network (EN) predictive model is found to exhibit high accuracy and speed compared to descriptor-based machine-trained modeling. EN modeling appears to be a highly promising new tool for the high demands of industrial applications such as drug and material design.

Bioconformational modulation of a thymidine kinase enzyme ligand through F⋯HO intramolecular hydrogen bond

While the induced-fit of a ligand towards an enzyme is pivotally dictated by intermolecular hydrogen bonds between the small molecule and amino acid residues in the binding site, the role of intramolecular hydrogen bond as contributing interaction for a bioactive conformation is not well understood. This work reports a theoretical conformational analysis of a thymidine kinase enzyme ligand (NMF) that is prone to experience an F⋯HO intramolecular hydrogen bond, inside and outside the biological binding site. This interaction stabilizes the most favorable conformations of NMF in the gas phase and, although it can be disrupted in a biological environment due to intermolecular hydrogen bonds in some cases, these interactions are competitive in other systems. Therefore, an intramolecular hydrogen bond can affect the conformational likeliness most related to the bioactivity. Moreover, isolated conformations governed by this interaction cannot be unequivocally used to generate molecular descriptors in 3D-QSAR (Quantitative Structure-Activity Relationships), since the bioactive conformation may not be determined only by intramolecular interactions.

Predicting reactive sites with quantum chemical topology: carbonyl additions in multicomponent reactions

The reactivity of an atom within a molecule depends mostly on the way the electron density polarizes reflected in the quadrupole moment of the reactive atom.

Intramolecular tautomerization of the quercetin molecule due to the proton transfer: QM computational study

Quercetin molecule (3, 3', 4', 5, 7-pentahydroxyflavone, C15H10O7) is an important flavonoid compound of natural origin, consisting of two aromatic A and B rings linked through the C ring with endocyclic oxygen atom and five hydroxyl groups attached to the 3, 3', 4', 5 and 7 positions. This molecule is found in many foods and plants, and is known to have a wide range of therapeutic properties, like an anti-oxidant, anti-toxic, anti-inflammatory etc. In this study for the first time we have revealed and investigated the pathways of the tautomeric transformations for the most stable conformers of the isolated quercetin molecule (Brovarets' & Hovorun, 2019) via the intramolecular proton transfer. Energetic, structural, dynamical and polar characteristics of these transitions, in particular relative Gibbs free and electronic energies, characteristics of the intramolecular specific interactions-H-bonds and attractive van der Waals contacts, have been analysed in details. It was demonstrated that the most probable process among all investigated is the proton transfer from the O3H hydroxyl group of the C ring to the C2' carbon atom of the C2'H group of the B ring along the intramolecular O3H…C2' H-bond with the further formation of the C2'H2 group. It was established that the proton transfer from the hydroxyl groups to the carbon atoms of the neighboring CH groups is assisted at the transition states by the strong intramolecular HCH…O H-bond (~28.5 kcal∙mol-1). The least probable path of the proton transfer-from the C8H group to the endocyclic O1 oxygen atom-causes the decyclization of the C ring in some cases. It is shortly discussed the biological importance of the obtained results.

Novel Tautomerisation Mechanisms of the Biologically Important Conformers of the Reverse Löwdin, Hoogsteen, and Reverse Hoogsteen G*·C* DNA Base Pairs via Proton Transfer: A Quantum-Mechanical Survey

For the first time, in this study with the use of QM/QTAIM methods we have exhaustively investigated the tautomerization of the biologically-important conformers of the G*·C* DNA base pair-reverse Löwdin G*·C*(rWC), Hoogsteen G*'·C*(H), and reverse Hoogsteen G*'·C*(rH) DNA base pairs-via the single (SPT) or double (DPT) proton transfer along the neighboring intermolecular H-bonds. These tautomeric reactions finally lead to the formation of the novel G· C O 2 * (rWC), G N 2 * · C(rWC), G*'N2·C(rWC), G N 7 * · C(H), and G*'N7·C(rH) DNA base mispairs. Gibbs free energies of activation for these reactions are within the range 3.64-31.65 kcal·mol-1 in vacuum under normal conditions. All TSs are planar structures (Cs symmetry) with a single exception-the essentially non-planar transition state TSG*·C*(rWC)↔G+·C-(rWC) (C1 symmetry). Analysis of the kinetic parameters of the considered tautomerization reactions indicates that in reality only the reverse Hoogsteen G*'·C*(rH) base pair undergoes tautomerization. However, the population of its tautomerised state G*'N7·C(rH) amounts to an insignificant value-2.3·10-17. So, the G*·C*(rWC), G*'·C*(H), and G*'·C*(rH) base pairs possess a permanent tautomeric status, which does not depend on proton mobility along the neighboring H-bonds. The investigated tautomerization processes were analyzed in details by applying the author's unique methodology-sweeps of the main physical and chemical parameters along the intrinsic reaction coordinate (IRC). In general, the obtained data demonstrate the tautomeric mobility and diversity of the G*·C* DNA base pair.