Atomic Van Der Waals Research Articles

The peeling behavior of compliant nano-films in adhesive contact with a planar rigid substrate: Insights from molecular dynamics and continuum mechanics

Peeling compliant nano-films from supporting substrates is crucial in the mechanical exfoliation and transfer processes. However, the peeling behavior, especially concerning the peeling stiffness and peak peeling force, exhibits intricate interplay with the geometric and material properties of nano-films, as well as interfacial interactions, which have yet to be fully elucidated. In this work, both classical molecular dynamics (MD) simulations and continuum analysis are adopted to investigate the entire peeling process of compliant nano-films from a planar rigid substrate. Considering the atomic structure and van der Waals (vdW) interactions at the interface, we establish a continuum mechanics model to describe the entire peeling process, encompassing the initial, transitional, steady-state, and unstable peel-off stages. The theoretical predictions are reasonably consistent with the results obtained by MD simulations. The effects of film length and interface toughness on the peeling process, the peeling stiffness and peak peeling force, are thoroughly investigated, and a phase diagram for the peeling deformation modes is quantitatively constructed. Finally, dimensional analysis yields scaling relations for the peak peeling force in terms of the length and bending stiffness of compliant nano-films, as well as the governing parameters for interfacial vdW interactions. These results contribute to a better understanding of the peeling mechanics of various two-dimensional nano-films (e.g., graphene, hexagonal boron nitride, and molybdenum disulfide) adhered to substrates.

In-silico mechanistic analysis of adsorption of Iodinated Contrast Media agents on graphene surface

The study aims at assessing the potential of graphene-based adsorbents to reduce environmental impacts of Iodinated Contrast Media Agents (ICMs). We analyze an extensive collection of ICMs. A modeling approach resting on molecular docking and Density Functional Theory simulations is employed to examine the adsorption process at the molecular level. The study also relies on a Quantitative Structure-Activity Relationship (QSAR) modeling framework to correlate molecular properties with the adsorption energy (Ead) of ICMs, thus enabling identification of the key mechanisms underpinning adsorption and of the key factors contributing to it. A collection of distinct QSAR-based models is developed upon relying on Multiple Linear Regression and a standard genetic algorithm method. Having at our disposal multiple models enables us to take into account the uncertainty associated with model formulation. Maximum Likelihood and formal model identification/discrimination criteria (such as Bayesian and/or information theoretic criteria) are then employed to complement the traditional QSAR modeling phase. This has the advantage of (a) providing a rigorous ranking of the alternative models included in the selected set and (b) quantifying the relative degree of likelihood of each of these models through a weight or posterior probability. The resulting workflow of analysis enables one to seamlessly embed DFT and QSAR studies within a theoretical framework of analysis that explicitly takes into account model and parameter uncertainty. Our results suggest that graphene-based surfaces constitute a promising adsorbent for ICMs removal, π-π stacking being the primary mechanism behind ICM adsorption. Furthermore, our findings offer valuable insights into the potential of graphene-based adsorbent materials for effectively removing ICMs from water systems. They contribute to ascertain the significance of various factors (such as, e.g., the distribution of atomic van der Waals volumes, overall molecular complexity, the presence and arrangement of Iodine atoms, and the presence of polar functional groups) on the adsorption process.

Interfacial Reconstructed Layer Controls the Orientation of Monolayer Transition-Metal Dichalcogenides.

Growing continuous monolayer films of transition-metal dichalcogenides (TMDs) without the disruption of grain boundaries is essential to realize the full potential of these materials for future electronics and optoelectronics, but it remains a formidable challenge. It is generally believed that controlling the TMDs orientations on epitaxial substrates stems from matching the atomic registry, symmetry, and penetrable van der Waals forces. Interfacial reconstruction within the exceedingly narrow substrate-epilayer gap has been anticipated. However, its role in the growth mechanism has not been intensively investigated. Here, we report the experimental conformation of an interfacial reconstructed (IR) layer within the substrate-epilayer gap. Such an IR layer profoundly impacts the orientations of nucleating TMDs domains and, thus, affects the materials' properties. These findings provide deeper insights into the buried interface that could have profound implications for the development of TMD-based electronics and optoelectronics.

Machine learning assisted classification of post-treatment amines for increasing the stability of organic-inorganic hybrid perovskites

The poor environmental stability of hybrid organic-inorganic perovskites (HOIPs) is the main reason hindering its application. The post-treatment of HOIPs with amines has gradually become a simple and effective protection strategy. Here, the support vector classifier (SVC) was proposed to select post-treatment amines for increasing the stability of HOIPs. Based on the seven optimal features, the accuracies of leave-one-out cross-validation (LOOCV) and test set of SVC are 95.45% and 90.91%, respectively. Additionally, the Shapley additive explanation (SHAP) was introduced to explain the model, and the results indicated that the features nHDon, PW2, Mv and MLOGP play the important roles in the prediction of amine molecular classes. Through the analysis of models, we found that amines with fewer hydrogen-bond donor atoms, larger mean atomic van der Waals volumes (scaled on Carbon atom), more hydrophobic groups, more branched chains, and smaller van der Waals surface area of atoms can be successfully used as potential candidates. Moreover, we successfully screened out 13718 candidate amines from tens of thousands of amines, which are expected to be the preferred post-treatment amines for HOIPs.

CP-MLR/PLS directed quantitative structure-activity relationship study on the histamine H3 receptor binding affinity: The cyclohexylamine based series

The histamine H3 receptor binding affinities of cyclohexylamine derivatives has been analysed with the topological and molecular features from Dragon software. Analysis of the structural features in conjunction with the biological endpoints in combinatorial protocol in multiple linear regression (CP-MLR) led to the identification of 26 descriptors for modelling the activity. The study clearly suggested the role of atomic properties such as mass, electronegativity or charge content, polarizability, atomic van der Waals volume, average valence connectivity index chi-5 and absence of number of acceptor atoms for H-bonds (N, O, F) type functionality to optimise the histamine H3 receptor binding affinity of titled compounds. The models developed and the participating descriptors advocate that the substituent groups of the cylohexylamine moiety hold scope for further modification in the optimization of the H3 receptor binding affinity. Analysis of these descriptors in partial least squares (PLS) highlighted their relative significance in modulating the biological response. The selected descriptors are enriched with information corresponding to the activity when compared to the remaining ones. Applicability domain analysis revealed that the suggested model matches the high quality parameters with good fitting power and the capability of assessing external data and all of the compounds was within the applicability domain of the proposed model and were evaluated correctly.

Synthesis, Antiproliferative Evaluation and QSAR Analysis of Novel Halogen- and Amidino-Substituted Benzothiazoles and Benzimidazoles.

Syntheses of 6-halogen-substituted benzothiazoles were performed by condensation of 4-hydroxybenzaldehydes and 2-aminotiophenoles and subsequent O-alkylation with appropriate halides, whereas 6-amidino-substituted benzothiazoles were synthesized by condensation of 5-amidino-2-aminothiophenoles and corresponding benzaldehydes. While most of the compounds from non-substituted and halogen-substituted benzothiazole series showed marginal antiproliferative activity on tested tumor cell lines, amidino benzazoles exhibited stronger inhibitory activity. Generally, imidazolyl benzothiazoles showed pronounced and nonselective activity, with the exception of 36c which had a strong inhibitory effect on HuT78 cells (IC50 = 1.6 µM) without adverse cytotoxicity on normal BJ cells (IC50 >100 µM). Compared to benzothiazoles, benzimidazole structural analogs 45a-45c and 46c containing the 1,2,3-triazole ring exhibited pronounced and selective antiproliferative activity against HuT78 cells with IC50 < 10 µM. Moreover, compounds 45c and 46c containing the methoxy group at the phenoxy unit were not toxic to normal BJ cells. Of all the tested compounds, benzimidazole 45a with the unsubstituted phenoxy central core showed the most pronounced cell growth inhibition on THP1 cells in the nanomolar range (IC50 = 0.8 µM; SI = 70). QSAR models of antiproliferative activity for benzazoles on T-cell lymphoma (HuT78) and non-tumor MDCK-1 cells elucidated the effects of the substituents at position 6 of benzazoles, demonstrating their dependence on the topological and spatial distribution of atomic mass, polarizability, and van der Waals volumes. A notable cell cycle perturbation with higher accumulation of cells in the G2/M phase, and a significant cell increase in subG0/G1 phase were found in HuT78 cells treated with 36c, 42c, 45a-45c and 46c. Apoptotic morphological changes, an externalization of phosphatidylserine, and changes in the mitochondrial membrane potential of treated cells were observed as well.

Quantum precision measurement of two-dimensional forces with [formula omitted]-Newton stability

High-precision sensing of vectorial forces has broad impact on both fundamental research and technological applications such as the examination of vacuum fluctuations and the detection of surface roughness of nanostructures. Recent years have witnessed much progress on sensing alternating electromagnetic forces for the rapidly advancing quantum technology—orders of magnitude improvement has been accomplished on the detection sensitivity with atomic sensors, whereas such high-precision measurements for static electromagnetic forces have rarely been demonstrated. Here, based on quantum atomic matter waves confined by a two-dimensional optical lattice, we perform precision measurement of static electromagnetic forces by imaging coherent wave mechanics in the reciprocal space. The lattice confinement causes a decoupling between real-space and reciprocal dynamics, and provides a rigid coordinate frame for calibrating the wavevector accumulation of the matter wave. With that we achieve a state-of-the-art sensitivity of 2.30(8)×10-26 N/Hz. Long-term stabilities on the order of 10-28 N are observed in the two spatial components of a force, which allows probing atomic Van der Waals forces at one millimeter distance. As a further illustrative application, we use our atomic sensor to calibrate the control precision of an alternating electromagnetic force applied in the experiment. Future developments of this method hold promise for delivering unprecedented atom-based quantum force sensing technologies.

Stabilization of porous borophene-graphene vertical heterostructure using unilateral hydrogenation

Unilateral surface passivation by hydrogen atoms is considered as an effective way to stabilize the borophene-graphene with periodic perforation. In the absence of hydrogen, a transition from porous graphene to graphenylene is observed. In the passivation by atomic hydrogen (H-passivated) porous borophene-graphene, the covalent and van der Waals forces are continuing their efforts between boron and carbon layers. The stability is confirmed by ab initio calculations of the cohesive energy and phonon spectrum. Using two calculation schemes, it is shown the semiconductor nature of the electronic properties, mainly due to boron sites. The elastic response is much weaker than that of borophene-graphene, but the out-of-plane piezoelectric effect is stronger. The optical properties are of particular interest mainly due to the absorption peak values in the optical range.

Machine learning and symbolic regression for adsorption of atmospheric molecules on low-dimensional TiO2

The adsorption of atmospheric gas molecules and the low-dimensional TiO2 species are relevant for the atmospheric photochemistry and energy-related photoelectrochemistry. In this manuscript, an accurate and interpretable machine learning model for the adsorption energies of the atmospheric and green-house molecules on a representative low-dimensional TiO2 surface is constructed, while the symbolic regression based on the genetic algorithm is employed to automatically design relevant molecular descriptors for the target adsorption outputs. Different algorithms are compared and the random forest algorithm achieves highest accuracies (r = 0.98, MAE = 0.29 and R2 = 0.96) to describe the adsorption energies of the atmospheric gas/TiO2 interface subjected to various external electric fields. The scientific analysis is performed to visualize the relationships between the input features and output target, including the impacts of the elemental chemical compositions and external electric field of the atmospheric gases on the adsorption energies; the beneficial effects of the NH/OH groups in the atmospheric molecules for the surface adsorption and the negatively effects of the atomic van der Waals volumes as well as the number of the heavy atoms are revealed. Importantly, a highly relevant and chemical-aware hybrid molecular descriptor is automatically designed via the symbolic regression process, which complements the machine learning model. This study highlights the machine learning and symbolic regression for the fundamental understanding of the structural interactions between the gas molecules and the low-dimensional metal oxide surfaces.

Interfacial triferroicity in monolayer chromium dihalide

Couplings between different ferroics in two-dimensional (2D) materials, with atomic thickness and van der Waals surface, have been long sought but not yet realized. Here we show the first-principles evidence of unique triferroic couplings in recently synthesized ${\mathrm{CrI}}_{2}$ monolayer. Its ferroelasticity with a low switching barrier stems from the Jahn-Teller effect. When coupled with the interaction of a substrate, a type of interfacial ferroelectricity with direction determined by the ferroelastic states can emerge which can be switched following the same low-barrier pathway of ferroelastic switching. The direction of its striped antiferromagnetism is also governed by its ferroelastic states, leading to a coupling of ferroelasticity, ferroelectricity, and magnetism. Such mechanism of multiferroic couplings, denoted as interfacial Jahn-Teller triferroicity, can be applied to other 2D materials such as ${\mathrm{CuCl}}_{2}$ and their Janus monolayers, enabling electrical manipulation of spintronics for efficient nonvolatile random-access memories.

Synthesis of two-dimensional Bi<sub>2</sub>O<sub>2</sub>Se on silicon substrate by chemical vapor deposition and its photoelectric detection application

As the scaling-down of semiconductor processing technology goes on, it is urgent to find the successor of silicon-based materials since the severe short channel effect lowers down their energy efficiency as logic devices. Owing to its atomic thickness and van der Waals surface, two-dimensional semiconductors have received huge attention in this area, among which Bi<sub>2</sub>O<sub>2</sub>Se has achieved a good trade-off among the carrier mobility, stability and costing. However, the synthesis of Bi<sub>2</sub>O<sub>2</sub>Se need some polarized substrates, which hinders its processing and application. Here, a Bi<sub>2</sub>O<sub>2</sub>Se layer with 25 µm in size and 51.0 nm in thickness is directly synthesized on a silicon substrate via chemical vapor deposition . A Field-effect transistor with a carrier mobility of 80.0 cm<sup>2</sup>/(V·s) and phototransistor with a photoresponsivity of 2.45×10<sup>4</sup> A/W and a photogain of 6×10<sup>4</sup> is also demonstrated, which hpossesses quite outstanding photodetection performance. Nevertheless, the high dark current and low on/off ratio brought by the large thickness leads to a fair detectivity (5×10<sup>10</sup> Jones). All in all, , although silicon substrate brings convenience in device fabricating, it is still needed to further optimizing the growth and integrating more applications of various two-dimensional materials .

Surface tension of liquids and binary mixtures from molecular dynamics simulations

In this work we assess and extend strategies for calculating surface tension of complex liquids from molecular dynamics simulations: the mechanical route and the instantaneous liquid interface (ILI) approach. The former employs the connection between stress tensor and surface tension, whereas the latter involves computation of instantaneous density field. Whereas the mechanical route is general, the ILI method involves system-dependent parameters restricting its original application to liquid water only. Here we generalize the approach to complex molecular liquids using atomic van der Waals radii. The performance of the approaches is evaluated on two liquid systems: acetonitrile and water–methanol mixture. In addition, we compare the effect of the computational models for interaction potentials based on semi-empirical electronic structure theory and classical force fields on the estimate of the surface tension within both stress tensor and ILI approaches.

Development of a Polarizable Force Field for Molecular Dynamics Simulations of Lithium-Ion Battery Electrolytes: Sulfone-Based Solvents and Lithium Salts.

A many-body polarizable force field (PFF) was developed for molecular dynamics (MD) simulations of sulfone-based solvents and lithium salts. Development of the polarizable force field included parameterization of atomic polarizabilities, electrostatic interactions, and van der Waals interactions of electrolyte components. 1λ6-thiolane-1,1-dione or sulfolane (SLF) compound was selected as one of the most appropriate solvents for high-voltage battery electrolytes. Atomic polarizabilities for the sulfolane solvent and lithium salts were obtained by means of a combination of quantum mechanics (QM) and molecular mechanics (MM) approaches using the isotropic atomic dipole polarizable (IADP) model. High-quality atomic polarizabilities were refined for 10 atomic types. Intermolecular interactions of Li+ ions with SLF were parameterized to reproduce the binding energies at the MP2/aug-cc-pvDZ level of theory in the gas phase. Intermolecular interactions of Li+ ions with polyatomic anions, such as nitrate [NO3]-, tetrafluoroborate [BF4]-, perchlorate [ClO4]-, hexafluorophosphate [PF6]-, bis(fluorosulfonyl)imide [FSI]-, and bis(trifluoromethylsulfonyl)imide [TFSI]-, were parameterized employing a similar methodology. A series of molecular dynamics simulations was performed for sulfolane-based electrolytes at several different lithium salt concentrations. Thermodynamic, structural, and transport properties were evaluated to validate the force field parameters against available simulation and experimental data. Transport properties of sulfolane were significantly improved as compared with those obtained from MD simulations using a nonpolarizable force field (NFF). A newly developed polarizable potential was shown to reproduce Li+ ion dynamics as a function of salt concentration. Faster diffusion of Li+ ions, among other electrolyte components, was obtained for high salt concentration electrolytes.

Quantum-mechanical force balance between multipolar dispersion and Pauli repulsion in atomic van der Waals dimers

The structure and stability of atomic and molecular systems with van der Waals (vdW) bonding are often determined by the interplay between attractive dispersion interactions and repulsive interactions caused by electron confinement. Arising due to different mechanisms -- electron correlation for dispersion and the Pauli exclusion principle for exchange-repulsion -- these interactions do not appear to have a straightforward connection. In this paper, we use a coarse-grained approach for evaluating the exchange energy for two coupled quantum Drude oscillators and investigate the mutual compensation of the attractive and repulsive forces at the equilibrium distance within the multipole expansion of the Coulomb potential. This compensation yields a compact formula relating the vdW radius of an atom to its multipole polarizabilities, $R_{\rm vdW} = A_l^{\,}\, \alpha_l^{{2}/{7(l+1)}}$, where $l$ is the multipole rank and $A_l$ is a conversion factor. Such a relation is compelling because it connects an electronic property of an isolated atom (atomic polarizability) with an equilibrium distance in a dimer composed of two closed-shell atoms. We assess the accuracy of the revealed formula for noble-gas, alkaline-earth, and alkali atoms and show that the $A_l$ can be assumed to be universal constants. Besides a seamless definition of vdW radii, the proposed relation can also be used for the efficient determination of atomic multipole polarizabilities solely based on the corresponding dipole polarizability and the vdW radius. Finally, our work provides a basis for the construction of efficient and minimally-empirical interatomic potentials by combining multipolar interatomic exchange and dispersion forces on an equal footing.

Chemometric QSAR modeling of acute oral toxicity of Polycyclic Aromatic Hydrocarbons (PAHs) to rat using simple 2D descriptors and interspecies toxicity modeling with mouse

The information of the acute oral toxicity for most polycyclic aromatic hydrocarbons (PAHs) in mammals are lacking due to limited experimental resources, leading to a need to develop reliable in silico methods to evaluate the toxicity endpoint. In this study, we developed the quantitative structure-activity relationship (QSAR) models by genetic algorithm (GA) and multiple linear regression (MLR) for the rat acute oral toxicity (LD50) of PAHs following the strict validation principles of QSAR modeling recommended by OECD. The best QSAR model comprised eight simple 2D descriptors with definite physicochemical meaning, which showed that maximum atom-type electrotopological state, van der Waals surface area, mean atomic van der Waals volume, and total number of bonds are main influencing factors for the toxicity endpoint. A true external set (554 compounds) without rat acute oral toxicity values, and 22 limit test compounds, were firstly predicted along with reliability assessment. We also compared our proposed model with the OPERA predictions and recently published literature to prove the prediction reliability. Furthermore, the interspecies toxicity (iST) models of PAHs between rat and mouse were also established, validated and employed for filling data gap. Overall, our developed models should be applicable to new or untested or not yet synthesized PAHs falling within the applicability domain (AD) of the models for rapid acute oral toxicity prediction, thus being important for environmental or personal exposure risk assessment under regulatory frameworks.

Methods of transferring two-dimensional materials

The advent of two-dimensional (2D) materials, a family of materials with atomic thickness and van der Waals (vdWs) interlayer interactions, offers a new opportunity for developing electronics and optoelectronics. For example, semiconducting 2D materials are promising candidates for extending the Moore's Law. Typical 2D materials, such as graphene, hexagonal boron nitride (h-BN), black phosphorus (BP), transition metal dichalcogenides (TMDs), and their heterostrcutures present unique properties, arousing worldwide interest. In this review the current progress of the state-of-the-art transfer methods for 2D materials and their heterostructures is summarized. The reported dry and wet transfer methods, with hydrophilic or hydrophobic polymer film assistance, are commonly used for physical stacking to prepare atomically sharp vdWs heterostructure with clear interfaces. Compared with the bottom-up synthesis of 2D heterostructures using molecular beam epitaxy (MBE) or chemical vapor deposition (CVD), the construction of 2D heterostructures by transfer methods can be implemented into a curved or uneven substrate which is suitable for pressure sensing, piezoelectric conversion as well as other physical properties’ research. Moreover, the transfer of 2D materials with inert gas protected or in vacuum operation can protect moisture-sensitive and oxygen-sensitive 2D materials from degerating and also yield interfaces with no impurities. The efficient and non-destructive large-area transfer technology provides a powerful technical guarantee for constructing the 2D heterostructures and exploring the intrinsic physical and chemical characteristics of materials. Further development of transfer technology can greatly facilitate the applications of 2D materials in high-temperature superconductors, topological insulators, low-energy devices, spin-valley polarization, twistronics, memristors, and other fields.

Molecular descriptors-based models for estimating net heat of combustion of chemical compounds

The heating values of fuels are determined by Heat of Combustion (ΔHC∘)in which the higher amount is more lucrative. Moreover, one of the best methods to compare the stabilities of chemical materials is using ΔHC∘. Therefore, improving precise and general models to estimate this property in different areas such as industries and academic perspective should be considered. In this study, three models namely Least Square Support Vector Machine optimized by Coupled Simulated Annealing optimization algorithm (CSA-LSSVM), Genetic Programming (GP) and Adaptive-Neuro Fuzzy Inference System optimized by PSO, and GA methods (PSO-ANFIS and GA-ANFIS) were applied to estimate ΔHC∘ Also, ΔHC∘ can be expressed by the GP model with an equation. The input parameters of the models are total carbon atoms in a molecule (nC), sum of atomic van der Waals volumes (scaled on carbon atom) (Sv), Broto–Moreau autocorrelation of a topological structure (ATS2m), and total Eigenvalue from electronegativity weighted distance matrix (siege). In addition, two parameter models based on measureable variables of nC and Sv are proposed. In a comprehensive set, 1714 data points were used to fulfill and develop the models. Results demonstrate that the models are trustworthy and accurate (especially the PSO-ANFIS model) in comparison with other recently developed literature models.

Molecular Docking and QSAR Studies of Coumarin Derivatives as NMT Inhibitors: Simple Structural Features as Potential Modulators of Antifungal Activity

Background:Treatments of fungal diseases, including Candidiasis, remain not up to scratch in spite of the mounting catalog of synthetic antifungal agents. These have served as the impetus for investigating new antifungal agents based on natural products. Consequently, genetic algorithm-multiple linear regression (GA-MLR) based QSAR (Quantitative Structure-Activity Relationship) studies of coumarin analogues along with molecular docking were carried out.Methods:Coumarin analogues with their MIC values were used to generate the training and test sets of compounds for QSAR models development; the analogues were also docked into the binding pocket of NMT (MyristoylCoA: protein N-myristoyltransferase).Results and Discussion:The statistical parameters for internal and external validation of QSAR analysis (R2= 0.830, Q2= 0.758, R2Pred= 0.610 and R2m overall= 0.683 ), Y Randomization, Ridge trace, VIF, tolerance and model criteria of Golbraikh and Tropsha data illustrate the robustness of the best proposed QSAR model. Most of the analogues bind to the electrostatic, hydrophobic clamp and display hydrogen bonding with amino acid residues of NMT. Interestingly, the most active coumarin analogue (MolDock score of -189.257) was docked deeply within the binding pocket of NMT, thereby displaying hydrogen bonding with Tyr107, Leu451, Leu450, Gln226, Cys393 and Leu394 amino acid residues.Conclusion:The combinations of descriptors from various descriptor subsets in QSAR analysis have highlighted the role of atomic properties such as polarizability and atomic van der Waals volume to explain the inhibitory activity. The models and related information may pave the way for important insight into the designing of putative NMT inhibitors for Candida albicans.

GA-SMLR-Based QSAR Modeling and Molecular Docking Studies of Bisamidine Derivatives as NMT Inhibitors

The present investigation deals with a combination of genetic algorithm-stepwise multiple linear regression (GA-SMLR)-based QSAR modeling and molecular docking applied to bisamidine analogues in an attempt to explore their role as novel NMT inhibitors of Candida albicans. In this regard, 43 bisamidine analogues were investigated for the development of mathematical models. The robustness of the proposed QSAR model was not only ascertained through traditionally used internal and external validation statistical parameters (Q2= 0.740, R2 = 0.819, R_Pred^2 = 0.636) but also through various R_(m)^2 metrics proposed by Roy and Mitra. The descriptors recognized in the QSAR analysis have culminated a significant role of atomic van der Waals volume, topology, nature of bond and dipole moment to modulate the antifungal activity of compounds under investigation. The most active compound revealed enhanced binding potency with MolDock score of -183.451 kcal/mol and displayed hydrogen bond interactions with active amino acids Leu177, Thr211, Tyr225, and IIe111 of NMT.

In silico evaluation of inhibitory potential of novel triazole derivatives against therapeutic target myristoyl-CoA: protein N-myristoyltransferase (NMT) of Candida albicans

This paper explores the confluence of genetic algorithm-multiple linear regression (GA-MLR) based quantitative structure–activity relationship (QSAR) modeling and molecular docking simulation studies relevant to the novel and effective triazole derivatives as NMT inhibitors in an attempt to develop superior antifungal activity coupled with less susceptibility to develop resistance. Initially, thirteen penta-variant models were generated through hybrid GA-MLR based QSAR approach. Eventually, a general pooled model was derived through the collective consideration of all the descriptors incorporated in the stated thirteen models. The model was found to be predictive model as per traditional internal and external validation parameters (R2 = 0.829, Q2 = 0.759, $${R}_{\mathrm{adj}}^{2}\hspace{0.17em}$$ = 0.796, $${R}_{\mathrm{Pred}}^{2}$$ = 0.613) along with Y-randomization test and recently proposed various $$r_{m}^{2}$$ metrics. The proposed model is in agreement with Golbraikh and Tropsha recommendations supplemented by non-existence of multicollinearity via ridge trace, VIF trace and tolerance data analysis. The modeled descriptors signify the role of atomic van der Waals volume, nitrogen-containing fragment, number of hydrogen atoms and hydrogen bonding in modulating antifungal activity. Further, the molecular docking simulation studies reinforce the role of hydrogen bonding interactions in modulating the activity; as reflected by QSAR analysis; docking poses have revealed that four triazole derivatives were profusely bonded through hydrophobic, electrostatic and hydrogen bond interactions and stabilize into the active site of target protein NMT. Asn392 and His227 are key amino acid residues liable for generation of hydrogen bond interactions whereas Leu350 and Tyr354 amino acid residues were accountable for steric interactions. Based on the studies the two molecules are hypothesized and proposed with optimized biological function.