Molecular Geometry Optimization Research Articles

Evaluation of the Xtb Semiempirical Method for the Prediction of Antioxidant Properties in Alzheimer’s Disease: Salen-Type Ligands

Alzheimer’s disease (AD) stands as the predominant form of dementia, accounting for up to 70% of all cases worldwide. AD is a complex disease with various contributing factors. Evidence suggests that the metalliccomplexes formed by the β-amyloid peptide (Aβ) and extraneuronal copper can catalyze the generation ofreactive oxygen species, consequently increasing oxidative stress and contributing to the decline of neurons. This interaction underscores the significance of bioavailable copper as a crucial redox-active target in exploring protocols for multifunctional agents in AD treatment. In the field of computational chemistry, density functional theory (DFT) is widely accepted as a standard method across different disciplines. Despite this, DFT presents computational challenges, particularly in screening extensive molecular sets during the initial phases of drug research. Recent advances in semiempirical quantum mechanical methods (SQM) offer a promising alternative, providing rapid molecular geometry optimization and approximate estimation of thermodynamical properties, being at least two orders of magnitude faster than traditional DFT calculations. In this work, we present an evaluation of the GFNn-xTB SQM methods in the rapid screening of antioxidant properties in AD, performed on a set of salen ligands by calculating the standard reduction potentials of their copper complexes as key property. Results show that the implementation of GFNn-xTB SQM calculations before DFT evaluations is

Synthesis, physicochemical and quantum chemical studies of 7-azaindolinium-3,5-dinitrobenzoate single crystal for nonlinear optical applications

Abstract A novel hydrogen bonded single crystal of 7-azaindolinium-3,5-dinitrobenzoate (7AIDNB) has been grown, and the structure of the crystal was determined by single-crystal XRD analysis. The results show that the grown crystal belongs to a triclinic crystal system with centrosymmetric space group P-1 and the unit cell parameters: a = 6.998(9) Å, b = 8.498(12) Å, c = 13.103(17) Å, V = 710.78(17) Å3, Z = 2. The presence of expected discrete functional groups in 7AIDNB has been identified by an FT-IR spectroscopic study. The thermal stability and decomposition of the title crystal have been studied by TG-DTA analysis. The optical property of the grown crystal has been analyzed by UV-Vis-NIR spectroscopy analysis. The DFT with 6-311++G(d, p) basis set has been used to examine optimization of molecular geometry, Mulliken charge, and HOMO-LUMO. As the frequency of the grown crystal increases, the dielectric loss and dielectric constant decrease. The third-order NLO parameters of 7AIDNB have been examined by a Z-scan technique.

MolOpt: Autonomous Molecular Geometry Optimization Using Multiagent Reinforcement Learning.

Most optimization problems require the user to select an algorithm and, to some extent, also tune it for better performance. Although intuition and knowledge about the problem can speed up these selection and fine-tuning processes, users often use trial-and-error methodologies, which can be time-consuming and inefficient. With all of that in mind and much more, the concept of "learned optimizers", "learning to learn", and "meta-learning" has been gathering attention in recent years. In this article, we propose MolOpt that uses multiagent reinforcement learning (MARL) for autonomous molecular geometry optimization (MGO). Typically MGO algorithms are hand-designed, but MolOpt uses MARL to learn a learned optimizer (policy) that can perform MGO without the need for other hand-designed optimizers. We cast MGO as a MARL problem, where each agent corresponds to a single atom in the molecule. MolOpt performs MGO by minimizing the forces on each atom of the molecule. Our experiments demonstrate the generalizing ability of MolOpt for the MGO of propane, pentane, heptane, hexane, and octane when trained on ethane, butane, and isobutane. In terms of performance, MolOpt outperforms the MDMin optimizer and demonstrates performance similar to that of the FIRE optimizer. However, it does not surpass the BFGS optimizer. The results demonstrate that MolOpt has the potential to introduce innovative advancements in MGO by providing a novel approach using reinforcement learning (RL), which may open up new research directions for MGO. Overall, this work serves as a proof-of-concept for the potential of MARL in MGO.

Integrating Chemical Information into Reinforcement Learning for Enhanced Molecular Geometry Optimization.

Geometry optimization is a crucial step in computational chemistry, and the efficiency of optimization algorithms plays a pivotal role in reducing computational costs. In this study, we introduce a novel reinforcement-learning-based optimizer that surpasses traditional methods in terms of efficiency. What sets our model apart is its ability to incorporate chemical information into the optimization process. By exploring different state representations that integrate gradients, displacements, primitive type labels, and additional chemical information from the SchNet model, our reinforcement learning optimizer achieves exceptional results. It demonstrates an average reduction of about 50% or more in optimization steps compared to the conventional optimization algorithms that we examined when dealing with challenging initial geometries. Moreover, the reinforcement learning optimizer exhibits promising transferability across various levels of theory, emphasizing its versatility and potential for enhancing molecular geometry optimization. This research highlights the significance of leveraging reinforcement learning algorithms to harness chemical knowledge, paving the way for future advancements in computational chemistry.

Talarolide A and Talaropeptides A-D: Potential Marine-Derived Therapeutic Peptides with Interesting Chemistry and Biological Activity Studied through Density Functional Theory (DFT) and Conceptual DFT.

Molecules sourced from marine environments hold immense promise for the development of novel therapeutic drugs, owing to their distinctive chemical compositions and valuable medicinal attributes. Notably, Talarolide A and Talaropeptides A-D have gained recent attention as potential candidates for pharmaceutical applications. This study aims to explore the chemical reactivity of Talarolide A and Talaropeptides A-D through the application of molecular modeling and computational chemistry techniques, specifically employing Conceptual Density Functional Theory (CDFT). By investigating their chemical behaviors, the study seeks to contribute to the understanding of the potential pharmacological uses of these marine-derived compounds. The molecular geometry optimizations and frequency calculations were conducted using the Density Functional Tight Binding (DFTBA) method. This was followed by a subsequent round of geometry optimization, frequency analysis, and computation of electronic properties and chemical reactivity descriptors. We employed the MN12SX/Def2TZVP/H2O model chemistry, utilizing the Gaussian 16 program and the SMD solvation model. The analysis of the global reactivity descriptors arising from CDFT was achieved as well as the graphical comparison of the dual descriptor DD revealing the areas of the molecules with more propensity to suffer a nucleophilic or electrophilic attack. Additionally, Molinspiration and SwissTargetPrediction were considered for the calculation of molecular characteristics and predicted biological targets. These include enzymes, nuclear receptors, kinase inhibitors, GPCR ligands, and ion channel modulators. The graphical results show that Talarolide A and the Talaropeptides A-D are likely to behave as protease inhibitors.

Experimental, spectroscopic, and theoretical investigation on structural and anticancer activities of Schiff bases derived from isonicotinohydrazide

Isoniazid hydrazones are promising possibilities as medicines since they have preserved efficacy and are less toxic and resistant to resistance than parent Isoniazid (INH). Here, we have synthesized a series of Schiff bases (INH1–9) derived from a clinically approved antitubercular drug Isoniazid (INH). These synthesized ligands have been characterized by various spectroscopic techniques like IR, UV–vis., NMR, HRMS, etc. Moreover, single crystal of three derivatives viz. INH4, INH8, and INH9 has been determined and they crystallize in monoclinic crystal system. Hirshfeld surface analysis has been performed to ascertain intermolecular interactions present in these compounds. The molecular geometry optimization and vibrational analysis of these compounds were performed using density functional theory (DFT) studies utilizing B3LYP/6–31++G(d, p) basis set. The TD-DFT analysis was also performed to understand electronic transitions and the nature of FMO in these compounds. There was a good correlation found between theoretical and experimental values, thereby confirming the molecular structures of synthesized compounds. Molecular docking studies were performed to obtain more insights on potential anticancer activities of these compounds along with standard anticancer drugs 5-fluorouracil and Tamoxifen against MDM2 (4HG7) protein. The outcome revealed a significant binding affinity of these compounds with target protein even better than 5-fluorouracil and comparable to Tamoxifen. The compounds (INH4 and INH9) having the strongest binding affinity with the target protein are further experimentally evaluated for their in-vitro cytotoxic action on Dalton's lymphoma cells employing MTT assay, fluorescence microscopy, and flow cytometry. IC50 value (150 µg/ml) of this compound is equated with before-reported complexes/molecules/extracts and found it has better or comparable cytotoxicity.

Vibrational spectroscopic studies, DFT, and molecular docking investigations of 4-fluoro- 3-methyl benzophenone

Potential energy distribution was used to describe the optimum molecular geometry, FT-IR, FT-Raman, and UV-Vis spectra, vibrational frequency bands, and assignment of suitable vibrational modes of 4-fluoro-3-methyl benzophenone. Utilizing DFT/B3LYP with a 6–31 +G level, spectroscopic investigations are conducted. The findings of the computation were utilized to model 4-fluoro-3-methyl benzophenone, which has a very good correlation with the spectra that were observed. The TDFT was used so that oscillator strengths could be determined. In order to determine how much charge was transferred by the molecule, HOMO and LUMO analyses were performed. Through the use of the NBO analysis, it was feasible to determine the existence of internal charge transfer, hyperconjugation, and stabilizing energy. DFT techniques were applied to investigate Mulliken's charges and hyperpolarizability characteristics. In the molecular docking investigation, the 4-Fluoro-3-methylbenzophenone molecule was docked with Prostaglandin H2 Synthase-1 and Prostaglandin H2 Synthase-2. Due to their hormone-like properties, the phospholipid molecules known as prostaglandins (PGs) play a crucial part in the body's pain and inflammatory responses. These compounds are composed of fatty acids, which are prevalent in cell membranes. Membrane-associated enzyme cyclooxygenase (COX) is responsible for this process. There are two isoforms of this enzyme: COX-1 and COX-2.

Synthesis, structural characterization and antibacterial activity evaluation of novel quinolone-1,2,3-triazole-benzimidazole hybrids

In this investigation, a series of 2-quinolone-benzimidazole hybrids linked by a 1,2,3-triazole moiety were prepared using copper-catalyzed Huisgen 1,3-dipolar cycloaddition and condensation reaction. The newly synthesized compounds were characterized by 1H NMR, 13C NMR, single crystal XRD and HRMS analysis. The density functional theory (DFT) calculations were performed using B3LYP/6–31+G (d,p) basis level set for the optimization of molecular geometries of compounds 7a, 7c, 7f and 7l The conformational analysis predicted the existence of two main conformations: a closed and an open conformation. In vitro antibacterial activity was assessed against Gram-positive bacterium Staphylococcus aureus (S. aureus) and Gram-negative bacterium Escherichia coli (E. coli). The bioassays revealed that compounds 7a, 7c, 7f and 7l inhibited the growth of E. coli at a concentration of 0.14 µmol/mL, which was nearly 2-fold stronger than the control drug ampicillin (0.26 µmol/mL). Besides, compounds 7f and 7l exhibited the most promising activity against S. aureus with MIC and MBC values of 0.07 µmol/mL for 7f and 0.14 µmol/mL for 7l compared to 0.27 µmol/mL for ampicillin. These results were supported by the chemical reactivity parameters and electrostatic surface potential (MEP) obtained using theoretical calculations. Molecular docking study of the most active compounds using DNA gyrase A and DNA gyrase B proteins of S. aureus also confirmed the experimental findings.

A spur to molecular geometry optimization: Gradient-enhanced universal kriging with on-the-fly adaptive ab initio prior mean functions in curvilinear coordinates.

We present a molecular geometry optimization algorithm based on the gradient-enhanced universal kriging (GEUK) formalism with ab initio prior mean functions, which incorporates prior physical knowledge to surrogate-based optimization. In this formalism, we have demonstrated the advantage of allowing the prior mean functions to be adaptive during geometry optimization over a pre-fixed choice of prior functions. Our implementation is general and flexible in two senses. First, the optimizations on the surrogate surface can be in both Cartesian coordinates and curvilinear coordinates. We explore four representative curvilinear coordinates in this work, including the redundant Coulombic coordinates, the redundant internal coordinates, the non-redundant delocalized internal coordinates, and the non-redundant hybrid delocalized internal Z-matrix coordinates. We show that our GEUK optimizer accelerates geometry optimization as compared to conventional non-surrogate-based optimizers in internal coordinates. We further showcase the power of the GEUK with on-the-fly adaptive priors for efficient optimizations of challenging molecules (Criegee intermediates) with a high-accuracy electronic structure method (the coupled-cluster method). Second, we present the usage of internal coordinates under the complete curvilinear scheme. A complete curvilinear scheme performs both surrogate potential-energy surface (PES) fitting and structure optimization entirely in the curvilinear coordinates. Our benchmark indicates that the complete curvilinear scheme significantly reduces the cost of structure minimization on the surrogate compared to the incomplete curvilinear scheme, which fits the surrogate PES in curvilinear coordinates partially and optimizes a structure in Cartesian coordinates through curvilinear coordinates via the chain rule.

Machine Learning Diffusion Monte Carlo Forces

Diffusion Monte Carlo (DMC) is one of the most accurate techniques available for calculating the electronic properties of molecules and materials, yet it often remains a challenge to economically compute forces using this technique. As a result, ab initio molecular dynamics simulations and geometry optimizations that employ Diffusion Monte Carlo forces are often out of reach. One potential approach for accelerating the computation of "DMC forces" is to machine learn these forces from DMC energy calculations. In this work, we employ Behler-Parrinello Neural Networks to learn DMC forces from DMC energy calculations for geometry optimization and molecular dynamics simulations of small molecules. We illustrate the unique challenges that stem from learning forces without explicit force data and from noisy energy data by making rigorous comparisons of potential energy surface, dynamics, and optimization predictions among ab initio density functional theory (DFT) simulations and machine-learning models trained on DFT energies with forces, DFT energies without forces, and DMC energies without forces. We show for three small molecules─C2, H2O, and CH3Cl─that machine-learned DMC dynamics can reproduce average bond lengths and angles within a few percent of known experimental results at one hundredth of the typical cost. Our work describes a much-needed means of performing dynamics simulations on high-accuracy, DMC PESs and for generating DMC-quality molecular geometries given current algorithmic constraints.

Duplex DNA Retains the Conformational Features of Single Strands: Perspectives from MD Simulations and Quantum Chemical Computations.

Molecular dynamics simulations and geometry optimizations carried out at the quantum level as well as by quantum mechanical/molecular mechanics methods predict that short, single-stranded DNA oligonucleotides adopt conformations very similar to those observed in crystallographic double-stranded B-DNA, with rise coordinates close to ≈3.3 Å. In agreement with the experimental evidence, the computational results show that DNA single strands rich in adjacent purine nucleobases assume more regular arrangements than poly-thymine. The preliminary results suggest that single-stranded poly-cytosine DNA should also retain a substantial helical order in solution. A comparison of the structures of single and double helices confirms that the B-DNA motif is a favorable arrangement also for single strands. Indeed, the optimal geometry of the complementary single helices is changed to a very small extent in the formation of the duplex.

Insights into solvent effects on molecular properties, physicochemical parameters, and NLO behavior of brinzolamide, a bioactive sulfonamide: A computational study

In this study, quantum chemical calculations performed on brinzolamide (BZ), a sulfonamide derivative widely used as an antibacterial and antifungal drug, are presented. The molecular geometry optimizations of BZ were calculated by using both B3LYP functional and HF method with the 6-31G (d, p) basis set via Gaussian software. Since solvent phase behaviors are extremely important especially for drug molecules, calculations were renewed for BZ in ten different solvent environments. Universal solvation model SMD (Solvent Model based on Density) was utilized for solvent phase simulations. Mulliken and natural atomic charges, frontier molecular orbital (FMO) energies, quantum chemical descriptors were studied in detail according to solvent and method variation. In addition, the partition coefficient (logPow) value, which is an important parameter for the activity of the drug candidates, was theoretically estimated according to the relevant formula through the computed values for the water and 1-octanol phases for each methodology. The slightly lipophilic character of the BZ molecule was supported by the obtained values and the molecular lipophilicity potential map prepared by means of Molinspiration Galaxy 3D Structure Generator v2018.01 beta program. In the nonlinear optical (NLO) property analysis, dipole moment (μtot), mean polarizability (αtot), anisotropic polarizability (Δα), and mean first-order hyperpolarizability (βtot) values of BZ and the solvent effect on these values have been reported. Last, the interaction energies between donor and acceptor orbitals were calculated by the analysis of natural bond orbitals, and the obtained results were summarized.

Theoretical study of the Meisenheimer and charge-transfer complexes formed upon colorimetric determination of nitroaromatic explosives

In this paper, we present a theoretical study of structure and UV-vis spectra of 11 colored complexes of nucleophiles with nitroaromatic energetic materials. Two different schemes were found to be the most suitable for absorption spectra simulation. In the case of covalently bound Meisenheimer complexes, the time-dependent density functional theory (TD-DFT) approach with the TPSS functional was the most accurate. Meanwhile, for intermolecular charge-transfer complexes, the closest spectral pattern was provided by the time-dependent Hartree-Fock (TD-HF) scheme with modified exchange contribution (40%). It has been found that the binding type is determined predominantly by the steric factors and less by the electronic effects of the nucleophile, which was approved by the quantum theory of atoms in molecules (QTAIM) analysis of the formed bond types and nucleophilicity index calculations. For the charge-transfer complex, an appropriate configuration with the intermolecular separation between the local electrophilic and nucleophilic sites (the C1···N distance) of about 3.1 Å, was revealed using both classical molecular dynamics simulations and geometry optimizations in polar continuum. Absorption energies and intensities of the electronic transitions are generally well-reproduced in all 11 cases and demonstrate a local π–π* excitation in the covalently-bound complexes and pure charge transfer in intermolecular system. The applied computational methods allow reproducing of the sample colors with a high degree of similarity, which may find their application for modeling of new reagents with other expected colorimetric characteristics.

Drug-Receptor Interaction of Peptidic HIV-1 Protease: The Hydrophobic Effect-I

When a drug interacts with its receptor, the nonpolar substituent of drug and receptor proteins attract each other because they have opposite magnitude with respect to each other. X-rays structure studies reflected that the S2/S2’ pocket in HIV-1 protease enzyme are essentially hydrophobic. The residues that make up these pockets are Val-32, Ile-47, Ile-50, and Ile-84 in each monomeric polypeptidic unit of the protease enzyme. Δπdr and ΔSASAdr have been used to measure the extent of hydrophobic interaction between peptidic protease inhibitors and receptor proteins (binding site: valine‒isoleucine; and catalytic site: glycine‒aspartic acid‒threonine) on the HIV-1 protease enzyme. For measurement of hydrophobic interaction, the molecular modeling and geometry optimization of all the inhibitors and the receptor amino acids have been carried out with CAChe Pro software by opting semiempirical PM3 methods. Log P was calculated using the atom-typing scheme of Ghose and Crippen, while solvent accessible surface area by conductor likes screening model. πd, πr, SASASd and SASASr well describe the hydrophobicities of the substituents and play the effective role for site selectivity for interaction of the drug with the receptor. Comparative study of values of Δπdr and ΔSASAdr show the order of hydrophobic interaction with respect to amino acids: Asp > Thr > Val > Ile and Thr > Val > Asp > Ile, respectively. Further, comparative study of the values of (ΣΔπdr)binding-site, (ΣΔπdr)catalytic-site, (ΣΔSASAdr)binding-site, (ΣΔSASAdr)catalytic-site shows that peptidic HIV-1-PRIs interact with binding site rather than catalytic site as binding site have lower value of ΣΔπdr and ΣΔSASAdr. Among the binding site, Val has maximum interaction than Ile, as it has lower vale of Δπdr and ΔSASAdr.

Extended Rule of Five and Prediction of Biological Activity of peptidic HIV-1-PR Inhibitors

In this research work, we have applied “Lipinski’s RO5” for pharmacokinetics (PK) study and to predict the activity of peptidic HIV-1 protease inhibitors. Peptidic HIV-1-PRIs have been taken from literature with their observed biological activities (OBAs) in term of IC50. The logarithms of the inverse of IC50 have been used as biological end point o(log1/C) in the study. For calculation of physicochemical parameters, the molecular modeling and geometry optimization of all the derivatives have been carried out with CAChe Pro software using semiempirical PM3 method. Prediction of the biological activity of the inhibitors has shown that the best QSAR model is constructed from pharmacokinetic properties, molecular weight and hydrogen bond acceptor. This also proved that these properties play important role to describe the PKs of the drugs. On the basis of the derived models one can build up a theoretical basis to access the biological activity of the compounds of the same series.

DFT study of super-halogen (Al7) doped carbon nitride (C2N) and its nonlinear optical properties

In this study, density functional theory (DFT) has been employed to conduct a systematic study of a non-linear optical and electrical characteristic of a specific class of carbon nitride (C2N) doped with super-halogen cluster (Al7). The optimization of molecular geometries of pure C2N (A1), single doped Al7@C2N (A2), and double doped 2Al7@C2N (A3) systems and comparison of their NLO properties are carried out at three functionals CAM-B3LYP, B3LYP, and LC-BLYP along with 6-31G basis set. Among these three methods of DFT, the B3LYP method provides the lowest bandgap and highest first hyperpolarizability (βo). The doping of Al7 on C2N significantly enhances the charge transfer characteristics by lowering the bandgap (Eg) from 2.32 eV to 0.99 and 0.95 eV for A1, A2, and A3 systems respectively. βo and dipole moment (μ) also increase remarkably from 1649 to 30221 au and 2.04 to 10.26 D, respectively. Among all the three A1, A2, and A3 systems, A2 system is extraordinarily efficient in HOMO and LUMO energies, Eint, EVI, as well as UV-Vis absorption. A2 exhibits significant absorption in the far-red region, which has been utilized in lasers. TD-DFT calculations along with FMO, DOS, and EDDM analysis were utilized to track down the source of electro-optical and structural property correlations. The mentioned systems, particularly single doped A2 system, have the opportunity to be used as useful NLO material.

Dual-Level Training of Gaussian Processes with Physically Inspired Priors for Geometry Optimizations.

Gaussian process (GP) regression has been recently developed as an effective method in molecular geometry optimization. The prior mean function is one of the crucial parts of the GP. We design and validate two types of physically inspired prior mean functions: force-field-based priors and posterior-type priors. In this work, we implement a dual-level training (DLT) optimizer for the posterior-type priors. The DLT optimizers can be considered as a class of optimization algorithms that belong to the delta-machine learning paradigm but with several major differences compared to the previously proposed algorithms in the same paradigm. In the first level of the DLT, we incorporate the classical mechanical descriptions of the equilibrium geometries into the prior function, which enhances the performance of the GP optimizer as compared to the one using a constant (or zero) prior. In the second level, we utilize the surrogate potential energy surfaces (PESs), which incorporate the physics learned in the first-level training, as the prior function to refine the model performance further. We find that the force-field-based priors and posterior-type priors reduce the overall optimization steps by a factor of 2-3 when compared to the limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) optimizer as well as the constant-prior GP optimizer proposed in previous works. We also demonstrate the potential of recovering the real PESs with GP with a force-field prior. This work shows the importance of including domain knowledge as an ingredient in the GP, which offers a potentially robust learning model for molecular geometry optimization and for exploring molecular PESs.

Experimental, Molecular Docking and Molecular Dynamics Investigation on Newly Synthesized Diethyl 4-(Anthracen-9-yl)-2,6-Dimethyl-1,4-Dihydropyridine-3,5-Dicarboxylate

The novel compound diethyl 4-(anthracen-9-yl)-2,6-dimethyl-1,4-dihydropyridine-3,5-dicarboxylate (DHPA) was synthesized via the Hantzsch method. The synthesized compound was studied through FT-IR, FT-Raman, UV-Vis and NMR studies were compared with Density Functional Theory (DFT) calculations using the B3LYP exchange-correlation functional to evaluate the synthesized compound. Analyzing the molecular structure of the compound provides more biological information that aids in drug design. Quantum chemical calculations using the DFT method, such as molecular geometry optimization, vibrational assignments, frontier molecular orbitals (FMO), molecular electrostatic potential (MEP) map, natural bond orbitals (NBO), global reactive descriptors, and local reactive descriptors, were performed to analyze the structural characteristics and biological properties of the compound. Finally, molecular docking and molecular dynamics were used to determine the binding interaction and stability of the compound in the docked site. The results from simulation studies suggest that DHPA might be an alternative calcium channel blocker drug.

DFT-Based Prediction of Anti-Leishmanial Activity of Carboxylates and Their Antimony(III) Complexes Against Five Leishmanial Strains

Carboxylates and their antimony(III) complexes experimentally scanned earlier for anti-leishmanial activity (IC50) against five leishmanial strains viz., L. major, L. major (Pak), L. tropica, L. mex mex, and L. donovani. These activities have been theoretically predicted by DFT method along with quantitative structure-activity relationship (QSAR) study. Molecular modeling and geometry optimization of the all the eight compounds have been performed on workspace program of CAChe Pro software of Fujitsu by opting B88-PW91 (Becke '88; Perdew & Wang '91) GGA (generalized-gradient approximation) energy functional with DZVP (double-zeta valence polarized ) basis set in DFT (Density Functional Theory). For QSAR, multiple linear regression (MLR) analysis has been performed on Project Leader Program associated with CAChe. The reliability of correlation between experimental activities and predicted activities are r2 = 0.826, r2CV = 0.426 (L. major); r2 = 0.905, r2CV = 0.507 (L. major (Pak)); r2 = 0.980, r2CV = 0.932 (L. tropica); r2 = 0.781, r2CV = 0.580 (L. mex mex) and r2 = 0.634, r2CV = 0.376 (L. donovani), and a comparison of the experimental values and the values obtained by theoretical calculations has been presented pictorially that shows close resemblance.

Electronic transport properties of compressed and stretched helicene-graphene nanostructures, a theoretical study

We present our ab initio simulations of strained helicene molecular junction. First, we present ab-initio models of [n]helicenes up to [100]helicene done by two different recent reactive force field molecular geometry optimization methods (ReaxFF CHOCsKNaClIFLi, 2019 and CHONSMgPNaCuCl, 2018). We used the [4]helicene and [10]helicene models for Density Functional Theory (DFT) electronic transport calculations of various graphene-helicene-graphene molecular junction configurations on Local Density Approximation (LDA) or Generalized Gradient Approach (GGA) level of exchange and correlation energy with Perdew-Zinger (PZ) or Perdew–Burke–Ernzerhof (PBE) functionals and Grimme DFT-D3 van der Waals correction. The electronic transport properties are studied by using of the Green's functions formalism. The electron transmissions and densities of states of [4]helicene (and [10]helicene) attached as a molecular junction in three different configurations to zigzag graphene nanoribbon (ZGNR) electrodes of three different widths were obtained. The structures are analyzed while relaxed, compressed or stretched. The current-voltage and current-strain characteristics are presented and results are compared and discussed. The work is supported by a short review summarizing some of the recent studies related to graphene, helicene, and molecular junctions under strain.