Computational Chemistry Tools Research Articles

Seamless integration of GEM, a density based-force field, for QM/MM simulations via LICHEM, Psi4, and Tinker-HP

Seamless integration of GEM, a density based-force field, for QM/MM simulations via LICHEM, Psi4, and Tinker-HP

Computational Chemistry: Prediction of Compound Accessibility of Targeted Synthesized Compounds.

In the present work, a series of novel pyridine carboxamides 3(a-h) were synthesized and screened with antibacterial activity. This research explores the application of Density Functional Theory (DFT) in studying biological systems at the quantum mechanical level, particularly in the context of drug design. DFT offers a streamlined approach to quantum mechanical calculations, making it indispensable in various scientific fields, and for its exceptional accuracy, reduced computational time, and cost-effectiveness has become a pivotal tool in computational chemistry. This research work highlights the integration of DFT studies with POM analyses, which effectively identify pharmacophoric sites. Moreover, the research incorporates in silico pharmacokinetics analyses to assess the pharmacokinetic properties of synthesized compounds. The paper focused on a series of compounds previously reported, aiming to provide a comprehensive understanding of their electronic structure, pharmacophoric features, and potential as drug candidates. This study not only contributes to the evolving field of computational chemistry but also holds implications for advancing drug design processes by combining theoretical insights with practical analyses. The compounds 3(a-h) were subjected to Density Functional Theory (DFT) computations using the B3LYP/6-31G(d) basis set to get optimized geometric structures. GaussViewis used to display the contributions of the highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO). The determination of energy gaps was conducted using Gaussian 09W. The pharmacokinetic profiles were evaluated using existing techniques such as Osiris, Petra, and Molinspiration, as well as a novel platform called POM Analyse. The computational studies DFT, POM and in silico pharmacokinetics studies revealed that the studied compounds are biologically active, non-toxic, non-carcinogenic in nature and may be utilized as drug candidates. Density functional theory (DFT) investigations emphasize the exceptional stability of complex 3d, which possesses the biggest energy gap and the lowest softness. In contrast, compound 3h demonstrates poorer stability among the tested compounds, characterized by the lowest energy gap and the highest softness values. These findings are further substantiated by absolute energy calculations. The negligible energy difference in compound 3h indicates an increased transfer of electric charge within the molecule, which is associated with its enhanced biological effectiveness. The drug-likeness of the compounds is confirmed by POM and in silico pharmacokinetics investigations, with compound 3h being identified as the most biologically active among the investigated compounds.

MLatom 3: A Platform for Machine Learning-Enhanced Computational Chemistry Simulations and Workflows.

Machine learning (ML) is increasingly becoming a common tool in computational chemistry. At the same time, the rapid development of ML methods requires a flexible software framework for designing custom workflows. MLatom 3 is a program package designed to leverage the power of ML to enhance typical computational chemistry simulations and to create complex workflows. This open-source package provides plenty of choice to the users who can run simulations with the command-line options, input files, or with scripts using MLatom as a Python package, both on their computers and on the online XACS cloud computing service at XACScloud.com. Computational chemists can calculate energies and thermochemical properties, optimize geometries, run molecular and quantum dynamics, and simulate (ro)vibrational, one-photon UV/vis absorption, and two-photon absorption spectra with ML, quantum mechanical, and combined models. The users can choose from an extensive library of methods containing pretrained ML models and quantum mechanical approximations such as AIQM1 approaching coupled-cluster accuracy. The developers can build their own models using various ML algorithms. The great flexibility of MLatom is largely due to the extensive use of the interfaces to many state-of-the-art software packages and libraries.

Predicting success in Cu-catalyzed C-N coupling reactions using data science.

Data science is assuming a pivotal role in guiding reaction optimization and streamlining experimental workloads in the evolving landscape of synthetic chemistry. A discipline-wide goal is the development of workflows that integrate computational chemistry and data science tools with high-throughput experimentation as it provides experimentalists the ability to maximize success in expensive synthetic campaigns. Here, we report an end-to-end data-driven process to effectively predict how structural features of coupling partners and ligands affect Cu-catalyzed C-N coupling reactions. The established workflow underscores the limitations posed by substrates and ligands while also providing a systematic ligand prediction tool that uses probability to assess when a ligand will be successful. This platform is strategically designed to confront the intrinsic unpredictability frequently encountered in synthetic reaction deployment.

Interaction mechanism of water-soluble inorganic arsenic onto pristine nanoplastics

Nanoplastics (NPLs) persist in aquatic habitats, leading to incremental research on their interaction mechanisms with metalloids in the environment. In this regard, it is known that plastic debris can reduce the number of water-soluble arsenicals in contaminated environments. Here, the arsenic interaction mechanism with pure NPLs, such as polyethylene terephthalate (PET), aliphatic polyamide (PA), polyvinyl chloride (PVC), polyethylene (PE), polypropylene (PP), and polystyrene (PS) is evaluated using computational chemistry tools. Our results show that arsenic forms stable monolayers on NPLs through surface adsorption, with adsorption energies of 9–24 kcal/mol comparable to those on minerals and composite materials. NPLs exhibit varying affinity towards arsenic based on their composition, with As(V) adsorption showing higher stability than As(III). The adsorption mechanism results from a balance between electrostatics and dispersion forces (physisorption), with an average combined contribution of 87%. PA, PET, PVC, and PS maximize the electrostatic effects over dispersion forces, while PE and PP maximize the dispersion forces over electrostatic effects. The electrostatic contribution is attributed to hydrogen bonding and the activation of terminal O–C, C–H, and C–Cl groups of NPLs, resulting in several pairwise interactions with arsenic. Moreover, NPLs polarity enables high mobility in aqueous environments and fast mass transfer. Upon adsorption, As(III) keeps the NPLs polarity, while As(V) limits subsequent uptake but ensures high mobility in water. The solvation process is destabilizing, and the higher the NPL polarity, the higher the solvation energy penalty. Finally, the mechanistic understanding explains how temperature, pressure, pH, salinity, and aging affect arsenic adsorption. This study provides reliable quantitative data for sorption and kinetic experiments on plastic pollution and enhances our understanding of interactions between water contaminants.

AI in computational chemistry through the lens of a decade-long journey.

This article gives a perspective on the progress of AI tools in computational chemistry through the lens of the author's decade-long contributions put in the wider context of the trends in this rapidly expanding field. This progress over the last decade is tremendous: while a decade ago we had a glimpse of what was to come through many proof-of-concept studies, now we witness the emergence of many AI-based computational chemistry tools that are mature enough to make faster and more accurate simulations increasingly routine. Such simulations in turn allow us to validate and even revise experimental results, deepen our understanding of the physicochemical processes in nature, and design better materials, devices, and drugs. The rapid introduction of powerful AI tools gives rise to unique challenges and opportunities that are discussed in this article too.

THEORETICAL STUDY OF MOLECULES DERIVED FROM BETA‑LACTAMS COMPOUNDS WITH POSSIBLE POTENTIAL ANTIBIOTIC ACTIVITY ON Staphylococcus aureus

Staphylococcus aureus is known worldwide as the principal cause of human bacterial infections; as well as methicillin-resistant Staphylococcus aureus (MRSA), is a major cause of hospital-acquired infections that are becoming increasingly difficult to combat because of emerging resistance to all current antibiotic classes. This work aims to carry out a theoretical study of molecules derived from beta-lactam compounds with possible potential antibiotic activity on Staphylococcus aureus. Therefore, computational chemistry tools were used to describe the interactions between potentially active molecules and the transpeptidase and beta-lactamase enzyme. These molecules were docked with transpeptidase and beta-lactamase enzymes, and all of them showed a higher affinity for transpeptidase (–4.81 kcal mol-1) than the reference molecule (methicillin), but only two showed a lower affinity for beta-lactamase (–6.8 kcal mol-1). The two ligands that presented the best ranking docking results were subjected to molecular dynamics simulations to assess conformational stability through root mean square deviation (RMSD) and root mean square fluctuation (RMSF) analysis. According to simulation, the ligands that bind to the proteins do not cause significant conformational changes in the structures of the proteins. The absorption, distribution, metabolism, and excretion (ADME) properties of the molecules with the lowest affinity for beta-lactamase were estimated. The ADME properties predict that the molecules would have good bioavailability.

Lutetium coordination compounds toward potential nanoradiopharmaceutical applications: Rational design and synthesis

Radiolabelled nanoparticles (nanoradiopharmaceuticals) merge the advantages of radiopharmaceuticals and nanoparticles. Among them, polymeric nanoparticles (PNp) can be designed and prepared with architecture, surface charge and functionalization according to applications. Lu-177 is a radionuclide widely used for the preparation of nanoradiopharmaceuticals due to its theranostic properties. PNp are loaded with a therapeutic agent while 177Lu is usually indirectly bonded to PNp-surface, mostly through bifunctional chelating agents such as DOTA and its derivatives. However, the use of these derivatives typically does not yield a high specific activity of the radiolabelled-PNp. A strategy to increase the specific activity of these nanosystems is implementing direct radiolabelling methodologies which bind 177Lu into the PNp surface. The aim of this study was to design and propose, aided by computational chemistry tools, a labelling strategy for PNp systems. Molecules selected for complexing Lu were bidentate and tridentate ligands. Such ligands together should close the 177Lu coordination sphere to form Lu-complexes with near Lu-DOTA stability constant. The combinations of ligands (glycine, diethylenetriamine, lactic acid, glycolic acid, ascorbic acid, poly(lactic-co-glycolic acid) (PLGA) and poly(L-γ-glutamic acid) (γ-PGA) have the desired characteristics and also are frequently used in the preparation of PNp, another feature sought in the ligand selection. The potential formation of Lu-complex using the aforementioned compounds were simulated (with non-radioactive atom) under standard conditions and in aqueous media at different temperatures. Four of the simulated complexes were prepared in acid media (also with a non-radioactive atom) and their potentiometric and spectrophotometric responses were followed during titration with NaOH. Experimental results showed the formation of 3 different complexes with probable coordination number 8, one of which shows possibilities as a potential candidate to assemble 177Lu-PNp.

Molecular toxicity of nitrobenzene derivatives to tetrahymena pyriformis based on SMILES descriptors using Monte Carlo, docking, and MD simulations

It is challenging to model the toxicity of nitroaromatic compounds due to limited experimental data. Nitrobenzene derivatives are commonly used in industry and can lead to environmental contamination. Extensive research, including several QSPR studies, has been conducted to understand their toxicity. Predictive QSPR models can help improve chemical safety, but their limitations must be considered, and the molecular factors affecting toxicity should be carefully investigated. The latest QSPR methods, molecular modeling techniques, machine learning algorithms, and computational chemistry tools are essential for developing accurate and robust models. In this work, we used these methods to study a series of fifty compounds derived from nitrobenzene. The Monte Carlo approach was used for QSPR modeling by applying the SMILES molecular structure representation and optimal molecular descriptors. The correlation ideality index (CII) and correlation contradiction index (CCI) were further introduced as validation parameters to estimate the developed models' predictive ability. The statistical quality of the CII models was better than those without CII. The best QSPR model with the following statistical parameters (Split-3): (R2 = 0.968, CCC = 0.984, IIC = 0.861, CII = 0.979, Q2 = 0.954, QF12 = 0.946, QF22 = 0.938, QF32 = 0.947, Rm2 = 0.878, RMSE = 0.187, MAE = 0.151, FTraining = 390, FInvisible = 218, FCalibration = 240, RTest2 = 0.905) was selected to generate the studied promoters with increasing and decreasing activity.

Accurate Computation of Thermodynamic Activation Parameters in the Chorismate Mutase Reaction from Empirical Valence Bond Simulations.

Chorismate mutase (CM) enzymes have long served as model systems for benchmarking new methods and tools in computational chemistry. Despite the enzymes' prominence in the literature, the extent of the roles that activation enthalpy and entropy play in catalyzing the conversion of chorismate to prephenate is still subject to debate. Knowledge of these parameters is a key piece in fully understanding the mechanism of chorismate mutases. Within this study, we utilize EVB/MD free energy perturbation calculations at a range of temperatures, allowing us to extract activation enthalpies and entropies from an Arrhenius plot of activation free energies of the reaction catalyzed by a monofunctional Bacillus subtilis CM and the promiscuous enzyme isochorismate pyruvate lyase of Pseudomonas aeruginosa. In comparison to the uncatalyzed reaction, our results show that both enzyme-catalyzed reactions exhibit a substantial reduction in activation enthalpy, while the effect on activation entropy is relatively minor, demonstrating that enzyme-catalyzed CM reactions are enthalpically driven. Furthermore, we observe that the monofunctional CM from B. subtilis more efficiently catalyzes this reaction than its promiscuous counterpart. This is supported by a structural analysis of the reaction pathway at the transition state, from which we identified key residues explaining the enthalpically driven nature of the reactions and also the difference in efficiencies between the two enzymes.

Computational Studies for Development of Triazole‐Pyrimidines as Inhibitor of α‐Tubulin Receptor

AbstractMicrotubules are among the critical targets for chemotherapeutic agents to fight against cancer because of their crucial role in mitosis. Microtubule inhibitors block cell cycle progression and result in apoptosis, they can frequently disrupt microtubule/tubulin dynamics by binding to the α‐tubulin receptor. To this end, we have developed compounds against α‐tubulin receptor using several tools of computational chemistry: 3D‐QSAR, Molecular Docking, Molecular Dynamics Simulation, and ADMET prediction. The significant findings incorporated two reliable models: CoMFA (Q2=0.75; R2=0.89) and CoMSIA (Q2=0.57; R2=0.78). Molecular Docking was generated and validated using Molecular Dynamics simulation to investigate the binding mode between the most active compound and the α‐tubulin receptor. The results of the MD simulation demonstrated stability over a 100 ns period. Four compounds (C1, C2, C3, and C4) were developed based on the contour maps, and their activities were predicted. Additionally, Molecular Docking was executed to elucidate the interaction and affinity between the novel compounds and the α‐tubulin receptor. C1 exhibited high affinity and essential interactions and was subjected to MD simulation studies. The results of the MD simulation revealed that the modifications made to enhance the anticancer activity did not impact stability. Finally, ADMET analysis was utilized to examine drug‐likeness.

A Review on Molecular Docking on HDAC Isoforms: Novel Tool for Designing Selective Inhibitors.

Research into histone deacetylases (HDACs) has experienced a remarkable surge in recent years. These enzymes are key regulators of several fundamental biological processes, often associated with severe and potentially fatal diseases. Inhibition of their activity represents a promising therapeutic approach and a prospective strategy for the development of new therapeutic agents. A critical aspect of their inhibition is to achieve selectivity in terms of enzyme isoforms, which is essential to improve treatment efficacy while reducing undesirable pleiotropic effects. The development of computational chemistry tools, particularly molecular docking, is greatly enhancing the precision of designing molecules with inherent potential for specific activity. Therefore, it was considered necessary to review the molecular docking studies conducted on the major isozymes of the enzyme in order to identify the specific interactions associated with each selective HDAC inhibitor. In particular, the most critical isozymes of HDAC (1, 2, 3, 6, and 8) have been thoroughly investigated within the scope of this review.

Comparison of Density-Functional Theory Dispersion Corrections for the DES15K Database.

While density-functional theory (DFT) remains one of the most widely used tools in computational chemistry, most functionals fail to properly account for the effects of London dispersion. Hence, there are many popular post-self-consistent methods to add a dispersion correction to the DFT energy. Until now, the most popular methods have never been compared on equal footing due to not being implemented in the same electronic structure packages. In this work, we performed a large-scale benchmarking study, directly comparing the accuracy of the exchange-hole dipole moment (XDM), D3BJ, D4, TS, MBD, and MBD-NL dispersion models when applied to the recent DES15K database of nearly 15,000 molecular complexes at both expanded and compressed geometries. Our study showed similarly good performance for all dispersion methods (except TS) when applied to neutral complexes. However, they all performed worse for ionic complexes, particularly those involving dications of alkaline earth metals, due to systematic overbinding by the base PBE0 density functional. Investigation of the largest outliers also revealed that only the MBD and MBD-NL methods demonstrate surprising errors for complexes involving alkali metal cations at compressed geometries where they tended to significantly overbind. As we would expect minimal dispersion binding for such complexes, we further investigated the origins of these errors for the potential energy curve of a model cation-π complex. Overall, there is little choice between the XDM, D3BJ, D4, MBD, and MBD-NL dispersion methods for most systems. However, the MBD-based methods are not recommended for complexes involving organic species and alkali or alkaline earth metal cations, for example when modeling Li+ intercalation into graphite.

Machine learning meets quantum mechanics in catalysis

Over the past decade many researchers have applied machine learning algorithms with computational chemistry and materials science tools to explore properties of catalysts. There is a rapid increase in publications demonstrating the use of machine learning for rational catalyst design. In our perspective, targeted tools for rational catalyst design will continue to make significant contributions. However, the community should focus on developing high-throughput simulation tools that utilize molecular dynamics capabilities for thorough exploration of the complex potential energy surfaces that exist, particularly in heterogeneous catalysis. Catalyst-specific databases should be developed to contain enough data to represent the complex multi-dimensional space that defines structure-function relationships. Machine learning tools will continue to impact rational catalyst design; however, we believe that more sophisticated pattern recognition algorithms would yield better understanding of structure-function relationships for heterogeneous catalysis.

Theoretical analysis of OLED performances of some aromatic nitrogen-containing ligands.

It is well-known that tris(8-hydroxyquinoline) aluminum (Alq3) complex and N,N'diphenyl-N,N'-bis(3-methylphenyl)-1,1'-diphenyl-4,4'-diamine compound (TPD) are widely used as electron transfer material (ETL) and hole transfer material (HTL) in organic light emitting diode (OLED) structure, respectively. Considering the reference materials, in the present work, the OLED performances of some cyclic aromatic structures such as 4,4'azopyridine [AZPY], 4,4'-bipyridine [BIPY], 1,2-bis[4'-(4-methylphenyl)2,2':6'2″-terpyridin6-yl]ethyne (BISTERPY), 5,5'-diamino-2,2'-bipyridine (DABP), dipyrido[3,2-a:2',3'c]phenazine (DPP), 4,7-phenanthroline (PHEN) including nitrogen atom have been theoretically analyzed. It is important to note that B3LYP/6-31G(d) and B3LYP/TZP levels of the theory were taken into account for the calculations about monomeric and dimeric structures, respectively. Additionally, the calculations of the mentioned monomeric form were performed at B3LYP-D3/6-31G, CAM-B3LYP/6-31G and ωB97X-D/6-31G(d) levels. For a detailed theoretical analysis, the reorganization energies (λe and λh), adiabatic and vertical ionization potentials and electron affinities, the effective transfer integrals (Ve and Vh), and the charge transfer rates (We and Wh) of all compounds were computed by means of computational chemistry tools. In the light of calculated parameters, it is determined that these mentioned aromatic cyclic structures will be used in which layers of OLED structure. The results obtained in this study will be helpful in the design and applications of new molecules as OLED materials in the future.

Organic light‐emitting diode behaviors of some synthesized platinum(II)‐based complexes

AbstractOrganic light‐emitting light diodes (OLEDs) have been increasingly used in displays, replacing liquid‐crystal displays (LCD) and light‐emitting diodes (LEDs) panels. The increase in commercial use of OLED has led to the search for OLED with high performance. For that reason, the OLED properties of monomers and dimers of some synthesized platinum(II)‐based complexes were estimated by using different computational chemistry tools with different codes. The electron/hole reorganization energies, the adiabatic/vertical ionization potentials, the adiabatic/vertical electron affinities, the chemical hardness values, the dipole moments, the frontier orbital shapes/energy levels, the energy gaps, the emission wavelengths, spin‐orbit matrix elements, the rates of reverse intersystem crossing and intersystem crossing of the investigated complexes were determined. From the theoretically obtained data, it was found that Pt(hppz)2 and Pt(fppz)2 complexes can be used as electron transfer material. Furthermore, it was stated that Pt(f2bipz)(bpy) is both electron‐blocking layer and hole blocking layer materials. Moreover, it was noted that that PtOEP complex can be utilized as a good electron injection layer and hole injection layer material. Addition to these, it was emphasized that that Pt(f2bipz)(bpy) can be considered as a good candidate for near infrared organic light emitting diodes and thermal activated delayed fluorescent organic light emitting diodes. In light of computational chemistry, it should be expected that the study will provide a great contribution to studies related to organic light emitting diodes.

Chemical Reactivities and Organic Light-emitting Diode Properties of some Polyaromatic Molecules

High-performance organic-light emitting diode (OLED) display panels have been very popular lately due to their many advantages compared to liquid-crystal display (LCD) and light-emitting diode (LED) panels. It is also well known that the materials used in OLED panels are important in determining OLED performance. Starting with the selection of materials which have rich π-electrons will be a good start for the design of high-performance OLED materials. For this aim, the OLED properties of some cyclic aromatic structures with rich π-electrons such as 2,2ꞌ-bi-1,6-naphthyridine (BNP), 1,6-bis(4ꞌ-pyridine)-2,5-diazahexane (BPDH), 3,3ꞌ-bis[3-(2-pyridyl)pyrazol-1-yl]biphenyl (BPPB), 5,5ꞌ-dicyano-2,2ꞌ-bipyridine (DCBP), 2,2ꞌ-dimethyl-4,4ꞌ-bipyrimidine (DMBP), and 4ꞌ-phenyl-2,2ꞌ:6ꞌ2ꞌꞌterpyridine (Ph-TERPY) were theoretically analyzed using computational chemistry tools. The calculations of monomeric and dimeric structures of mentioned molecules were carried out at B3LYP/6-31G(d) and B3LYP/TZP levels, respectively. The OLED properties of the investigated compound were explained by means of OLED parameters such as the reorganization energies, adiabatic-vertical ionization potentials and adiabatic-vertical electron affinities, the effective transfer integrals, and the charge transfer ratios. In the light of computational chemistry, it is indicated that these studied molecules will be utilized in which layers of OLED device. In addition to OLED analysis, in the light of the calculated reactivity descriptors, the chemical reactivities of the studied molecules were discussed.

Interaction mechanism of triclosan on pristine microplastics

Urban wastewaters comprise different hydrophobic pollutants such as microplastics (MPs), pharmaceuticals, and personal care products. Among these pollutants, triclosan (TCS) shows a worrying interaction ability with MPs; recent studies show MPs serve as a vector between TCS and aquatic environments, whose interaction is still being studied to understand their combined toxicity and transport ability. Using computational chemistry tools, this work evaluates the TCS-MPs interaction mechanism, including pristine polymers, i.e., aliphatic polyamides (PA), polyethylene (PE), polystyrene (PS), polyvinyl chloride (PVC), and polyethylene terephthalate (PET). Our results show that TCS adsorption on MPs solely occurs via physisorption, where PA reaches the higher adsorption ability. Remarkably, MPs reach higher or comparable adsorption stability than carbon-based materials, boron nitrides, and minerals, indicating their worrying transport properties. Also, the adsorption capacity is strongly influenced by entropy changes rather than thermal effects, which determine the different sorption capacities among polymers and agree well with reported sorption capacities from adsorption kinetic experiments in the literature. MPs show a polar and highly susceptible surface to establish electrostatics and dispersion effects on TCS. Accordingly, the TCS-MPs interaction mechanism arises from the interplay between electrostatics and dispersion forces, with a combined contribution of 81–93 %. Specifically, PA and PET maximize the electrostatic effects, while PE, PP, PVC, and PS maximize the dispersion effects. From the chemical viewpoint, TCS-MPs complexes interact by a series of pairwise interactions such as Van der Waals, hydrogen bonding, C-H⋯π, C-H⋯C-H, C-Cl⋯C-H, and C-Cl⋯Cl-C. Finally, the mechanistic information explains the effects of temperature, pressure, aging, pH, and salinity on TCS adsorption. This study quantitatively elucidates the interaction mechanism of TCS-MP systems, which were hard to quantify to date, and explains the TCS-MPs sorption performance for sorption/kinetic studies.

Simulations of Amyloid-Forming Peptides in the Crystal State

There still is little treatment available for amyloid diseases, despite their significant impact on individuals and the social and economic implications for society. One reason for this is that the physical nature of amyloid formation is not understood sufficiently well. Therefore, fundamental research at the molecular level remains necessary to support the development of therapeutics. A few structures of short peptides from amyloid-forming proteins have been determined. These can in principle be used as scaffolds for designing aggregation inhibitors. Attempts to this end have often used the tools of computational chemistry, in particular molecular simulation. However, few simulation studies of these peptides in the crystal state have been presented so far. Hence, to validate the capability of common force fields (AMBER19SB, CHARMM36m, and OPLS-AA/M) to yield insight into the dynamics and structural stability of amyloid peptide aggregates, we have performed molecular dynamics simulations of twelve different peptide crystals at two different temperatures. From the simulations, we evaluate the hydrogen bonding patterns, the isotropic B-factors, the change in energy, the Ramachandran plots, and the unit cell parameters and compare the results with the crystal structures. Most crystals are stable in the simulations but for all force fields there is at least one that deviates from the experimental crystal, suggesting more work is needed on these models.

Theoretical FRET Efficiency of an Antenna Material Containing Natural Dyes and Zeolite L.

We calculated the Förster resonance energy-transfer (FRET) efficiency of a theoretical host-guest composite formed by all-trans β-cryptoxanthin (BCRY), all-trans zeaxanthin (ZEA), and a zeolite-LTL (Linde Type L) nanochannel with the help of computational chemistry tools. Climate change demands urgently the development of novel renewable energies, and in such a context, artificial photosynthesis arises as a promising technology capable of contributing to satisfying humankind's energy needs. All artificial photosynthetic devices need antennas to harvest and transfer energy to a reaction center efficiently. Antenna materials integrated by highly fluorescent synthetic pigments embedded onto the nanochannels of a zeolite-LTL have already been shown experimentally to be very efficient supramolecular assemblies. However, research work computing the efficiency of an antenna made of nonfluorescent natural pigments and a zeolite-LTL nanochannel has not been undertaken yet, at least to our knowledge. Fortunately, natural dyes possess outstanding features to study them dynamically; they are environmentally friendly, inexpensive, ubiquitous, and abundant. Density functional theory (DFT) methods were chiefly employed along with the CAM-B3LYP functional and the 3-21G*/6-311+G(d,p) basis sets. The ONIOM method enabled geometry and energy calculations of dyes inside the zeolite-LTL (ZL) nanochannel. The Förster resonance energy-transfer (FRET) efficiency and the Förster radius of the composite were 40.9% and 24.9 Å, respectively. Theoretical findings suggested that this composite might contribute to diminishing costs and improving the environmental friendliness of an antenna system.