Computational Chemistry Tools Research Articles

Searching for New Antibacterial Compounds Against Staphylococcus aureus: A Computational Study on the Binding Between FtsZ and FtsA

Background: Staphylococcus aureus is a pathogen that has become resistant to different antibiotics, which makes it a threat to human health. Although the first penicillin-resistant strain appeared in 1945, nowadays, there are just a few alternatives to fight it. To circumvent this issue, novel approaches to develop drugs to target proteins of the bacteria cytoskeleton, essential for bacteria’s binary fission, are being developed. FtsZ and FtsA are two proteins that are key for the initial stages of binary fission. On one side, FtsZ forms a polymeric circular structure called the Z ring; meanwhile, FtsA binds to the cell membrane and then anchors to the Z ring. According to the literature, this interaction occurs within the C-terminus domain of FtsZ, which is mainly disordered. Objective: In this work, we studied the binding of FtsZ to FtsA using computational chemistry tools to identify the interactions between the two proteins to further use this information for the search of potential protein-protein binding inhibitors (PPBIs). Methods: We made a bioinformatic analysis to obtain a representative sequence of FtsZ and FtsA of Staphylococcus aureus. With this information, we built homology models of the FtsZ to carry out the molecular docking with the FtsA. Furthermore, alanine scanning was conducted to identify the key residues forming the FtsZ–FtsA complex. Finally, we used this information to generate a pharmacophore model to carry out a virtual screening approach. Results: We identified the key residues forming the FtsZ-FtsA complex as well as five molecules with high potential as PPBIs.

Discovering Photoswitchable Molecules for Drug Delivery with Large Language Models and Chemist Instruction Training.

Background: As large language models continue to expand in size and diversity, their substantial potential and the relevance of their applications are increasingly being acknowledged. The rapid advancement of these models also holds profound implications for the long-term design of stimulus-responsive materials used in drug delivery. Methods: The large model used Hugging Face's Transformers package with BigBird, Gemma, and GPT NeoX architectures. Pre-training used the PubChem dataset, and fine-tuning used QM7b. Chemist instruction training was based on Direct Preference Optimization. Drug Likeness, Synthetic Accessibility, and PageRank Scores were used to filter molecules. All computational chemistry simulations were performed using ORCA and Time-Dependent Density-Functional Theory. Results: To optimize large models for extensive dataset processing and comprehensive learning akin to a chemist's intuition, the integration of deeper chemical insights is imperative. Our study initially compared the performance of BigBird, Gemma, GPT NeoX, and others, specifically focusing on the design of photoresponsive drug delivery molecules. We gathered excitation energy data through computational chemistry tools and further investigated light-driven isomerization reactions as a critical mechanism in drug delivery. Additionally, we explored the effectiveness of incorporating human feedback into reinforcement learning to imbue large models with chemical intuition, enhancing their understanding of relationships involving -N=N- groups in the photoisomerization transitions of photoresponsive molecules. Conclusions: We implemented an efficient design process based on structural knowledge and data, driven by large language model technology, to obtain a candidate dataset of specific photoswitchable molecules. However, the lack of specialized domain datasets remains a challenge for maximizing model performance.

Ultrasound‐Assisted Catalysis: A Pathway to Novel and Selective Chemical Transformations in Condensed Phase

AbstractIn this paper, we review the concept of synergistically using ultrasonic sonochemistry in combination with heterogeneous catalysis, to selectively perform condensed phase chemical transformations. Through specific examples, we highlight that ultrasound induced cavitation bubbles not only provide local high temperature and pressure conditions for initiating chemical reactions but also improves heat, mass and charge transfer characteristics in the system. We also show that sonocatalysis can alter reaction mechanisms and open new activation and reaction pathways that are not intuitive to thermocatalysis. Computational chemistry tools are introduced and are combined with sonocatalysis experimental studies to demonstrate that, in‐situ generated radicals propelled into the bulk liquid solution upon bubbles implosion can be stabilized by catalyst surfaces, while creating active sites for unique reactions during heterogeneous sonocatalytic chemistry. We also report recent examples of condensed phase sonocatalysis applications, including pollutant degradation, chemical and pharmaceutical synthesis and biomass upgrading, and provide mechanistic insights into the process.

Hydrogen bonding patterns and cooperativity in polyproline II helical bundles

Hydrogen bond cooperativity (HBC) plays an important role in stabilizing protein assemblies built by α-helices and β-sheets, the most common secondary structures. However, whether HBC exists in other types of protein secondary structures such as polyproline II (PPII) helices remains unexplored. This is intriguing, since PPII systems as assembling blocks are continuously emerging across multiple fields. Here, using a combination of computational chemistry tools and molecular modeling corroborated by experimental observables, we characterize the distinct H-bonding patterns present in PPII helical bundles and establish that HBC stabilizes intermolecular PPII helices as seen in other protein assemblies such as amyloid fibrils. In addition to cooperative interactions in canonical CO···HN H-bonds, we show that analogous interactions in non-canonical CO···HαCα H-bonds are relevant in Gly-rich PPII bundles, thus compensating for the inability of glycine residues to create hydrophobic cores. Our results provide a mechanistic explanation for the assembly of these bundles.

Group Theoretic Approach towards the Balaban Index of Catacondensed Benzenoid Systems and Linear Chain of Anthracene

In this work, we present the analytical closed forms of the Balaban index for anthracene and catacondensed benzenoid systems using group theoretic techniques. The Balaban index is a distance-based topological index that provides valuable information about the properties of chemical structures. We emphasize the importance of determining analytical closed forms of the Balaban index for catacondensed benzenoid systems and linear chains of anthracene, as it enables a deeper understanding of these systems and their behavior. Our analysis utilizes the group action of the automorphism group of these chains on the set of vertices, which refer to the points where the chains intersect. In future work, we plan to determine the Balaban index of other polymeric linear chains using group theoretic techniques and extend the applications of this index to other fields, such as materials science and biology. It is clear that the Balaban index remains a valuable tool in theoretical and computational chemistry, and its applications are constantly evolving.

Tuning the Emission of Bis-ethylenedioxythiophene-thiophenes upon Aggregation.

The ability of small lipophilic molecules to penetrate the blood-brain barrier through transmembrane diffusion has enabled researchers to explore new diagnostics and therapies for brain disorders. Until now, therapies targeting the brain have mainly relied on biochemical mechanisms, while electrical treatments such as deep brain stimulation often require invasive procedures. An alternative to implanting deep brain stimulation probes could involve administering small molecule precursors intravenously, capable of crossing the blood-brain barrier, and initiating the formation of conductive polymer networks in the brain through in vivo polymerization. This study examines the aggregation behavior of five water-soluble conducting polymer precursors sharing the same conjugate core but differing in side chains, using spectroscopy and various computational chemistry tools. Our findings highlight the significant impact of side chain composition on both aggregation and spectroscopic response.

QSAR modeling for cytotoxicity of sulfur-containing Shikonin oxime derivatives targeting HCT-15, MGC-803, BEL-7402, and MCF-7 cell lines

Targeting cancer cells through drug-based treatment or combination therapy protocols involving chemical compounds can be challenging due to multiple factors, including their resistance to bioactive compounds and the potential of drugs to damage healthy cells. This study aims to investigate the relationship between the structure of novel sulfur-containing shikonin oxime compounds and the corresponding cytotoxicity against four cancer types, namely colon, gastric, liver, and breast cancers, through computational chemistry tools. This investigation is suggested to help build insights into how the structure of the compounds influences their activity and understand the mechanisms behind it and subsequently might be used in multi-cancer drug design process to propose novel optimized compounds that potentially exhibit the desired activity. The findings showed that the cytotoxic activity against the four cancer types was accurately predictable (R2 > 0.7, NRMSE <20%) by a combination of search and machine learning algorithms, based on the information on the structure of the compounds, including their lipophilicity, surface area, and volume. Overall, this study is supposed to play a crucial role in effective multi-cancer drug design in cancer research areas.

Studying the Intrinsic Reactivity of Chromanes by Gas-Phase Infrared Spectroscopy.

Tandem mass spectrometry is routinely used for the structural analysis of organic molecules, but many fragmentation reactions are not well understood. Because several potential structures can correspond to a measured mass, the assignment of product ions is ambiguous using mass spectrometry alone. Here, we combine mass spectrometry with high-resolution gas-phase infrared spectroscopy and computational chemistry tools to identify product ion structures and derive collision-induced fragmentation mechanisms of the chromane derivatives Trolox and Methyltrolox. We find that protonated Trolox and Methyltrolox fragment identically via dehydration and decarbonylation, while deprotonated ions display substantially diverging reactivities. For deprotonated Methyltrolox, we observe unusual radical fragmentation reactions and suggest a [1,2]-Wittig rearrangement involving aryl migration in the gas phase. Overall, the combined experimental and theoretical approach presented here revealed complex proton dynamics and intramolecular rearrangement reactions, which expand our understanding on structure-reactivity relationships of isolated molecules in different protonation states.

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

Hybrid quantum mechanics/molecular mechanics (QM/MM) simulations have become an essential tool in computational chemistry, particularly for analyzing complex biological and condensed phase systems. Building on this foundation, our work presents a novel implementation of the Gaussian Electrostatic Model (GEM), a polarizable density-based force field, within the QM/MM framework. This advancement provides seamless integration, enabling efficient and optimized QM/GEM calculations in a single step using the LICHEM Code. We have successfully applied our implementation to water dimers and hexamers, demonstrating the ability to handle water systems with varying numbers of water molecules. Moreover, we have extended the application to describe the double proton transfer of the aspartic acid dimer in a box of water, which highlights the method's proficiency in investigating heterogeneous systems. Our implementation offers the flexibility to perform on-the-fly density fitting or to utilize pre-fitted coefficients to estimate exchange and Coulomb contributions. This flexibility enhances efficiency and accuracy in modeling molecular interactions, especially in systems where polarization effects are significant.

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.

Crystal structure of poly(trimethylene 2,5-furandicarboxylate) redux – a new model supported by computational spectroscopy

The crystal structure of biobased poly(trimethylene 2,5-furandicarboxylate) is revisited following new evidence from vibrational spectroscopy and computational chemistry tools.

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.