MolGC: molecular geometry comparator algorithm for bond length mean absolute error computation on molecules.

Density Functional Theory (DFT) is extensively used in theoretical and computational chemistry to study molecular and crystal properties across diverse fields, including quantum chemistry, materials physics, catalysis, biochemistry, and surface science. Despite advances in DFT hardware and software for optimized geometries, achieving consensus in molecular structure comparisons with experimental counterparts remains a challenge. This difficulty is exacerbated by the lack of automated bond length comparison tools, resulting in labor-intensive and error-prone manual processes. To address these challenges, we propose MolGC, a Molecular Geometry Comparator algorithm that automates the comparison of optimized geometries from different theoretical levels. MolGC calculates the mean absolute error (MAE) of bond lengths by integrating data from various DFT software. It provides interactive and customizable visualization of geometries, enabling users to explore different views for enhanced analysis. In addition, it saves MAE computations for further analysis and offers a comprehensive statistical summary of the results. MolGC effectively addresses complex graph labeling challenges, ensuring accurate identification and categorization of bonds in diverse chemical structures. It achieves a 98.91% average rate in correct bond label assignments on an antibiotics dataset, showcasing its effectiveness for comparing molecular bond lengths across geometries of varying complexity and size. The executable file and software resources for running MolGC can be downloaded from https://github.com/AbimaelGP/MolGC/tree/main .

The 3D pharmacophore modeling to explore new antischistosomal agents among US FDA approved drugs.

Aim: To identify potential antischistosomal agents through 3D pharmacophore-based virtual screening of US FDA approved drugs.Materials & methods: A comprehensive virtual screening was conducted on a dataset of 10,000 FDA approved drugs, employing praziquantel as a template. Promising candidates were selected and assessed for their impact on Schistosoma mansoni viability in vitro and in vivo using S. mansoni infected mice.Results & conclusion: Among the selected drugs, betamethasone and doxazosin demonstrated in vitro efficacy, with effective concentration 50% (EC50) values ranging from 35 to 60 µM. In vivo studies revealed significant (>50%) reductions in worm burden for both drugs. These findings suggest that betamethasone and doxazosin hold promise for repurposing in treating schistosomiasis. Additionally, the study showcases a useful approach for identifying new antischistosomal drugs.

Discovering new treatments for #schistosomiasis is crucial [Formula: see text]. Our study used virtual screening to identify potential antischistosomal drugs from US FDA approved compounds [Formula: see text]. Promising results in vitro and in vivo. [Formula: see text] #drugdiscovery #tropicaldiseases.

Electrospray ionization tandem mass spectrometry of 4-aryl-3,4-dihydrocoumarins.

We have investigated the gas-phase fragmentation reactions of 11 synthetic 4-aryl-3,4-dihydrocoumarins by electrospray ionization tandem mass spectrometry (ESI-MS/MS) on a quadrupole-time-of flight (Q-TOF) hybrid mass spectrometer. We have also estimated thermochemical data for the protonated coumarins (precursor ion A) and product ion structures by computational chemistry at a B3LYP level of theory to establish the ion structures and to rationalize the fragmentation pathways. The most abundant ions in the product ion spectra of coumarins 1-11 resulted from C8H8O2, CO2, C4H4O3, C8H10O3, C8H8O2, and CH3OH eliminations through retro-Diels-Alder (RDA) reactions, remote hydrogen rearrangements (ß-eliminations), and ß-lactone ring contraction. Although the investigated coumarins shared most of the fragmentation pathways, formation of a benzylic product ion and its corresponding tropylium ion was diagnostic of the substituents at ring C. The thermochemical data revealed that the nature and position of the substituents at ring C played a key role in the formation of this product ion and determined its relative intensity in the product ion spectrum. The results of this study contribute to knowledge of the gas-phase ion chemistry of this important class of organic compounds.

Substitution Effects in Aryl Halides and Amides into the Reaction Mechanism of Ullmann-Type Coupling Reactions.

In this article, we present a comprehensive computational investigation into the reaction mechanism of N-arylation of substituted aryl halides through Ullmann-type coupling reactions. Our computational findings, obtained through DFT ωB97X-D/6-311G(d,p) and ωB97X-D/LanL2DZ calculations, reveal a direct relation between the previously reported experimental reaction yields and the activation energy of haloarene activation, which constitutes the rate-limiting step in the overall coupling process. A detailed analysis of the reaction mechanism employing the Activation Strain Model indicates that the strain in the substituted iodoanilines is the primary contributor to the energy barrier, representing an average of 80% of the total strain energy. Additional analysis based on conceptual Density Functional Theory (DFT) suggests that the nucleophilicity of the nitrogen in the lactam is directly linked to the activation energies. These results provide valuable insights into the factors influencing energetic barriers and, consequently, reaction yields. These insights enable the rational modification of reactants to optimize the N-arylation process.

Luminescent Supramolecular Metallogels: Drug Loading and Eu(III) as Structural Probe.

Supramolecular metallogels combine the rheological properties of gels with the color, magnetism, and other properties of metal ions. Lanthanide ions such as Eu(III) can be valuable components of metallogels due to their fascinating luminescence. In this work, we combine Eu(III) and iminodiacetic acid (IDA) into luminescent hydrogels. We investigate the tailoring of the rheological properties of these gels by changes in their metal:ligand ratio. Further, we use the highly sensitive Eu(III) luminescence to obtain information about the chemical structure of the materials. In special, we take advantage of computational calculations to employ an indirect method for structural elucidation, in which the simulated luminescent properties of candidate structures are matched to the experimental data. With this strategy, we can propose molecular structures for different EuIDA gels. We also explore the usage of these gels for the loading of bioactive molecules such as OXA, observing that its aldose reductase activity remains present in the gel. We envision that the findings from this work could inspire the development of luminescent hydrogels with tunable rheology for applications such as 3D printing and imaging-guided drug delivery platforms. Finally, Eu(III) emission-based structural elucidation could be a powerful tool in the characterization of advanced materials.

Enhanced Sampling in Molecular Dynamics Simulations: How Many MD Snapshots can be Needed to Reproduce the Biological Behavior?

Since its early days in the 19th century, medicinal chemistry has concentrated its efforts on the treatment of diseases, using tools from areas such as chemistry, pharmacology, and molecular biology. The understanding of biological mechanisms and signaling pathways is crucial information for the development of potential agents for the treatment of diseases mainly because they are such complex processes. Given the limitations that the experimental approach presents, computational chemistry is a valuable alternative for the study of these systems and their behavior. Thus, classical molecular dynamics, based on Newton's laws, is considered a technique of great accuracy, when appropriated force fields are used, and provides satisfactory contributions to the scientific community. However, as many configurations are generated in a large MD simulation, methods such as Statistical Inefficiency and Optimal Wavelet Signal Compression Algorithm are great tools that can reduce the number of subsequent QM calculations. Accordingly, this review aims to briefly discuss the importance and relevance of medicinal chemistry allied to computational chemistry as well as to present a case study where, through a molecular dynamics simulation of AMPK protein (50 ns) and explicit solvent (TIP3P model), a minimum number of snapshots necessary to describe the oscillation profile of the protein behavior was proposed. For this purpose, the RMSD calculation, together with the sophisticated OWSCA method was used to propose the minimum number of snapshots.

Marine Toxins as Pharmaceutical Treasure Troves: A Focus on Saxitoxin Derivatives from a Computational Point of View.

This work highlights the significant potential of marine toxins, particularly saxitoxin (STX) and its derivatives, in the exploration of novel pharmaceuticals. These toxins, produced by aquatic microorganisms and collected by bivalve mollusks and other filter-feeding organisms, offer a vast reservoir of chemical and biological diversity. They interact with sodium channels in physiological processes, affecting various functions in organisms. Exposure to these toxins can lead to symptoms ranging from tingling sensations to respiratory failure and cardiovascular shock, with STX being one of the most potent. The structural diversity of STX derivatives, categorized into carbamate, N-sulfocarbamoyl, decarbamoyl, and deoxydecarbamoyl toxins, offers potential for drug development. The research described in this work aimed to computationally characterize 18 STX derivatives, exploring their reactivity properties within marine sponges using conceptual density functional theory (CDFT) techniques. Additionally, their pharmacokinetic properties, bioavailability, and drug-likeness scores were assessed. The outcomes of this research were the chemical reactivity parameters calculated via CDFT as well as the estimated pharmacokinetic and ADME properties derived using computational tools. While they may not align directly, the integration of these distinct datasets enriches our comprehensive understanding of the compound's properties and potential applications. Thus, this study holds promise for uncovering new pharmaceutical candidates from the considered marine toxins.

Theoretical study of the interaction between the antibiotic linezolid and the active site of the 50S ribosomal subunit of the bacterium Haloarcula marismortui.

First antibiotic in the oxazolidinone class, linezolid fights gram-positive multiresistant bacteria by inhibiting protein synthesis through its interaction with the 50S subunit of the functional bacterial ribosome. For its antimicrobial action, it is necessary that its chiral carbon located in the oxazolidinone ring is in the S-conformation. Computational calculation at time-dependent density functional theory methodology, ultraviolet-visible (UV-Vis), and electronic circular dichroism spectra was obtained for noncomplexed and complexed forms of linezolid to verify the possible chirality of nitrogen atom in the acetamide group of the molecule. The molecular system has two chiral centers. So, there are now four possible configurations: RR, RS, SR, and SS. For a better understanding of the system, the electronic spectra at the PBE0/6-311++G(3df,2p) level of theory were obtained. The complexed form was obtained from the crystallographic data of the ribosome, containing the S-linezolid molecular system. The computational results obtained for the electronic properties are in good agreement with the experimental crystallographic data and available theoretical results.

MD Simulations to Calculate NMR Relaxation Parameters of Vanadium(IV) Complexes: A Promising Diagnostic Tool for Cancer and Alzheimer's Disease.

Early phase diagnosis of human diseases has still been a challenge in the medicinal field, and one of the efficient non-invasive techniques that is vastly used for this purpose is magnetic resonance imaging (MRI). MRI is able to detect a wide range of diseases and conditions, including nervous system disorders and cancer, and uses the principles of NMR relaxation to generate detailed internal images of the body. For such investigation, different metal complexes have been studied as potential MRI contrast agents. With this in mind, this work aims to investigate two systems containing the vanadium complexes [VO(metf)2]·H2O (VC1) and [VO(bpy)2Cl]+ (VC2), being metformin and bipyridine ligands of the respective complexes, with the biological targets AMPK and ULK1. These biomolecules are involved in the progression of Alzheimer's disease and triple-negative breast cancer, respectively, and may act as promising spectroscopic probes for detection of these diseases. To initially evaluate the behavior of the studied ligands within the aforementioned protein active sites and aqueous environment, four classical molecular dynamics (MD) simulations including VC1 + H2O (1), VC2 + H2O (2), VC1 + AMPK + H2O (3), and VC2 + ULK1 + H2O (4) were performed. From this, it was obtained that for both systems containing VCs and water only, the theoretical calculations implied a higher efficiency when compared with DOTAREM, a famous commercially available contrast agent for MRI. This result is maintained when evaluating the system containing VC1 + AMPK + H2O. Nevertheless, for the system VC2 + ULK1 + H2O, there was observed a decrease in the vanadium complex efficiency due to the presence of a relevant steric hindrance. Despite that, due to the nature of the interaction between VC2 and ULK1, and the nature of its ligands, the study gives an insight that some modifications on VC2 structure might improve its efficiency as an MRI probe.

Exploring marine toxins: comparative analysis of chemical reactivity properties and potential for drug discovery.

Marine toxins, produced by various marine microorganisms, pose significant risks to both marine ecosystems and human health. Understanding their diverse structures and properties is crucial for effective mitigation and exploration of their potential as therapeutic agents. This study presents a comparative analysis of two hydrophilic and two lipophilic marine toxins, examining their reactivity properties and bioavailability scores. By investigating similarities among these structurally diverse toxins, valuable insights into their potential as precursors for novel drug development can be gained. The exploration of lipophilic and hydrophilic properties in drug design is essential due to their distinct implications on drug distribution, elimination, and target interaction. By elucidating shared molecular properties among toxins, this research aims to identify patterns and trends that may guide future drug discovery efforts and contribute to the field of molecular toxinology. The findings from this study have the potential to expand knowledge on toxins, facilitate a deeper understanding of their bioactivities, and unlock new therapeutic possibilities to address unmet biomedical needs. The results showcased similarities among the studied systems, while also highlighting the exceptional attributes of Domoic Acid (DA) in terms of its interaction capabilities and stability.

Artificial intelligence-guided approach for efficient virtual screening of hits against Schistosoma mansoni.

Background: The impact of schistosomiasis, which affects over 230 million people, emphasizes the urgency of developing new antischistosomal drugs. Artificial intelligence is vital in accelerating the drug discovery process. Methodology & results: We developed classification and regression machine learning models to predict the schistosomicidal activity of compounds not experimentally tested. The prioritized compounds were tested on schistosomula and adult stages of Schistosoma mansoni. Four compounds demonstrated significant activity against schistosomula, with 50% effective concentration values ranging from 9.8 to 32.5 µM, while exhibiting no toxicity in animal and human cell lines. Conclusion: These findings represent a significant step forward in the discovery of antischistosomal drugs. Further optimization of these active compounds can pave the way for their progression into preclinical studies.

The pseudotorsional space of RNA.

The characterization of the conformational landscape of the RNA backbone is rather complex due to the ability of RNA to assume a large variety of conformations. These backbone conformations can be depicted by pseudotorsional angles linking RNA backbone atoms, from which Ramachandran-like plots can be built. We explore here different definitions of these pseudotorsional angles, finding that the most accurate ones are the traditional η (eta) and θ (theta) angles, which represent the relative position of RNA backbone atoms P and C4'. We explore the distribution of η - θ in known experimental structures, comparing the pseudotorsional space generated with structures determined exclusively by one experimental technique. We found that the complete picture only appears when combining data from different sources. The maps provide a quite comprehensive representation of the RNA accessible space, which can be used in RNA-structural predictions. Finally, our results highlight that protein interactions lead to significant changes in the population of the η - θ space, pointing toward the role of induced-fit mechanisms in protein-RNA recognition.

Computational Discovery of Marine Molecules of the Cyclopeptide Family with Therapeutic Potential.

Stellatolides are natural compounds that have shown promising biological activities, including antitumor, antimicrobial, and anti-inflammatory properties, making them potential candidates for drug development. Chemical Reactivity Theory (CRT) is a branch of chemistry that explains and predicts the behavior of chemical reactions based on the electronic structure of molecules. Conceptual Density Functional Theory (CDFT) and Computational Peptidology (CP) are computational approaches used to study the behavior of atoms, molecules, and peptides. In this study, we present the results of our investigation of the chemical reactivity and ADMET properties of Stellatolides A-H using a novel computational approach called Conceptual DFT-based Computational Peptidology (CDFT-CP). Our study uses CDFT and CP to predict the reactivity and stability of molecules and to understand the behavior of peptides at the molecular level. We also predict the ADMET properties of the Stellatolides A-H to provide insight into their effectiveness, potential side effects, and optimal dosage and route of administration, as well as their biological targets. This study sheds light on the potential of Stellatolides A-H as promising candidates for drug development and highlights the potential of CDFT-CP for the study of other natural compounds and peptides.

Parts per Million of Propanol and Arsine as Responsible for the Poisoning of the Propylene Polymerization Reaction.

Polypropylene synthesis is a critical process in the plastics industry, where control of catalytic activity is essential to ensure the quality and performance of the final product. In this study, the effect of two inhibitors, propanol and arsine, on the properties of synthesized polypropylene was investigated. Experiments were conducted using a conventional catalyst to polymerize propylene, and different concentrations of propanol and arsine were incorporated into the process. The results revealed that the addition of propanol led to a significant decrease in the Melt Flow Index (MFI) of the resulting polypropylene. The reduction in the MFI was most notable at a concentration of 62.33 ppm propanol, suggesting that propanol acts as an effective inhibitor by slowing down the polymerization rate and thus reducing the fluidity of the molten polypropylene. On the other hand, introducing arsine as an inhibitor increased the MFI of polypropylene. The maximum increase in the MFI was observed at a concentration of 0.035 ppm arsine. This suggests that small amounts of arsine affect the MFI and Mw of the produced PP. Regarding the catalyst productivity, it was found that as the concentration of propanol in the sample increased (approximately seven ppm), there was a decrease in productivity from 45 TM/kg to 44 TM/kg. Starting from 10 ppm, productivity continued to decline, reaching its lowest point at 52 ppm, with only 35 MT/kg. In the case of arsine, changes in catalyst productivity were observed at lower concentrations than with propanol. Starting from about 0.006 ppm, productivity decreased, reaching 39 MT/kg at a concentration of 0.024 ppm and further decreasing to 36 TM/kg with 0.0036 ppm. Computational analysis supported the experimental findings, indicating that arsine adsorbs more stably to the catalyst with an energy of -60.8 Kcal/mol, compared to propanol (-46.17 Kcal/mol) and isobutyl (-33.13 Kcal/mol). Analyses of HOMO and LUMO orbitals, as well as reactivity descriptors, such as electronegativity, chemical potential, and nucleophilicity, shed light on the potential interactions and chemical reactions involving inhibitors. Generated maps of molecular electrostatic potential (MEP) illustrated the charge distribution within the studied molecules, further contributing to the understanding of their reactivity. The computational results supported the experimental findings and provided additional information on the molecular interactions between the inhibitors and the catalyst, shedding light on the possible modes of inhibition. Solubles in xylene values indicate that both propanol and arsine affect the polymer's morphology, which may have significant implications for its properties and final applications.

The Power of Molecular Dynamics Simulations and Their Applications to Discover Cysteine Protease Inhibitors.

A large family of enzymes with the function of hydrolyzing peptide bonds, called peptidases or cysteine proteases (CPs), are divided into three categories according to the peptide chain involved. CPs catalyze the hydrolysis of amide, ester, thiol ester, and thioester peptide bonds. They can be divided into several groups, such as papain-like (CA), viral chymotrypsin-like CPs (CB), papain-like endopeptidases of RNA viruses (CC), legumain-type caspases (CD), and showing active residues of His, Glu/Asp, Gln, Cys (CE). The catalytic mechanism of CPs is the essential cysteine residue present in the active site. These mechanisms are often studied through computational methods that provide new information about the catalytic mechanism and identify inhibitors. The role of computational methods during drug design and development stages is increasing. Methods in Computer-Aided Drug Design (CADD) accelerate the discovery process, increase the chances of selecting more promising molecules for experimental studies, and can identify critical mechanisms involved in the pathophysiology and molecular pathways of action. Molecular dynamics (MD) simulations are essential in any drug discovery program due to their high capacity for simulating a physiological environment capable of unveiling significant inhibition mechanisms of new compounds against target proteins, especially CPs. Here, a brief approach will be shown on MD simulations and how the studies were applied to identify inhibitors or critical information against cysteine protease from several microorganisms, such as Trypanosoma cruzi (cruzain), Trypanosoma brucei (rhodesain), Plasmodium spp. (falcipain), and SARS-CoV-2 (Mpro). We hope the readers will gain new insights and use our study as a guide for potential compound identifications using MD simulations.

Effect of preprocessing and simulation parameters on the performance of molecular docking studies.

CONTEXT: Molecular docking is an important and rapid tool that provides a comprehensive view of different molecular mechanisms. It is often used to verify the binding interactions of many pairs of molecules and is much faster than more rigorous approaches. However, its application requires carefully preprocessing each molecule and selecting a series of simulation parameters, which is not always done correctly. We show how preprocessing and simulation parameters can positively or negatively impact molecular docking performance. For example, the inclusion of hydrogen atoms leads to better redocking scores, but molecular dynamics simulations must be performed under certain constraints; otherwise, it may worsen performance rather than improve it. This study clarifies the importance and influence of these different parameters in the simulation results. METHODS: We analyzed the influence of different parameters on the predictive ability of molecular docking techniques using two software packages: AutoDock Vina and AutoDock-GPU. Thus, 90 receptor-ligand complexes were redocked, evaluating the root mean square deviation (RMSD) between the original position of the ligand (receptor-ligand complex obtained experimentally) and that obtained by the software for every analysis. We investigated the influence of hydrogen atoms (on the receptor and on the receptor-ligand complex), partial charges (QEq, QTPIE, EEM, EEM2015ha, MMFF94, Gasteiger-Marsili, and no charge), search boxes (size and exhaustiveness), ligand characteristics (size and number of torsions), and the use of molecular dynamics (of the receptor or the receptor-ligand complex) before docking analyses.

Protocol for iterative optimization of modified peptides bound to protein targets.

Peptides are commonly used as therapeutic agents. However, they suffer from easy degradation and instability. Replacing natural by non-natural amino acids can avoid these problems, and potentially improve the affinity towards the target protein. Here, we present a computational pipeline to optimize peptides based on adding non-natural amino acids while improving their binding affinity. The workflow is an iterative computational evolution algorithm, inspired by the PARCE protocol, that performs single-point mutations on the peptide sequence using modules from the Rosetta framework. The modifications can be guided based on the structural properties or previous knowledge of the biological system. At each mutation step, the affinity to the protein is estimated by sampling the complex conformations and applying a consensus metric using various open protein-ligand scoring functions. The mutations are accepted based on the score differences, allowing for an iterative optimization of the initial peptide. The sampling/scoring scheme was benchmarked with a set of protein-peptide complexes where experimental affinity values have been reported. In addition, a basic application using a known protein-peptide complex is also provided. The structure- and dynamic-based approach allows users to optimize bound peptides, with the option to personalize the code for further applications. The protocol, called mPARCE, is available at: https://github.com/rochoa85/mPARCE/ .

(R)-Carvone is a potential soil fumigant against Meloidogyne incognita whose likely enzymatic target in the nematode is acetylcholinesterase.

To contribute to the development of new fumigant nematicides for the control of the plant-parasitic nematode Meloidogyne incognita, this study started with 31 volatile organic compounds reported as toxic to nematodes. At 500 µg/mL, α-ionone, (S)-carvone, (R)-carvone, 2-methylpropyl acetate, undecan-2-one, decan-2-one, and dodecan-2-one caused mortalities to M. incognita second-stage juveniles (J2) that were similar to those obtained with the commercial nematicides carbofuran (170 µg/mL) and fluensulfone (42.2 µg/mL). (R)-carvone, with a lethal concentration to 50% J2 (LC50) equal to 524 µg/mL, was selected for subsequent studies. When J2 were exposed to the (R)-carvone solution, the infectivity and reproduction on tomato were reduced. In the M. incognita egg hatching assay, (R)-carvone behaved like a true ovicide. When employed as a fumigant, (R)-carvone (3.9 g/L) was as efficient as the soil fumigant dazomet (0.245 g/L) in eliminating eggs of the nematode in a substrate to be used for tomato planting. According to in silico studies employing pharmacophoric searches and molecular docking, acetylcholinesterases are the target of (R)-carvone in the nematode.

Materials Discovery With Machine Learning and Knowledge Discovery.

Machine learning and other artificial intelligence methods are gaining increasing prominence in chemistry and materials sciences, especially for materials design and discovery, and in data analysis of results generated by sensors and biosensors. In this paper, we present a perspective on this current use of machine learning, and discuss the prospects of the future impact of extending the use of machine learning to encompass knowledge discovery as an essential step towards a new paradigm of machine-generated knowledge. The reasons why results so far have been limited are given with a discussion of the limitations of machine learning in tasks requiring interpretation. Also discussed is the need to adapt the training of students and scientists in chemistry and materials sciences, to better explore the potential of artificial intelligence capabilities.

Recent Advances in the Prediction of Pharmacokinetics Properties in Drug Design Studies: A Review.

This review presents the main aspects related to pharmacokinetic properties, which are essential for the efficacy and safety of drugs. This topic is very important because the analysis of pharmacokinetic aspects in the initial design stages of drug candidates can increase the chances of success for the entire process. In this scenario, experimental and in silico techniques have been widely used. Due to the difficulties encountered with the use of some experimental tests to determine pharmacokinetic properties, several in silico tools have been developed and have shown promising results. Therefore, in this review, we address the main free tools/servers that have been used in this area, as well as some cases of application. Finally, we present some studies that employ a multidisciplinary approach with synergy between in silico, in vitro, and in vivo techniques to assess ADME properties of bioactive substances, achieving successful results in drug discovery and design.