Molecular Modeling Research Articles

Investigation of Folate-Functionalized Magnetic-Gold Nanoparticles Based Targeted Drug Delivery for Liver: In Vitro, In Vivo and Docking Studies.

Magnetic nanoparticles used for targeted drug administration present a promising approach in cancer treatment owing to its notable advantages, such as targeted and enhanced encapsulation ability and improved bio protection compared with conventional drug delivery methods. Au shell-iron core nanoparticles (Fe3O4@Au) were manufactured by a chemical process, coated with dextran to encapsulate curcumin, and functionalized for precision drug delivery using folic acid to combat liver cancer. Dynamic light scattering, scanning electron microscopy, transmission electron microscopy, vibrational spectroscopy, and magnetometry were applied to assess the synthesis of the Fe3O4@Au-DEX-CU-FA compound. The mean size, zeta potential, and polydispersity of Fe3O4@Au-DEX-CU-FA were 63.3 ± 2.33 nm, -68.3 ± 1.78 mV, and 0.041 ± 0.008, respectively. Molecular docking models were created to examine the relationship between Fe3O4@Au-CU and BCL-XL, BAK, and to identify potential binding sites. The loading efficiency and release profile tests examined the medication delivery system's ability. MTT assay was subsequently utilized to determine the optimal dosage and therapeutic efficacy of Fe3O4@Au-DEX-CU-FA on cancer SNU-449 and healthy THLE-2 cell lines. Flow cytometry demonstrated that Fe3O4@Au-DEX-CU-FA effectively induced cancer cell death. Fe3O4@Au-DEX-FA showed a regulated release profile of free curcumin at 37 °C and pH values of 7.4 and 5.4. Real-time PCR revealed increased BAK expression and decreased BCL-XL expression. Nude tumor-bearing mice were used for in vivo experiments. Fe3O4@Au-DEX-CU-FA treatment dramatically reduced the swelling size compared with free CU and control treatments. It also resulted in a longer lifespan, expanded splenocyte proliferation, increased IFN-γ levels, and decreased IL-4 levels. The regular cells showed no cytotoxic effect compared with the cancer type, confirming that Fe3O4@Au-DEX-CU-FA maintained its potent anticancer actions. The data suggests that Fe3O4@Au-DEX-CU-FA possesses a promising potential as a therapeutic agent for combating tumors.

Advancing rational pesticide development against Drosophila suzukii: bioinformatics tools and applications-a systematic review.

Drosophila suzukii (Matsumura, 1931) is a widespread agricultural pest responsible for significant damage to various soft-skinned fruit hosts. The revolutionary potential of bioinformatics in agriculture emerges from its ability to provide extensive information on pests, fungi, chemical resistance, implications of non-target species, and other critical aspects. This wealth of information allows researchers to engage in projects and applied research in diverse agricultural domains that face these challenges. In this context, bioinformatics tools play a fundamental role. The negative impact of pests on crops, resulting in substantial economic losses, has highlighted the importance of in silico methods. To achieve this, we conducted a systematic search in scientific databases using as keywords "Drosophila suzukii," "biopesticides," "simulations computational," and "in-silico." After applying the filters of relevance and publication date, we organized the articles and prioritized those that directly addressed that matched the keywords and the use of bioinformatics tools. Additionally, we included studies focusing on in silico assays of biopesticides, such as molecular docking. Our review aimed to present a collection of recent literature on biopesticides against Drosophila suzukii, emphasizing bioinformatics methods. Through this work, we strive to contribute to the literature of new perspectives on the development and efficiency of biopesticides, along with to advance research that may improve pest control strategies. In the results of the systematic review, we found 2734 articles related to the selected keywords. Six of these articles directly address Drosophila suzukii and the use of bioinformatics tools in the search for alternatives in pest control. In the selected studies, we observed that two articles tend to focus on phylogenetic approaches, searching for gene sequences, amino acids, and constructing phylogenetic trees. The other three articles used molecular modeling and docking of receptors such as GABA and TRP with plant-derived and synthetic compounds to study intermolecular interactions. However, we identified gaps in these studies that could lead to further research in the biorational development of biopesticides using bioinformatics tools.

Development of New N- and S-substituted-imidazolidin-4-one Analogues with Potent Anti-breast Cancer Activity: In vitroMolecular Docking Assessment

Abstract: 2-Thioxoimidazolidin-4-one derivatives 3, 4, 7, 8, and 9 have been synthesized from 3- (benzylideneamino)-2-thioxoimidazolidin-4-one (2) as a starting material. Compounds 3, 4, 7, 8, and 9 were obtained via the reaction of compound (2) with ethyl chloroacetate, methyl acrylate, and chlorophenacyl bromide, respectively. Elemental analysis and several spectroscopy techniques were used to confirm the synthesized compounds. The synthesized compounds, particularly compounds 7 and 8, exhibited significant cytotoxic influences on MCF-7 cells, surpassing staurosporine. Compounds 7 and 8 can induce apoptosis in those treated MCF-7 cells. Studying molecular docking approved that compounds 7 and 8 bind in two and three dimensions to the aromatase binding pockets. Molecular modeling indicates compounds 7 and 8 have a strong affinity for human topoisomerase II beta, establishing its promise as a multifaceted antitumor agent for breast cancer.

Investigation of methane adsorption mechanism in Low-Rank bituminous coal considering internal water Distribution: A molecular simulation and experimental study

The impact of high internal water in low-rank bituminous coal on methane adsorption properties has significant implications for the extraction and utilization of low-rank coalbed methane. Mercury-in-pressure (MIP), low-pressure N2/CO2 adsorption (LPNA/CA), low-field nuclear magnetic resonance (NMR), and isothermal adsorption of methane experiments were conducted on two types of low-rank bituminous coal from neighboring mines in the same region. Additionally, molecular dynamics simulations were performed by constructing a molecular model of the low-rank bituminous coal. Experimental and simulation results indicate that the unique internal water distribution characteristics of low-rank bituminous coal and the methane adsorption mechanism under its influence. The initial adsorption of water molecules occurs within the micropores of coal, forming a microporous filling. As relative humidity increases, water molecules migrate spontaneously to the macropores, but they do not seal the pores. This phenomenon is observed in low-rank bituminous coals. The internal water primarily affects the microporous filling of methane, yet it does not alter the absolute predominance of this form in adsorption, and the internal water spontaneously migrates to provide part of the filling site for the methane molecules with the increase in adsorption pressure. The results provide valuable insights into optimizing the extraction strategies for low-rank coalbed methane resources.

Eco-friendly Synthesis of Indole Conjugated Chromeno[d]Pyrimidines as Anti-cancer Agents and their Molecular Modelling Studies

Introduction: Many medicinally active new chemical entities depend on indole conjugated chromeno[d]pyrimidine derivatives as a building block. The synthesis of 4-(1H-indol-3-yl)-3,4- dihydro-1H-chromeno[4,3-d]pyrimidine-2,5-dione 4 were achieved in the current study by treating 4- hydroxy-2H-chromen-2-one 1, indole aldehydes 2, and urea/thiourea 3 in the presence of L-proline. Methods: By adopting the above protocol, we were able to synthesize eight compounds, i.e. 4-(1Hindol- 3-yl)-3,4-dihydro-1H-chromeno[4,3-d]pyrimidine-2,5-diones (4a-4h), in the presence of Lproline as a catalyst in ethanol as solvent for 2-3 hours at 70-75°C with decent yields of 80-85%, and their structures were ascertained by various spectral techniques. They were further screened for their potentiality to inhibit cancer growth in HepG2 and MDA-MD-231 cells. Results: The scope of the synthesis of biological relevant Indole conjugated Chromeno[ d]Pyrimidines by three-component reaction (MCRs) process was investigated. The most optimised conditions obtained were 0.3 eq of L-proline for 2 hours at 70-75°C which gave the best yield (85%). The few advantages of this newly developed method are excellent yields, no metal catalyst, less toxic solvents, simple workup no chromatographic column purifications. On further screening for their anticancer activities, out of all, the compound 4b displayed noteworthy cytotoxicity with IC50 values of 8.1 and 9.2 μM against HepG2 and MDA-MD-231, respectively. Additionally, in silico studies also supported that compound 4b had favourable binding energy (-7.8 kcal/mol) when compared to the co-crystal ligand (LS5) in inhibiting the human cyclin-dependent kinase 2 (CDK2) protein. Conclusion: In conclusion, we have developed a simple, convenient, and efficient method for the synthesis of structurally diverse indole conjugated chromeno[d]pyrimidine analogues in the presence of L-proline as catalyst in ethanol as solvent with good yields. Further, the in vitro cytotoxic studies against HepG2 and MDA-MD-231 cells demonstrated that the synthesized compounds had good to reasonable activity, except for compound 4d.

COMPUTER-AIDED MOLECULAR MODELING AND DESIGN STUDIES OF SOME N-(4-(DIMETHYL AMINO) PHENYL)-4-METHOXY-3-PROPIONAMIDOBENZAMIDE DERIVATIVES AS NS5B POLYMERASE INHIBITORS

Hepatitis C Virus (HCV) is a stealthy ailment, and most people are unaware they have it in the early stages. Insilco modeling was used to develop a model with high predictive power for N-(4-(dimethyl amino) phenyl)-4-methoxy-3-propionamidobenzamide inhibitors. The inhibitors were optimized using Density Functional Theory (DFT) based on a notion of B3LYP/6-31G* levels. The genetic function algorithm (GFA) was utilized to create the QSAR models. The best model was chosen based on its statistical fitness using the subsequent measurement parameters: R2trng = 0.828181, R2adj = 0.800468, Q2cv = 0.734795, R2test = 0.7028, and LOF = 0.613985, led to its selection and publication. The model underwent additional assessments, including the Y-scrambling test, and applicability domain, and was considered to have statistical significance. The docking results demonstrate that compound 17 has the greatest inhibitory effect on NS5B-polymerase of any drug in the dataset. Using this compound as a template, novel anti-hepatitis C compounds were developed by including a few beneficial substituents. The docking results of the proposed compounds yielded a high MolDock score (−152.12), indicating effective binding to the target's active sites. Two comparable compounds (SBD4 and SBD3) were shown to be superior to the food drug administration (FDA) approved medication, revibirin/pegylated interferon. The chemicals were determined to be orally accessible after passing Lipinski's criteria, a drug similarity test. Furthermore, the synthesized compounds were found to have favorable pharmacokinetic (absorption, distribution, metabolism, excretion, and toxicity) properties. In conclusion, using the QSAR model, a model with excellent prediction power for certain Biaryl amide inhibitors was constructed. The model was validated and determined to have good internal and external assessment criteria. According to the docking results, compound 17 inhibits NS5B-polymerase the most out of the compounds in the dataset. Furthermore, the compounds' oral bioavailability met drug-like standards. The ultimate goal of this research is to contribute to the development of new therapeutic agents for the treatment of HCV infections, addressing the pressing need for effective antiviral medications.

Self-diffusion is temperature independent on active membranes.

Molecular transport maintains cellular structures and functions. For example, lipid and protein diffusion sculpts the dynamic shapes and structures on the cell membrane that perform essential cellular functions, such as cell signaling. Temperature variations in thermal equilibrium rapidly change molecular transport properties. The coefficient of lipid self-diffusion increases exponentially with temperature in thermal equilibrium, for example. Hence, maintaining cellular homeostasis through molecular transport is hard in thermal equilibrium in the noisy cellular environment, where temperatures can fluctuate widely due to local heat generation. In this paper, using both molecular and lattice-based modeling of membrane transport, we show that the presence of active transport originating from the cell's cytoskeleton can make the self-diffusion of the molecules on the membrane robust to temperature fluctuations. The resultant temperature-independence of self-diffusion keeps the precision of cellular signaling invariant over a broad range of ambient temperatures, allowing cells to make robust decisions. We have also found that the Kawasaki algorithm, the widely used model of lipid transport on lattices, predicts incorrect temperature dependence of lipid self-diffusion in equilibrium. We propose a new algorithm that correctly captures the equilibrium properties of lipid self-diffusion and reproduces experimental observations.

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.

Synthesis of some benzothiazole-piperazine derivatives, investigation by in vitro and molecular modelling for hMAO inhibitory activities

Synthesis of some benzothiazole-piperazine derivatives, investigation by in vitro and molecular modelling for hMAO inhibitory activities

Conformational Space Profiling Enhances Generic Molecular Representation for AI-Powered Ligand-Based Drug Discovery.

The molecular representation model is a neural network that converts molecular representations (SMILES, Graph) into feature vectors, and is an essential module applied across a wide range of artificial intelligence-driven drug discovery scenarios. However, current molecular representation models rarely consider the three-dimensional conformational space of molecules, losing sight of the dynamic nature of small molecules as well as the essence of molecular conformational space that covers the heterogeneity of molecule properties, such as the multi-target mechanism of action, recognition of different biomolecules, dynamics in cytoplasm and membrane. In this study, a new model named GeminiMol is proposed to incorporate conformational space profiles into molecular representation learning, which extracts the feature of capturing the complicated interplay between the molecular structure and the conformational space. Although GeminiMol is pre-trained on a relatively small-scale molecular dataset (39290 molecules), it shows balanced and superior performance not only on 67 molecular properties predictions but also on 73 cellular activity predictions and 171 zero-shot tasks (including virtual screening and target identification). By capturing the molecular conformational space profile, the strategy paves the way for rapid exploration of chemical space and facilitates changing paradigms for drug design.

High-Resolution Reconstruction of a C. elegans Ribosome Sheds Light on Evolutionary Dynamics and Tissue Specificity.

Caenorhabditis elegans is an important model organism for human health and disease, with foundational contributions to the understanding of gene expression and tissue patterning in animals. An invaluable tool in modern gene expression research is the presence of a high-resolution ribosome structure, though no such structure exists for C. elegans. Here we present a high-resolution single-particle cryogenic electron microscopy (cryoEM) reconstruction and molecular model of a C. elegans ribosome, revealing a significantly streamlined animal ribosome. Many facets of ribosome structure are conserved in C. elegans, including overall ribosomal architecture and the mechanism of cycloheximide, while other facets such as expansion segments and eL28 are rapidly evolving. We identify uL5 and uL23 as two instances of tissue-specific ribosomal protein paralog expression conserved in Caenorhabditis, suggesting that C. elegans ribosomes vary across tissues. The C. elegans ribosome structure will provide a basis for future structural, biochemical, and genetic studies of translation in this important animal system.

Candida antartica lipase-catalyzed esterification for efficient partial acylglycerol synthesis in solvent-free system: Substrate selectivity, molecular modelling and optimization

Partial acylglycerols are valued for their emulsifying and stabilizing properties, yet precise green synthesis remains challenging due to low yield and selectivity. This study aimed to elucidate the “lipase selectivity-substrate structure-product composition” relationship to enhance the yield of targeted partial acylglycerol. The results showed that lipase exhibited a greater selectivity towards fatty acids with shorter chain lengths and higher unsaturation. Hydroxyl donors also affected the esterification process, with the enzyme-acyl complex exhibiting selectivity towards hydroxyl donors as follows: glycerol > monoacylglycerol > diacylglycerol. Substrate ratio significantly influenced enzymatic reactions; a 10:1 ratio favored triacylglycerol formation (>80 %), while a 1:1 ratio produced > 90 % partial acylglycerols. Molecular docking simulations revealed that substrates primarily interacted with lipase through hydrogen bonding and hydrophobic interactions. A comprehensive understanding of lipase selectivity patterns could facilitate the design of more efficient reaction systems, enabling the conversion of basic lipid resources into desired high value-added products.

Unlocking comprehensive molecular design across all scenarios with large language model and unordered chemical language.

Molecular generation stands at the forefront of AI-driven technologies, playing a crucial role in accelerating the development of small molecule drugs. The intricate nature of practical drug discovery necessitates the development of a versatile molecular generation framework that can tackle diverse drug design challenges. However, existing methodologies often struggle to encompass all aspects of small molecule drug design, particularly those rooted in language models, especially in tasks like linker design, due to the autoregressive nature of large language model-based approaches. To empower a language model for a wider range of molecular design tasks, we introduce an unordered simplified molecular-input line-entry system based on fragments (FU-SMILES). Building upon this foundation, we propose FragGPT, a universal fragment-based molecular generation model. Initially pretrained on extensive molecular datasets, FragGPT utilizes FU-SMILES to facilitate efficient generation across various practical applications, such as de novo molecule design, linker design, R-group exploration, scaffold hopping, and side chain optimization. Furthermore, we integrate conditional generation and reinforcement learning (RL) methodologies to ensure that the generated molecules possess multiple desired biological and physicochemical properties. Experimental results across diverse scenarios validate FragGPT's superiority in generating molecules with enhanced properties and novel structures, outperforming existing state-of-the-art models. Moreover, its robust drug design capability is further corroborated through real-world drug design cases.

C9orf72 polyPR interaction with the nuclear pore complex

The C9orf72 gene associated with ALS/FTD is translated to five dipeptide repeat proteins (DPRs), among which poly-proline-arginine (PR) is the most toxic in cell and animal models, contributing to a variety of cellular defects. It has been proposed that polyPR disrupts nucleocytoplasmic transport (NCT) through several mechanisms including accumulation in the nuclear pore complex (NPC), accumulation in the nucleolus, and direct interactions with transport receptors. The NPC, which is the key regulator of transport between the cytoplasm and nucleus, plays a central role in these suggested mechanisms. Exploring polyPR interaction with the NPC provides valuable insight into the molecular details of polyPR-mediated NCT defects. To address this, we use coarse-grained molecular dynamics models of polyPR and the yeast NPC lined with intrinsically-disordered FG-nucleoporins (FG-Nups). Our findings indicate no aggregation of polyPR within the NPC or permanent binding to FG-Nups. Instead, polyPR translocates through the NPC, following a trajectory through the central low-density region of the pore. In the case of longer polyPRs, we observe a higher energy barrier for translocation and a narrower translocation channel. Our study shows that polyPR and FG-Nups are mainly engaged in steric interactions inside the NPC with only a small contribution of specific cation-pi, hydrophobic and electrostatic interactions, allowing polyPR to overcome the entropic barrier of the NPC in a size-dependent manner.

Molecular dynamics model quantum field for prediction of the interaction between chitosan–silver nanoparticles

ABSTRACT In this work, a composite material between chitosan and silver oxide nanoparticles (Ag2NPs) was developed. The universal force field option in the Materials Studio® software was applied to the modelled Ag2NPs within a sphere of variable radius, being 11 å the most stable. In addition, each of these models was taken to their state of minimum energy to verify their stability, and then, a simulation was carried out using a canonical ensemble at 298 K. The X-ray diffraction pattern of these models was simulated and is according to those reported for Ag2O. Molecular and quantum dynamics simulations for the models of the chitosan chains in interaction with Ag2ONPs were made. Each proposed model was based on the Schrödinger equation, considering the quantum fields for the interaction of the nanoparticle in the chitosan substrate. It was observed that the fields were deformed, demonstrating that the presence of the nanoparticle on the surface produces deformation in the polymeric matrix, which suggests a phenomenon of surface adsorption that was confirmed with the RDF where a chemical interaction between the polymer and the silver oxide nanoparticles was shown. This result supports that is possible to obtain the chitosan–Ag2ONPs experimentally.

New Approach as Inhibitor Against Head-Neck Cancer by In silico, DFT, FMOs, Docking, Molecular Dynamic, and ADMET of Euphorbia tirucalli (Pencil Cactus)

Background: Head and neck cancer (HNC) is on the rise worldwide, endangering lives and straining healthcare systems in both developing and developed nations. Despite the availability of a number of therapy options, the success rate for treating and controlling head and neck cancer remains dismal. To combat the aggressiveness and drug resistance of Epstein-Barr virus (EBV)-positive Head-Neck cancer cells, this study looks into the potential of Euphorbia tirucalli (pencil cactus) leaf extract. Objectives: The goal of this study is to identify prospective therapeutic candidates from the extract of Euphorbia tirucalli (pencil cactus) leaves, which have the ability to inhibit Epstein-Barr virus (EBV)-positive Head-Neck cancer cells. Materials and Methods: The thirteen most important chemical components found in Euphorbia tirucalli (pencil cactus) leaves were analyzed by means of molecular modeling techniques such as Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET), Quantum Mechanics (QM) calculation, Molecular Dynamics (MD) profiling, molecular docking, and molecular dynamics (MD) simulations. Using the Prediction of Activity Spectra for Substances (PASS) model, we assess the potency of these compounds in vitro against an Epstein- Barr virus (EBV)-positive head-neck cancer cell line. Important molecular properties such as chemical potential, electronegativity, hardness, and softness can be determined with the use of quantum chemical calculations employing HOMO-LUMO analysis. These drugs' safety and toxicological characteristics are better understood thanks to assessments of their pharmacokinetics and ADMET. These tests show that there is no risk of hepatotoxicity or cancer in humans and that they are safe to use. In order to verify binding interactions and complex stability, molecular dynamics simulations are used to reveal stable docked complexes. Results: The molecular docking analysis identifies ligands (01), (02), and (10) as strong competitors, with strong binding affinity for the Epstein-Barr virus (EBV)-positive Head-Neck cancer cell line. Not only do the ligands (01), (02), and (10) match the criteria for a potential new inhibitor of head-neck cancer, but they also outperform the present FDA-approved treatment. Conclusion: Taraxerol, euphol, and ephorginol, three phytochemicals isolated from the leaves of the Euphorbia tirucalli (pencil cactus), have been identified as effective anti-cancer agents with the potential to serve as a foundation for novel head-neck cancer therapies, particularly those targeting the Epstein-Barr virus (EBV)-overexpressing subtype of this disease. An effective, individualized treatment plan for head-neck cancer is a long way off, but this study is a major step forward that could change the lives of patients and lessen the global burden of this disease.

BuildAMol: a versatile Python toolkit for fragment-based molecular design.

In recent years computational methods for molecular modeling have become a prime focus of computational biology and cheminformatics. Many dedicated systems exist for modeling specific classes of molecules such as proteins or small drug-like ligands. These are often heavily tailored toward the automated generation of molecular structures based on some meta-input by the user and are not intended for expert-driven structure assembly. Dedicated manual or semi-automated assembly software tools exist for a variety of molecule classes but are limited in the scope of structures they can produce. In this work we present BuildAMol, a highly flexible and extendable, general-purpose fragment-based molecular assembly toolkit. Written in Python and featuring a well-documented, user-friendly API, BuildAMol empowers researchers with a framework for detailed manual or semi-automated construction of diverse molecular models. Unlike specialized software, BuildAMol caters to a broad range of applications. We demonstrate its versatility across various use cases, encompassing generating metal complexes or the modeling of dendrimers or integrated into a drug discovery pipeline. By providing a robust foundation for expert-driven model building, BuildAMol holds promise as a valuable tool for the continuous integration and advancement of powerful deep learning techniques.Scientific contributionBuildAMol introduces a cutting-edge framework for molecular modeling that seamlessly blends versatility with user-friendly accessibility. This innovative toolkit integrates modeling, modification, optimization, and visualization functions within a unified API, and facilitates collaboration with other cheminformatics libraries. BuildAMol, with its shallow learning curve, serves as a versatile tool for various molecular applications while also laying the groundwork for the development of specialized software tools, contributing to the progress of molecular research and innovation.

Biosynthetic fuels: A marriage of renewable resources and chemical thermodynamics

Terpenes represent a highly diverse group of natural products with wide applications. Terpenoid structures can be derived from different renewable sources and utilized as fuels. Application as fuels requires hydrogenation of the double bonds in the respective molecules to ensure clean combustion. This hydrogenation can be performed either directly with elemental hydrogen or indirectly via transfer hydrogenation with a Liquid Organic Hydrogen Carrier. In this study, the thermochemical fundamentals of these reaction has been evaluated. For this task, for a first time a combination of experimental thermochemistry and quantum-chemical molecular modelling has been applied. The results show the hydrogenation of the respective structures is thermodynamically highly favourable. Correspondingly, dehydrogenation would be comparatively unfavorable. Yet, for a fuel no dehydrogenation is required. As an alternative to conventional hydrogenation, transfer hydrogenation, which is often subject to partial conversions due to limitations by the reaction equilibrium, can be realized with a high degree of conversion. While the entropy of reaction for hydrogen transfer from cyclohexyl-structures to terpene structures is close to zero, the enthalpy of reaction is highly negative. This leads to a Gibbs energy of reaction which is clearly negative and ensures a large thermodynamic driving force for the reaction. As a consequence, downstream processing, which is usually very demanding for transfer hydrogenations, is comparatively easy. These finding underline the great potential of these bio-derived substances for conversion into sustainable fuels.

Mathematical endeavours to explain biological phenomena

The use of mathematical methods and models is continually increasing in many areas of contemporary biological research: in genomics, molecular modelling, structural biology, neurobiology, biology of systems and so on. In this paper we discuss the importance of Mathematics, mathematical modelling and dynamic visualization, with the aid of technology, in the field of Biology and Medicine. We emphasize that the language of Mathematics and the graphical display is remarkably powerful and flexible, and the modes of understanding and thinking about phenomena can yield deep insight into it that pervades the modern life.

Synthesis and evaluation of isothiazolo[4,5-b]pyridines as cyclin G-associated kinase (GAK) inhibitors.

Isothiazolo[4,3-b]pyridines have been extensively explored as inhibitors of cyclin G-associated kinase (GAK). In order to expand the structure-activity relationship study and to discover other chemotypes that act as GAK inhibitors, the closely related isothiazolo[4,5-b]pyridine scaffold was explored. An easy and efficient synthetic procedure to access 3,5- and 3,6-dihalogenated isothiazolo[4,5-b]pyridines as key building blocks was developed. Regioselective functionalization with various substituents was performed. None of the newly synthesized isothiazolo[4,5-b]pyridines were active as GAK inhibitors. Molecular modeling was applied to rationalise their inactivity as GAK binders.