Computational Chemistry Techniques Research Articles

TIMM9 as a prognostic biomarker in multiple cancers and its associated biological processes

TIMM9 has been identified as a mediator of essential functions in mitochondria, but its association with pan-cancer is poorly understood. We herein employed bioinformatics, computational chemistry techniques and experiments to investigate the role of TIMM9 in pan-cancer. Our analysis revealed that overexpression of TIMM9 was significantly associated with tumorigenesis, pathological stage progression, and metastasis. Missense mutations (particularly the S49L variant), copy number variations (CNV) and methylation alterations in TIMM9 were found to be associated with poor cancer prognosis. Moreover, TIMM9 was positively related with cell cycle progression, mitochondrial and ribosomal function, oxidative phosphorylation, TCA cycle activity, innate and adaptive immunity. Additionally, we discovered that TIMM9 could be regulated by cancer-associated signaling pathways, such as the mTOR pathway. Using molecular simulations, we identified ITFG1 as the protein that has the strongest physical association with TIMM9, which show a promising structural complement.

Interaction Analysis by Fragment Molecular Orbital Method for Drug Discovery Research.

The use of computational methods in drug discovery research has increased substantially in recent years. Computational chemistry techniques, such as quantum chemical calculations and molecular dynamics simulations, continue to be widely used. In this review, we focused on drug discovery-related studies that employ fragment molecular orbital methods. Furthermore, we focused on inhibitor discovery, protein-protein interaction analysis, including antigen-antibody interaction analysis, and integration with molecular dynamics simulations.

Bimetallic porphyrin PET radiotracers for Low-Dose MRI contrast enhancement

In medical imaging, concerns over the risks associated with imaging agents have been consistently raised by doctors and patients, and ongoing efforts are being made to cautiously explore safer and more efficient compounds. There is continued interest in combining the well-known imaging modalities of positron emission tomography (PET) and magnetic resonance imaging (MRI). However, there has not been reported a single-molecule probe offering dual-modal imaging without performance degradation. We herein present a potential solution wherein the compound, PGaLGd, incorporates radioactive Ga(III) for PET imaging whilst simultaneously being able to exhibit MRI contrast through a coordinated Gd(III) ion but at low concentrations/doses. Besides elucidating MRI enhancing mechanisms through computational chemistry techniques, our compound has proven to be a successful dual-imaging agent for both PET and MRI in mice. Additionally, we conducted comprehensive in vitro and in vivo studies, assessing biosafety and photodynamic therapeutic potential across various cell lines and organoids. We have placed importance on interlinking structures, coordinated metals, adjacent ligands, and frontier molecular orbitals in our probe design to enhance water relaxation ability and move towards the design of next-generation low-dose imaging agents.

Discovery of HDM2010, a highly potent PTPN2 inhibitor for immunotherapy.

e15021 Background: Protein tyrosine phosphatase non-receptor type 2 (PTPN2), a negative regulator of JAK–STAT pathways that plays a crucial role in the immune system, has been identified as a promising target for immunotherapy. Genetic deletion of PTPN2 in either tumor cells or host immune cells promotes the anti-tumor immunity. In addition, PTPN2 inhibitors, alone or in combination with anti-PD1 therapy, significantly reduced tumor growth and enhanced survival in several subcutaneous tumour models. These findings demonstrate that PTPN2 inhibitors may have clinical benefits for immunotherapy-resistant tumors. Herein, the discovery of a series of novel PTPN2 inhibitors and their implications for immunotherapy are reported. Methods: We have successfully developed a series of small molecule inhibitors that specifically target PTPN2/N1. Molecular design was based on a novel backbone and incorporating both structure-based drug design (SBDD) and computational chemistry techniques. To evaluate the efficacy of our inhibitors, PTPN2 phosphatase activity was monitored using the small-molecule substrate DiFMUP in a fluorescence assay format in vitro. Tumor cells were treated with IFNγ and the cell viability was measured. Pharmacokinetic studies were conducted in mice, with serial blood samples analyzed by liquid chromatography−mass spectrometry (LC−MS)/MS. Pharmacokinetic parameters were calculated by noncompartmental analysis. Results: Based on the co-crystal structure of this target reported and multiple previous research projects, we have designed a multitude of novel compounds with the employment of computer-aided drug design (CADD) and scaffold hopping. Through a comprehensive evaluation of the in vitro and in vivo parameters, we have identified a series of compounds displaying potent PTPN2 inhibition (IC50 < 10 nM). Moreover, the compounds displayed remarkable efficacy in killing tumor cells to IFNγ treatment (IC50 < 100 nM). Artificial intelligence was utilized to conduct assessment and prediction of ADME properties, improving the drug-ability including permeability and physicochemical properties. Currently, a series of compounds demonstrating outstanding potency and good pharmacokinetic profiles, with lower hERG blockage (hERG IC50 > 30 µM), were synthesized and characterized. Among these hit compounds, a leading compound with PTPN2 IC50 = 1.1 nM and tumor cells IC50 = 79.5 nM was identified for further optimization and evaluation. Conclusions: We reported our work on discovering HDM2010, in which a series of potent PTPN2 inhibitors with prominent PK and safety profile were synthesized and characterized. Further optimization including in vivo experiments will lead to a preclinical candidate (PCC) selection.

In silico analysis of the action of saturated, monounsaturated, and polyunsaturated fatty acids against Echinococcus granulosus fatty-acid-binding protein 1

Background The zoonotic infection caused by tapeworms Echinococcus is a neglected tropical disease in poor regions with limited access to suitable sanitary conditions. Hydatid cysts produced by Echinococcus granulosus use fatty-acid-binding proteins (FABP) to obtain the fatty acids and cholesterol necessary for their survival from the host. In this work, we analyzed the behaviour of saturated, monounsaturated, and polyunsaturated fatty acids against EgFABP1. Methods We used computational biology and chemistry techniques and binding free energy estimations by molecular mechanics generalized Born surface area (MM/GBSA). Results This research has enabled us to clarify the EgFABP1 isoforms identified in the database, suggesting their potential involvement in diverse cellular activities of Echinococcus granulosus. Conversely, examining the global and local chemical reactivity of 14 fatty acids revealed that liposolubility is contingent upon the degree of unsaturation in the FAs. Additionally, FAs exhibited acceptable levels of oral absorption and bioavailability. The binding of EgFABP1 with FAs analyzed by molecular dynamics simulation showed us that these are highly stable, where the best affinity was with docosahexaenoic acid. Conclusions Our results suggest that the action of fatty acids could play an interesting role in detecting early Echinococcus granulosus.

Advances in hydrogen storage materials: harnessing innovative technology, from machine learning to computational chemistry, for energy storage solutions

The demand for clean and sustainable energy solutions is escalating as the global population grows and economies develop. Fossil fuels, which currently dominate the energy sector, contribute to greenhouse gas emissions and environmental degradation. In response to these challenges, hydrogen storage technologies have emerged as a promising avenue for achieving energy sustainability. This review provides an overview of recent advancements in hydrogen storage materials and technologies, emphasizing the importance of efficient storage for maximizing hydrogen's potential. The review highlights physical storage methods such as compressed hydrogen (reaching pressures of up to 70 MPa) and material-based approaches utilizing metal hydrides and carbon-containing substances. It also explores design considerations, computational chemistry, high-throughput screening, and machine-learning techniques employed in developing efficient hydrogen storage materials. This comprehensive analysis showcases the potential of hydrogen storage in addressing energy demands, reducing greenhouse gas emissions, and driving clean energy innovation.

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

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

Identification and dynamics of novel scaffolds against Enterococcus faecium serine hydroxymethyltransferase enzyme: a potential target for antibiotics development

Serine hydroxymethyltransferase enzyme is a significant player in purine, thymidylate, and L-serine biosynthesis and has been tagged as a potential target for cancer, viruses, and parasites. However, this enzyme as an anti-bacterial druggable target has not been explored much. Herein, in this work, different computational chemistry and biophysics techniques were applied to identify potential computational predicted inhibitory molecules against Enterococcus faecium serine hydroxymethyltransferase enzyme. By structure based virtual screening process of ASINEX antibacterial library against the enzyme two main compounds: Top-1_BDC_21204033 and Top-2_BDC_20700155 were reported as best binding molecules. The Top-1_BDC_21204033 and Top-2_BDC_20700155 binding energy value is −9.3 and −8.9 kcal/mol, respectively. The control molecule binding energy score is −6.55 kcal/mol. The mean RMSD of Top-1-BDC_21204033, Top-2-BDC_20700155 and control is 3.7 Å (maximum 5.03 Å), 1.7 Å (maximum 3.05 Å), and 3.84 Å (maximum of 6.7 Å), respectively. During the simulation time, the intermolecular docked conformation and interactions were seen stable despite of few small jumps by the compounds/control, responsible for high RMSD in some frames. The MM/GBSA and MM/PBSA binding free energy of lead Top-2-BDC_20700155 complex is −79.52 and −82.63 kcal/mol, respectively. This complex was seen as the most stable compared to the control. Furthermore, the lead molecules and control showed good druglikeness and pharmacokinetics profile. The lead molecules were non-toxic and non-mutagenic. In short, the compounds are promising in terms of binding to the serine hydroxymethyltransferase enzyme and need to be subjected to experimental studies. Communicated by Ramaswamy H. Sarma

Guiding maps of solvents for lithium-sulfur batteries via a computational data-driven approach

Practical realization of lithium-sulfur batteries requires designing optimal electrolytes with controlled dissolution of polysulfides, high ionic conductivity, and low viscosity. Computational chemistry techniques enable tuning atomistic interactions to discover electrolytes with targeted properties. Here, we introduce ComBat (Computational Database for Lithium-Sulfur Batteries), a public database of ∼2,000 quantum-chemical and molecular dynamics properties for lithium-sulfur electrolytes composed of solvents spanning 16 chemical classes. We discuss the microscopic origins of polysulfide clustering and the diffusion mechanism of electrolyte components. Our findings reveal that polysulfide solubility cannot be determined by a single solvent property like dielectric constant. Rather, observed trends result from the synergistic effect of multiple factors, including solvent C/O ratio, fluorination degree, and steric hindrance effects. We propose binding energy as a proxy for Li+ dissociation, which is a property that impacts the ionic conductivity. The insights obtained in this work can serve as guiding maps to design optimal lithium-sulfur electrolyte compositions.

Molecular Simulation Techniques as Applied to Silica and Carbon-Based Adsorbents for Carbon Capture

There has been ongoing interest in research to mitigate climate change through carbon capture (CC) by adsorption. This guideline is meant to introduce computational chemistry techniques in CC by applying them to mesoporous structures and disordered morphologies. The molecular simulation techniques presented here use examples of literature studies on silica and carbon-based adsorbents. An initial summary of molecular simulation techniques and concepts is first presented. This is followed by a section on molecular simulation applications in mesoporous amorphous silica, both functionalized and not. Novel strategies to validate and output useful results are discussed, specifically when modelling chemisorption. The use of computational chemistry to build upon experimental results is reviewed, and a similar summation is presented for carbon-based adsorbents. The final section provides a short review of computational chemistry methods in novel applications and highlights potential complications. Computational chemistry techniques provide a streamlined method of gathering data across a range of conditions. Alongside experimental studies, these techniques can provide valuable information on underlying molecular mechanisms. This paper aims to be a starting point for navigating these numerical methods by providing an initial understanding of how these techniques can be applied to carbon capture while clarifying the current and inherent limitations present.

Two Conformational Polymorphs of a Bioactive Pyrazolo[3,4-d]pyrimidine

Two monoclinic (P21/c; Z′ = 1) polymorphs, α (from methanol) and β (from ethanol, n-propanol and iso-propanol), of a bioactive pyrazolo[3,4-d]pyrimidine derivative have been isolated and characterised by X-ray crystallography as well as by a range of computational chemistry techniques. The different conformations observed for the molecules in the crystals are due to the dictates of molecular packing as revealed by geometry-optimisation calculations. The crucial difference in the molecular packing pertains to the formation of phenylamino-N–H···N(pyrazolyl) hydrogen bonding within supramolecular chains with either helical (α-form; 21-screw symmetry) or zigzag (β-form; glide symmetry). As a consequence, the molecular packing is quite distinct in the polymorphs. Lattice energy calculations indicate the β-form is more stable by 11 kJ/mol than the α-form.

Virtual Screening for Identification of Dual Inhibitors against CDK4/6 and Aromatase Enzyme.

CDK4/6 and aromatase are prominent targets for breast cancer drug discovery and are involved in abnormal cell proliferation and growth. Although aromatase inhibitors have proven to be effective (for example exemestane, anastrozole, letrozole), resistance to treatment eventually occurs through the activation of alternative signaling pathways, thus evading the antiproliferative effects of aromatase inhibitors. One of the evasion pathways is Cylin D-CDK4/6-Rb signaling that promotes tumor proliferation and resistance to aromatase inhibitors. There is significant evidence that the sequential inhibition of both proteins provides therapeutic benefits over the inhibition of one target. The basis of this study objective is the identification of molecules that are likely to inhibit both CDK4/6 and aromatase by computational chemistry techniques, which need further biochemical studies to confirm. Initially, a structure-based pharmacophore model was constructed for each target to screen the sc-PDB database. Consequently, pharmacophore screening and molecular docking were performed to evaluate the potential lead candidates that effectively mapped both of the target pharmacophore models. Considering abemaciclib (CDK4/6 inhibitor) and exemestane (aromatase inhibitor) as reference drugs, four potential virtual hit candidates (1, 2, 3, and 4) were selected based on their fit values and binding interaction after screening a sc-PDB database. Further, molecular dynamics simulation studies solidify the stability of the lead candidate complexes. In addition, ADMET and DFT calculations bolster the lead candidates. Hence, these combined computational approaches will provide a better therapeutic potential for developing CDK4/6-aromatase dual inhibitors for HR+ breast cancer therapy.

Computational Chemistry and Molecular Modeling Techniques for the Study of Micropeptin EI-964: Insights into Its Chemical Reactivity and Potential Pharmaceutical Properties

Computational Chemistry and Molecular Modeling Techniques for the Study of Micropeptin EI-964: Insights into Its Chemical Reactivity and Potential Pharmaceutical Properties

Review on Molecular Modelling in Chemistry Education

Molecular models derived from results of quantum-chemical calculations present an important category of didactic instruments in chemistry education. These models can be used especially as tools for supporting the students’ understanding by visual learning, which can adequately address complexity of many chemical topics, incorporate appropriate didactic principles, as well as utilize the benefits brought up by the actual information technology. The proposed molecular models are non-trivial examples of didactic application of computational chemistry techniques in illustration of electron interactions in amidic group, namely the interaction of the free electron pair on the nitrogen atom with the carbonyl group and also the interaction of atoms in the amide group with other surrounding atoms in the molecule. By these molecular models it is possible to explain acid-base properties of amides applying knowledge of electron density distribution in the molecules and the resulting electrostatic potential. Presentation of the structure and properties of the amides within education is important also for the reason that amidic functions are involved in many important natural substances (e.g. proteins, peptides, nucleic acids or alkaloids), synthetic macromolecular substances (e.g. Silon) or pharmaceutical preparations (e.g. paracetamol). This paper investigated the effect of using different types of models while teaching organic chemistry on student understanding of new concepts and the spatial structure of new molecules, as well as preference of a particular model type and illustrates the possibilities of computer-assisted molecular modelling in supporting chemistry teaching and learning. Keywords: Molecular models, Acidity, Amides, Basicity, Electrostatic potential

Natural remedies medicine derived from flaxseed (secoisolariciresinol diglucoside, lignans, and α-linolenic acid) improve network targeting efficiency of diabetic heart conditions based on computational chemistry techniques and pharmacophore modeling.

Cytokine storms lead to cardiovascular diseases (CVDs). Natural herbal compounds are considered the primary source of active agents with the potential to prevent or treat inflammatory-related pathologies such as CVD and diabetes. Flaxseed contains phytochemicals, including secoisolariciresinol diglucoside (SDG), α-linolenic acid (ALA), and lignans, termed "SAL." Hence, we evaluated the effect of the SAL on the H9c2 cardiac cells in hyperlipidemic and hyperglycemic conditions. Here, candidate hub genes, TNF-α, IL6, SIRT1, NRF1, NPPA, and FGF7, were selected as effective genes in diabetic cardiovascular pathogenesis based on in-silico analysis and chemoinformatic. Myocardial infarction (MI) was induced using H9c2 cardiac cells in hyperlipidemic and hyperglycemic conditions. Real-time qPCR was conducted to assess the expression level of hub genes. This study indicated that SAL compounds bound to the Il-6, SIRT1, and TNF-α active sites as druggable candidate proteins based on the chemoinformatics analysis. This study displayed that the TNF-α, IL6, SIRT1, NRF1, NPPA, and FGF7 network dysfunction in MI models were ameliorated by SAL consumption. Furthermore, SAL compounds improved the function and myogenesis of H9c2 cells in hyperlipidemic and hyperglycemic conditions. Our data suggested that phytochemicals obtained from flaxseed might have proposed potential complementary treatment or preventive strategies for MI. PRACTICAL APPLICATIONS: Phytochemicals obtained from flaxseed (SAL) could reverse diabetic heart dysfunction hallmarks and provide new potential treatment approaches in cardiovascular therapy. SAL could be considered complementary and alternative medicines for treating various disorders/diseases singly or synchronizing with prescription drugs.

In Silico Ligand Docking Approaches to Characterise the Binding of Known Allosteric Modulators to the Glucagon-Like Peptide 1 Receptor and Prediction of ADME/Tox Properties

The glucagon-like peptide 1 receptor (GLP-1R) is a member of the family (or class) B G-protein-coupled receptor (GPCR). The receptor is a regulator of insulin and a key target in treating Type 2 diabetes mellitus. In this investigation, computational chemistry techniques such as molecular docking were combined with in silico ADME/Tox predictions to determine the position and structure of the allosteric binding site, as well as to examine how the allosteric modulators bind to the binding site. In silico evaluation was used to evaluate the ADME/Tox properties of the allosteric modulators. The findings of the ligand docking studies suggest that the allosteric binding site is situated around the transmembrane (TM) domain TM 6 of the receptor in the active state. ADME/Tox characterisation of the allosteric modulators demonstrate that compounds 1–3 (2,6,7-trichloro-3-(trifluoromethyl)quinoxaline, 1-(5-(4-(tert-butyl)phenyl)-1,3,4-oxadiazol-2-yl)-6,6-dimethyl-3-(methylsulfonyl)-6,7-dihydrobenzo[c]thiophen-4(5H)-one, 2-((4-chlorophenyl)thio)-3-(trifluoromethyl)quinoxaline, respectively) complied with the traditional method of evaluating drug-likeness; Lipinski’s rule of 5. The allosteric modulator compound 4 (3-(8-chloro-6-(trifluoromethyl)imidazo[1,2-a]pyridin-2-yl)phenyl cyclohexanecarboxylate) failed to comply with Lipinski’s rule of five as a result of having a logP value of over 5.6. Moreover, molecular docking studies provide insights into potential allosteric binding sites and possible interactions. Finally, the in silico ADME/Tox study results are described as relevant to developing a viable drug candidate.

Freestanding non-covalent thin films of the propeller-shaped polycyclic aromatic hydrocarbon decacyclene

Molecularly thin, nanoporous thin films are of paramount importance in material sciences. Their use in a wide range of applications requires control over their chemical functionalities, which is difficult to achieve using current production methods. Here, the small polycyclic aromatic hydrocarbon decacyclene is used to form molecular thin films, without requiring covalent crosslinking of any kind. The 2.5 nm thin films are mechanically stable, able to be free-standing over micrometer distances, held together solely by supramolecular interactions. Using a combination of computational chemistry and microscopic imaging techniques, thin films are studied on both a molecular and microscopic scale. Their mechanical strength is quantified using AFM nanoindentation, showing their capability of withstanding a point load of 26 ± 9 nN, when freely spanning over a 1 μm aperture, with a corresponding Young’s modulus of 6 ± 4 GPa. Our thin films constitute free-standing, non-covalent thin films based on a small PAH.

Recent Achievements in Total Synthesis for Integral Structural Revisions of Marine Natural Products.

A great effort to discover new therapeutic ingredients is often initiated through the discovery of the existence of novel marine natural products. Since substances produced by the marine environment might be structurally more complex and unique than terrestrial natural products, there have been cases of misassignments of their structures despite the availability of modern spectroscopic and computational chemistry techniques. When it comes to refutation to erroneously or tentatively proposed structures empirical preparations through organic chemical synthesis has the greatest contribution along with close and sophiscated inspection of spectroscopic data. Herein, we analyzed the total synthetic studies that have decisively achieved in revelation of errors, ambiguities, or incompleteness of the isolated structures of marine natural products covering the period from 2018 to 2021.

Insights into Binding Affinity of Flavonoid Compounds from Thai Herbs against 2009 H1N1 Hemagglutinin

Outbreak of influenza virus is one of serious concerns for public health. Hemagglutinin (HA), a spike-shaped glycoprotein on the viral surface, plays an important role during the early stage of influenza infection. In the present work, a set of flavonoids were screened against 2009 H1N1 HA by computational chemistry techniques. Among 35 flavonoids, the docking results showed that the epicatechin gallate (ECG) and puerarin exhibited a good binding affinity towards 2009 H1N1 HA. These 2 compounds were then studied by all-atom molecular dynamics (MD) simulations. The predicted binding free energy of the H1-puerarin complex (–25.86 ± 2.92 kcal/mol) was slightly greater than that of H1-ECG (–22.81 ± 2.19 kcal/mol), suggesting that the puerarin and ECG could provide similar binding affinity towards 2009 H1 HA target. However, the stronger electrostatic energy contribution of ~10 kcal/mol was found in the puerarin binding to 2009 H1N1 HA. This molecular information of ligand-protein interaction could be helpful in further drug design and development for influenza treatment.
 HIGHLIGHTS
 
 The 35 flavonoid bioactive compounds were screened against 2009 H1N1 HA by molecular docking
 The epicatechin gallate (ECG) and puerarin exhibited a good binding affinity and were then studied by all-atom molecular dynamics (MD) simulations
 The predicted binding free energy revealed that the puerarin and ECG could provide similar binding affinity towards 2009 H1 HA target
 
 GRAPHICAL ABSTRACT

Exploring New Tetrahydrothienopyridine Derivatives as Platelet Agglutination Inhibitors: Synthesis, Biological Evaluation and In Silico Study

AbstractThe P2Y12 receptor is the major target for antithrombic drugs which plays a key role in platelet activation. New derivatives of 4,5,6,7‐tetrahydrothieno[3,2‐c]pyridine (THP) were designed targeting P2Y12 receptor. An efficient route was developed for synthesis of THP derivatives and subsequently evaluated for their antiplatelet agglutination activity. Amongst the synthesized THP derivatives (4 a–4 g), the compounds 4 a and 4 g displayed significant activity (with 88.25 and 70.17 % inhibition) as compared to other analogs and comparable with that of the reference drugs, aspirin and prasugrel. Data extracted from computational chemistry techniques such as molecular docking, provided the structural rationale for the observed platelet agglutination inhibition by the newly synthesized tetrahydrothienopyridine analogs. We proposed the involvement of residues such as Cys‐194 in the formation of the covalent adduct with the active metabolite of tetrahydrothienopyridine derivatives. This study also put forward the possibility of the existence of an alternate pathway for metabolizing the tetrahydrothienopyridine compounds. The structural data presented in this study is expected to accelerate the research on developing a tetrahydrothienopyridine scaffold as an effective antithrombotic therapeutic modality.