Angina Bullosa Hemorrhagica: A Rare Presentation of Oral Bleeding.

Angina bullosa hemorrhagica (ABH) is a rare condition seen in the oral cavity which is characterized by the presence of single or multiple blood-filled cavities which are generally not associated with any other systemic illness or condition. These lesions tend to rupture spontaneously and lead to epithelial erosions, which heal over the course of a few days without any signs of scarring. The condition is mostly attributed to trauma in the oral cavity, which occurs as a result of sharp metallic crowns or other such metal implants on the teeth or due to chewing hard and crispy food. This report presents a case of a 50-year-old female with no comorbidities who presented with bleeding from the mouth after eating cashew nuts. The case was diagnosed clinically and conservatively managed. The report aims to increase awareness regarding the causes and management of the condition.

Design expert based optimization of the pyrolysis process for the production of cattle dung bio-oil and properties characterization.

This study aimed to optimize pyrolysis conditions to maximize bio-oil yield from cattle dung, a waste product of livestock practices. Pyrolysis of cattle dung was carried out in batch type reactor. The pyrolysis process was optimized using a central composite design in response surface methodology, with conversion parameters such as pyrolysis temperature, vapor cooling temperature, residence time, and gas flow rate taken into account. The cattle dung bio-oil was analyzed using gas chromatography/mass spectroscopy (GC/MS), an elemental analyzer, a pH probe, and a bomb calorimeter. Furthermore, the ASTM standard procedures were used to determine the bio-fuel characteristics. The optimized conditions were found to be a pyrolysis temperature of 402 °C, a vapor cooling temperature of 2.25 °C, a residence time of 30.72 min, and a gas flow rate of 1.81 l min-1, resulting in a maximum bio-oil yield of 18.9%. According to the findings, the yield of bio-oil was predominantly affected by pyrolysis temperature and vapor cooling temperature. Moreover, the bio-oil that was retrieved was discovered to be similar to conventional liquid fuels in numerous ways.

Machine-Learning-Driven High-Throughput Screening of Transition-Metal Atom Intercalated g-C3N4/MX2 (M = Mo, W; X = S, Se, Te) Heterostructures for the Hydrogen Evolution Reaction.

Rising global energy demand, accompanied by environmental concerns linked to conventional fossil fuels, necessitates a shift toward cleaner and sustainable alternatives. This study focuses on the machine-learning (ML)-driven high-throughput screening of transition-metal (TM) atom intercalated g-C3N4/MX2 (M = Mo, W; X = S, Se, Te) heterostructures to unravel the rich landscape of possibilities for enhancing the hydrogen evolution reaction (HER) activity. The stability of the heterostructures and the intercalation within the substrates are verified through adhesion and binding energies, showcasing the significant impact of chalcogenide selection on the interaction properties. Based on hydrogen adsorption Gibbs free energy (ΔGH) computed via density functional theory (DFT) calculations, several ML models were evaluated, particularly random forest regression (RFR) emerges as a robust tool in predicting HER activity with a low mean absolute error (MAE) of 0.118 eV, thereby paving the way for accelerated catalyst screening. The Shapley Additive exPlanation (SHAP) analysis elucidates pivotal descriptors that influence the HER activity, including hydrogen adsorption on the C site (HC), MX layer (HMX), S site (HS), and intercalation of TM atoms at the N site (IN). Overall, our integrated approach utilizing DFT and ML effectively identifies hydrogen adsorption on the N site (site-3) of g-C3N4 as a pivotal active site, showcasing exceptional HER activity in heterostructures intercalated with Sc and Ti, underscoring their potential for advancing catalytic performance.

Accelerating In Silico Discovery of Metal-Organic Frameworks for Ethane/Ethylene and Propane/Propylene Separation: A Synergistic Approach Integrating Molecular Simulation, Machine Learning, and Active Learning.

Cryogenic distillation, a currently employed method for C2H4/C2H6 and C3H6/C3H8 mixture separation, is energy-intensive, prompting the research toward alternative technologies, including adsorbent-based separation. In this work, we combine machine learning (ML) technique with high-throughput screening to screen ∼23,000 hypothetical metal-organic frameworks (MOFs) for paraffin (C2H6 and C3H8) selective adsorbent separation. First, structure-based prescreening was employed to remove MOFs with undesired geometric properties. Further, a random forest model built upon the multicomponent grand canonical Monte Carlo (m-GCMC) simulation data of training set MOFs was found to be the most successful in learning the relationship between MOF features and olefin/paraffin mixture separation. Using this technique, the separation performance of the remaining (test set) MOFs was predicted, and the top-performing MOFs were identified. We also employed active learning (AL) to evaluate its effectiveness in improving the prediction of olefin/paraffin selectivity. AL was discovered to be ∼29 times more efficient than the best-supervised ML model, as it was able to identify the top materials in limited training data and at a fraction of computational cost and time as compared to ML techniques. Among the top selected materials, framework chemistry was found to be the most important parameter. Nickel and copper (as a metal node) in a tfzd and hms topological arrangement respectively, were discovered to be a prevalent attribute in high-performing MOFs, further demonstrating the prominent significance of framework chemistry. Additionally, the top MOFs discovered were studied in detail and further compared to the previously reported MOFs. These MOFs show the highest selectivity for C2H4/C2H6 and C3H6/C3H8 mixture separation, as reported until date. The hierarchical strategy devised in this study will facilitate the quick screening of MOFs across multiple databases toward industrially significant separation processes by leveraging molecular simulations and AL.

Does freezing induce self-assembly of polymers? A molecular dynamics study.

This work investigates the freezing-induced self-assembly (FISA) of polyvinyl alcohol (PVA) and PVA-like polymers using molecular dynamics simulations. In particular, the effect of the degree of supercooling, degree of polymerization, polymer type, and initial local concentration on the FISA was studied. It was found that the preeminent factor responsible for FISA is not the diffusion of the polymers away from the nucleating ice front, but the increase in the polymer's local concentration upon freezing of the solvent (water). At a higher degree of supercooling, the polymers are engulfed by the growing ice front, impeding their diffusion into the supercooled solution and finally inhibiting their self-assembly. Conversely, at a relatively lower degree of supercooling, the rate of diffusion of the polymers into the supercooled solution is higher, which increases their local concentration and results in FISA. FISA was also observed to depend on the polymer-solvent interactions. Strongly favorable solute-solvent interactions hinder the self-assembly, whereas unfavorable solute-solvent interactions promote the self-assembly. The polymer and aggregate morphology were investigated using the radius of gyration, end-to-end distance, and asphericity analysis. This study brings molecular insights into the quintessential factors governing self-assembly via freezing of the solvent, which is a novel self-assembly technique especially suitable for biomedical applications.

An amine decorated MOF for direct capture of CO2 from ambient air.

A Zn(II)-based metal-organic framework (MOF) was synthesized by the self-assembly of the dicarboxylate ligand terephthalic acid (TPA), 2-aminoterephthalic acid (NH2-TPA) and N-donor auxiliary ligand 1,4-bis(4-pyridinylmethyl)piperazine (bpmp) using Zn(NO3)2·6H2O under hydrothermal conditions. {[Zn(TPA)0.5(NH2TPA)0.5(bpmp)]·DMF·7H2O}n (framework 1) has an sra topology with a BET surface area of 756 m2 g-1. The microporous nature of the framework is apparent from the significant CO2 adsorption capacities observed at various temperatures: 57 cc g-1 at 283 K, 46 cc g-1 at 293 K, 37 cc g-1 at 303 K, and 30 cc g-1 at 313 K. The considerable CO2 adsorption may be caused by the existence of free carboxylate and amine substituents that interact with the gas molecules and micropores. At room temperature, the activated MOF readily converts CO2 into cyclic carbonates when a suspension of the MOF is bubbled with ambient air and different epoxides under solvent-free conditions. The amine groups located within the pores of the MOF interact with CO2 molecules, enhancing their sorption and conversion to cyclic carbonates. However, due to interpenetration within framework 1, only smaller size epoxides can be accommodated and converted to cyclic carbonates in good yields. Additionally, the effectiveness of the catalyst is further confirmed by the positive outcomes obtained from the hot filtration control test. Grand canonical Monte Carlo (GCMC) molecular simulations were utilized to gain a better understanding of molecular interactions. GCMC results are in line with the experiments. The substantial adsorption of CO2 can be ascribed to the strong intermolecular interactions that occur between the amine groups within the framework and the CO2 molecules.

Does supercooled water retain its universal nucleation behavior under shear at high pressure?

Understanding the nucleation of homogeneous flow systems at high pressures is vital in protein crystallization and cryopreservation, where high pressure prevents the freezing of biological samples. This study examines the behavior of ice nucleation under shear at various pressures and explores the universal nucleation behavior of the sheared systems applied to supercooled water at higher pressures. In this study, the nucleation rates for the TIP4P/Ice model via a seeding method based on extended classical nucleation theory (CNT) are computed at pressures of 1, 100, 500, 700, and 1000 bar and a constant temperature of 240 K. Using extended CNT with explicitly embodying the shear rate, we analyzed the dependence of pressure on the transport and thermodynamic properties. In line with previous studies, we observed that Δµliq-ice and viscosity decrease while diffusivity increases with an increase in pressure. Furthermore, we showed that the dependence of the nucleation rate on shear at higher pressure is non-monotonic, with the maximum at optimal shear rates between 107 and 108 s-1. Our results demonstrate a non-monotonic pressure dependence of the optimal shear rates, which could originate from a violation of the Stokes-Einstein relation.

Understanding the role of polymers on the nucleating behavior of water in dilute supercooled solutions.

Understanding the nucleation behavior of water in dilute polymeric solutions is quintessential for the development of suitable artificial ice recrystallization inhibition (IRI) agents. Although poly(vinyl alcohol) (PVA) is found to be one of the most potent biomimetic IRI agents, the molecular understanding of the nucleation behavior of water in the presence of PVA is still lacking. Here, we use molecular dynamics to elucidate the role of concentration, degree of supercooling, degree of polymerization, and amphiphilicity of PVA and PVA-like polymers on the homogeneous nucleation of water in dilute polymeric solutions using the seeding method. Using classical nucleation theory (CNT), our simulations indicate an increase in the chemical potential difference between ice and melt that favors ice nucleation. However, it also predicts a significant increase in the ice-melt interfacial energy that impedes nucleation. The relative increase in the interfacial energy dominates the increase in the chemical potential difference, which results in a decrease in the nucleation rate of water with an increase in the solute concentration. This study contradicts the previous simulation study that suggested the promotion of homogeneous ice nucleation by PVA and supports the experimental observations of the heterogeneous origins of ice nucleation. Our results also suggest the non-classical origins of ice nucleation in polymeric solutions and the limitation of the CNT in predicting heterogeneous ice nucleation in polymeric solutions.

Machine Learning-Driven Discovery of Key Descriptors for CO2 Activation over Two-Dimensional Transition Metal Carbides and Nitrides.

Fusing high-throughput quantum mechanical screening techniques with modern artificial intelligence strategies is among the most fundamental ─yet revolutionary─ science activities, capable of opening new horizons in catalyst discovery. Here, we apply this strategy to the process of finding appropriate key descriptors for CO2 activation over two-dimensional transition metal (TM) carbides/nitrides (MXenes). Various machine learning (ML) models are developed to screen over 114 pure and defective MXenes, where the random forest regressor (RFR) ML scheme exhibits the best predictive performance for the CO2 adsorption energy, with a mean absolute error ± standard deviation of 0.16 ± 0.01 and 0.42 ± 0.06 eV for training and test data sets, respectively. Feature importance analysis revealed d-band center (εd), surface metal electronegativity (χM), and valence electron number of metal atoms (MV) as key descriptors for CO2 activation. These findings furnish a fundamental basis for designing novel MXene-based catalysts through the prediction of potential indicators for CO2 activation and their posterior usage.

Molecular Insights on Morphology, Composition, and Stability of Mixed Micelles Formed by Ionic Surfactant and Nonionic Block Copolymer in Water Using Coarse-Grained Molecular Dynamics Simulations.

The nanoscale association domains are the ultimate determinants of the macroscopic properties of complex fluids involving amphiphilic polymers and surfactants, and hence, it is foremost important to understand the role of polymer/surfactant concentration on these domains. We have used coarse-grained molecular dynamics simulations to investigate the effect of polymer/surfactant concentration on the morphology of poly(ethylene oxide)-poly(propylene oxide)-poly(ethylene oxide) (PEO-PPO-PEO, i.e., pluronics or poloxamers) block copolymer, and ionic surfactants sodium dodecyl sulfate (SDS), mixed micelles in aqueous solution. The proclivity of the surfactant to form the mixed micelles is also probed using umbrella sampling simulations. In this study, we observed that the core of the pluronic + SDS formed mixed micelles consists of PPO, the alkyl tail of SDS, and some water molecules, whereas the PEO, water, and sulfate headgroups of SDS form a shell, consistent with experimental observations. The micelles are spherical at high-pluronic/low-SDS compositions, ellipsoidal at high-SDS/low-pluronic compositions, and wormlike-cylindrical at high-pluronic/high-SDS compositions. The transitions in micelle morphology are governed by the solvent accessible surface area of mixed aggregates, electrostatic repulsion between SDS-headgroups, and dehydration of PEO and PPO segments. The free energy barrier for SDS escape is much higher in mixed micelles than in pure SDS micelles, indicating a stronger tendency for SDS to form pluronic-SDS mixed micelles.

Cardiac Complications in Marfan Syndrome: A Review.

Marfan syndrome (MFS) is a rare inherited disorder of the connective tissue with an autosomal dominant mode of inheritance which happens as a result of a mutation in the fibrillin-1 (FBN1) gene located on chromosome 15q21.1. This mutation results in the defective formation of microfibrils and increased levels of active transforming growth factor beta (TGF beta), leading to defective connective tissue synthesis. These changes affect various parts of the body but most notably affected are the heart, eyes, and the musculoskeletal system. The standard presenting features of a person suffering from MFS are tall stature with a large arm span, kyphosis, congenital dislocation of the lens (ectopia lentis) and cardiovascular manifestations. The 2010 modified Ghent criteria are used to diagnose MFS on the basis of parameters such as cardiovascular, eye, and musculoskeletal disorders. The cardiovascular manifestations in a patient with MFS are the leading causes of mortality. The most common and dreaded complication is an aortic aneurysm and subsequent dissection. Cardiomyopathy and arrhythmia are also potential killers in such patients. This article aims to look at the various cardiac complications mentioned above and gain an understanding of their pathogenesis, incidence, and outcome. It also includes a brief overview of the rare complication post-Bentall graft infection, and its cause, diagnosis, and management. Various articles by several different authors from around the world were searched for information regarding the pathogenesis, incidence, and outcomes of these patients and are referenced below.

Updated greenhouse gas inventory estimates for Indian underground coal mining based on the 2019 IPCC refinements.

Underground coal mining has been known as a significant source of fugitive greenhouse gas emissions. Past analyses of these emissions in India used deterministic emission factors and predominantly focused on methane emissions with reporting of CO2 emissions remaining limited to a few sites. This study addresses these gaps via field measurements on 108 underground mines (out of a total 338) to evolve greenhouse gas reporting in this sector. Results show large heterogeneity across "degrees" of mines as categorized by the Indian government. In addition, CO2 emissions are found to be significant in shallower mines of lower gassiness. Overall, the emissions from underground mining have reduced from 2.6 to 8.3 Mt-CO2e to 1.3-3.6 Mt-CO2e during 1980-2019. These emissions might remain significant by 2050 under a 2-2.5°C constraint or may decline below 100,000 t-CO2e under a 1.5°C constraint. We also discuss several generalizable outcomes and approaches to make inventories in this sector more robust.

Pheochromocytoma in Pregnancy: A Syndromic Association.

Introduction: Pheochromocytoma during pregnancy is a rare cause of secondary hypertension with lethal consequences to both mother and fetus. As patients are young, the possibility of syndromic associations like MEN-2, VHL, NF-1, etc., needs to be considered. Methodology: Three primigravida were diagnosed before the 20th week of gestation when they presented with classical triad of pheochromocytoma. Results: Diagnosis of pheochromocytoma was confirmed by 24 h urinary metanephrine/normetanephrine or epinephrine/norepinephrine levels. Non-contrast MRI abdomen could localize the tumor. One patient had medullary thyroid carcinoma with hyperparathyroidism, indicative of MEN-2A. Another patient had brain stem hemangioblastoma, pancreatic cysts and family history of spinal hemangioblastoma, so diagnosed to have Von Hippel-Lindau (VHL) syndrome. Whereas, the third patient had sporadic pheochromocytoma. Preoperatively, they required antihypertensive medications including prazosin and metoprolol. They underwent laparoscopic/open adrenalectomy between 19th and 21st week of gestation without complication. Histopathology in all the three patients revealed low-grade pheochromocytoma by pheochromocytoma of the adrenal gland scaled score. None required antihypertensive medications after surgery. All the three newborns were small for gestational age, while one neonate expired due to intra-cardiac rhabdomyoma. So, the timely evaluation and surgical intervention for pheochromocytoma avoid lethal consequences. Conclusions: Pregnancy leads to unmasking of pheochromocytoma as it is physiological stress. The syndromic association is more frequent as the population is younger. A poor fetal outcome like IUGR can be explained by endovascular changes in uterine vessel or due to the associated manifestations of MEN-2A, VHL syndromes. Family members should be screened for associated syndromic feature.

Efficient CO2 Capture and Activation on Novel Two-Dimensional Transition Metal Borides.

The large-scale production of CO2 in the atmosphere has triggered global warming, the greenhouse effect, and ocean acidification. The CO2 conversion to valuable chemical products or its capture and storage are of fundamental importance to mitigate the greenhouse effect on the environment. Therefore, exploring new two-dimensional (2D) materials is indispensable due to their potential intriguing properties. Here, we report a new family of 2D transition metal borides (M2B2, M = Sc, Ti, V, Cr, Mn, and Fe; known as MBenes) and demonstrate their static and dynamic stability. These MBenes show a metallic nature and exhibit excellent electrical conductivity. The CO2 adsorption energy on MBenes ranges from -1.04 to -3.95 eV and exhibits the decreasing order as Sc2B2 > Ti2B2 > V2B2 > Cr2B2 > Mn2B2 > Fe2B2. The spin-polarization calculation shows a reduction in the adsorption energy for magnetic systems. Bader charge transfer indicates the formation of CO2δ- moiety on the MBene surface, so-called activated CO2, which is essential for its reaction with other surface chemicals. Differential charge density plots reveal a significant charge accumulation around the CO2 molecule. Our theoretical results predict the usage of new MBenes as a cost-effective catalyst for CO2 capture and activation.

The pressure induced phase diagram of double-layer ice under confinement: a first-principles study.

Here, we present double-layer ice confined within various carbon nanotubes (CNTs) using state-of-the-art pressure induced (-5 GPa to 5 GPa) dispersion corrected density functional theory (DFT) calculations. We find that the double-layer ice exhibits remarkably rich and diverse phase behaviors as a function of pressure with varying CNT diameters. The lattice cohesive energies for various pure double layer ice phases follow the order of hexagonal > pentagonal > square tube > hexagonal-close-packed (HCP) > square > buckled-rhombic (b-RH). The confinement width was found to play a crucial role in the square and square tube phases in the intermediate pressure range of about 0-1 GPa. Unlike the phase transition in pure bilayer ice structures, the relative enthalpies demonstrate that the pentagonal phase, rather than the hexagonal structure, is the most stable ice polymorph at ambient pressure as well as in a deep negative pressure region, whereas the b-RH phase dominates under high pressure. The relatively short O⋯O distance of b-RH demonstrates the presence of a strong hydrogen bonding network, which is responsible for stabilizing the system.

Insights into the Phase Diagram of Pluronic L64 Using Coarse-Grained Molecular Dynamics Simulations.

We investigate the concentration-dependent phase diagram of pluronic L64 in aqueous media at 300 and 320 K using coarse-grained (CG) molecular dynamics (MD) simulations. The CG model is derived by adapting the Martini model for nonbonded interactions along with the Boltzmann inversion (BI) of bonded interactions from all-atom (AA) simulations. Our derived CG model successfully captures the experimentally observed micellar-, hexagonal-, lamellar-, and polymer-rich solution phase. The end-to-end distance reveals the conformational change from an open-chain structure in the micellar phase to a folded-chain structure in the lamellar phase, increasing the orientational order. An increase in temperature leads to expulsion of water molecules from the L64 moiety, suggesting an increase in L64 hydrophobicity. Thermodynamic analysis using the two-phase thermodynamics (2PT) method suggests the entropy of the system decreases with increasing L64 concentration and the decrease in free energy (F) with temperature is mainly driven by the entropic factor (-TS). Further, the increase in aggregation number at lower concentrations and self-assembly at very high concentrations is energetically driven, whereas the change from the micellar phase to the lamellar phase with increasing L64 concentration is entropically driven. Our model provides molecular insights into L64 phases which can be further explored to design functionality-based suprastructures for drug delivery and tissue engineering applications.

Mechanistic insights of key host proteins and potential repurposed inhibitors regulating SARS-CoV-2 pathway.

The emergence of pandemic situations originated from severe acute respiratory syndrome (SARS)-CoV-2 and its new variants created worldwide medical emergencies. Due to the non-availability of efficient drugs and vaccines at these emergency hours, repurposing existing drugs can effectively treat patients critically infected by SARS-CoV-2. Finding a suitable repurposing drug with inhibitory efficacy to a host-protein is challenging. A detailed mechanistic understanding of the kinetics, (dis)association pathways, key protein residues facilitating the entry-exit of the drugs with targets are fundamental in selecting these repurposed drugs. Keeping this target as the goal of the paper, the potential repurposing drugs, Nafamostat, Camostat, Silmitasertib, Valproic acid, and Zotatifin with host-proteins HDAC2, CSK22, eIF4E2 are studied to elucidate energetics, kinetics, and dissociation pathways. From an ensemble of independent simulations, we observed the presence of single or multiple dissociation pathways with varying host-proteins-drug systems and quantitatively estimated the probability of unbinding through these specific pathways. We also explored the crucial gateway residues facilitating these dissociation mechanisms. Interestingly, the residues we obtained for HDAC2 and CSK22 are also involved in the catalytic activity. Our results demonstrate how these potential drugs interact with the host machinery and the specific target residues, showing involvement in the mechanism. Most of these drugs are in the preclinical phase, and some are already being used to treat severe COVID-19 patients. Hence, the mechanistic insight presented in this study is envisaged to support further findings of clinical studies and eventually develop efficient inhibitors to treat SARS-CoV-2.

Integrated docking and enhanced sampling-based selection of repurposing drugs for SARS-CoV-2 by targeting host dependent factors.

Since the onset of global pandemic, the most focused research currently in progress is the development of potential drug candidates and clinical trials of existing FDA approved drugs for other relevant diseases, in order to repurpose them for the COVID-19. At the same time, several high throughput screenings of drugs have been reported to inhibit the viral components during the early course of infection but with little proven efficacies. Here, we investigate the drug repurposing strategies to counteract the coronavirus infection which involves several potential targetable host proteins involved in viral replication and disease progression. We report the high throughput analysis of literature-derived repurposing drug candidates that can be used to target the genetic regulators known to interact with viral proteins based on experimental and interactome studies. In this work we have performed integrated molecular docking followed by molecular dynamics (MD) simulations and free energy calculations through an expedite in silico process where the number of screened candidates reduces sequentially at every step based on physicochemical interactions. We elucidate that in addition to the pre-clinical and FDA approved drugs that targets specific regulatory proteins, a range of chemical compounds (Nafamostat, Chloramphenicol, Ponatinib) binds to the other gene transcription and translation regulatory proteins with higher affinity and may harbour potential for therapeutic uses. There is a rapid growing interest in the development of combination therapy for COVID-19 to target multiple enzymes/pathways. Our in silico approach would be useful in generating leads for experimental screening for rapid drug repurposing against SARS-CoV-2 interacting host proteins.Communicated by Ramaswamy H. Sarma.

Metadynamics-based enhanced sampling protocol for virtual screening: case study for 3CLpro protein for SARS-CoV-2.

In recent times, computational methods played an important role in the down selection of chemical compounds, which could be a potential drug candidate with a high affinity to target proteins. However, the screening methodologies, including docking, often fails to identify the most effective compound, which could be a ligand for the target protein. To solve that, here we have integrated meta-dynamics, an enhanced sampling molecular simulation method, with all-atom molecular dynamics to determine a specific compound that could target the main protease of novel severe acute respiratory syndrome coronavirus 2 (SARS-COV-2). This combined computational approach uses the enhanced sampling to explore the free energy surface associated with the protein's binding site (including the ligand) in an explicit solvent. We have implemented this method to find new chemical entities that exhibit high specificity of binding to the 3-chymotrypsin-like cysteine protease (3CLpro) present in the SARS-CoV-2 and segregated to the most strongly bound ligands based on free energy and scoring functions (defined and implemented) from a set of 17 ligands which were prescreened for synthesizability and druggability. Additionally, we have compared these 17 ligands' affinities against controls, N3 and 13b α-ketoamide inhibitors, for which experimental crystal structures are available. Based on our results and analysis from the combined molecular simulation approach, we could identify the best compound which could be further taken as a potential candidate for experimental validation.Communicated by Ramaswamy H. Sarma.

Building Unit Extractor for Metal-Organic Frameworks.

Metal-organic frameworks (MOFs) have relevance in extensive applications such as gas adsorption, separation, and energy storage. The tunability demonstrated by MOFs has encouraged research on MOF database generation via distinct methodologies. One of the crucial stages of these procedures is pre-processing, which often includes extraction of the building units (BUs). The process of BU extraction is intricate, and it is further amplified with the presence of solvent molecules/ions in the structure. This work presents MOF BU developer (mBUD), a platform to deconstruct the BUs, such as metal nodes, organic linkers, and functional groups of the MOF structure. The deconstruction algorithm has been assessed on the MOF structures of the CoRE MOF 2019 database. A total of 2,580 BUs have been extracted and provided as a database. This platform has been utilized to create a ready-to-use database of unique BUs deconstructed from the CoRE MOF database. We have also provided the web version of mBUD that can be easily used to extract BUs. These BUs can be employed to develop hypothetical MOF structures. It is envisaged that the BU database built with the deconstruction platform will aid the design of novel application-specific MOFs.