Multistep Procedure Research Articles

Phytochemical-Based Drug Discovery for Breast Cancer: Combining Virtual Screening and Molecular Dynamics to Identify Novel Therapeutics.

Maternal Embryonic Leucine Zipper Kinase (MELK), a pivotal signaling protein, plays a crucial role in various physiological processes such as cell growth, survival, and differentiation. There is currently a growing interest in MELK as a promising therapeutic target for multiple cancers, including triple-negative breast cancer (TNBC). Exploring MELK as a target offers a prospective strategy to impede cancer progression and enhance the efficacy of conventional anticancer therapies. In this study, we employed a multistep docking procedure to evaluate the anticancer potential of phyto-compounds from the NPACT and PhytoHub databases targeting the MELK protein. The extensive analysis identified five compounds (PHUB000697, PHUB002010, NPACT00373, PHUB002005, and PHUB001739) as potent inhibitors of the MELK protein, exhibiting a docking scores lower than -11 Kcal/mol i.e. -12.90, -12.00, -11.23, -11.19, -11.09, Kcal/mol respectively. PHUB000697 exhibited very crucial interactions with Gly20, Lys40, Cys89, and Glu93 (2.74 Å). To evaluate the stability of protein-ligand interactions in dynamic states, 100 ns molecular dynamics (MD) simulations were conducted using the entire trajectory, revealing a substantial binding affinity for all identified compounds towards the MELK protein. We are aiming to report few in vitro and in vivo studies on these compounds to validate the computational results.

Manganese(I)-Catalyzed Enantioselective Alkylation To Access P-Stereogenic Phosphines.

This work introduces a novel Mn(I)-catalyzed enantioselective alkylation methodology that efficiently produces a wide array of P-chiral phosphines with outstanding yields and enantioselectivities. Notably, the exceptional reactivity of Mn(I) complexes in these reactions is demonstrated by their effective catalysis with both typically reactive alkyl iodides and bromides, as well as with less reactive alkyl chlorides. This approach broadens the accessibility to various P-chiral phosphines and simplifies the synthesis of chiral tridentate pincer phosphines to a concise 1-2 step process, contrary to conventional, labor-intensive multistep procedures. Importantly, the development significantly expands the applicability of earth-abundant Mn(I)-based complexes beyond their recently established roles in catalytic hydrogenative and conjugate addition reactions, emphasizing the catalytic potential of Mn(I) complexes as a viable alternative to noble metal chemistry and, in some cases, even surpassing their performance.

The Virtual DCCT: Adding Continuous Glucose Monitoring to a Landmark Clinical Trial for Prediction of Microvascular Complications.

Objective: Using a multistep machine-learning procedure, add virtual continuous glucose monitoring (CGM) traces to the original sparse data of the landmark Diabetes Control and Complications Trial (DCCT). Assess the association of CGM metrics with the microvascular complications of type 1 diabetes observed during the DCCT and establish time-in-range (TIR) as a viable marker of glycemic control. Research Design and Methods: Utilizing the DCCT glycated hemoglobin data obtained every 1 or 3 months plus quarterly 7-point blood glucose (BG) profiles in a multistep procedure: (i) utilized archival BG traces to model interday BG variability and estimate glycated hemoglobin; (ii) trained across the DCCT BG profiles and associated each profile with an archival BG trace; and (iii) used previously identified CGM "motifs" to associate a CGM trace to a BG trace, for each DCCT participant. Results: TIR (70-180 mg/dL) computed from virtual CGM data over 14 days prior to each glycated hemoglobin measurement reproduced the observed glycemic control differences between the intensive and conventional DCCT groups, with TIR generally >60% and <40% in these groups, respectively. Similar to glycated hemoglobin, TIR was associated with the risk of development or progression of retinopathy, nephropathy, and neuropathy (all P-values <0.0001). Poisson regressions indicated that TIR predicted retinopathy and microalbuminuria similarly to the original glycated hemoglobin data. Conclusions: The landmark DCCT was revisited using contemporary data science methods, which allowed adding individual CGM traces to the original data. Fourteen-day CGM metrics predicted microvascular diabetes complications similarly to glycated hemoglobin. Clinical Trials Registration: Not a clinical trial.

Diffusion-based generation of gene regulatory network from scRNA-seq data with DigNet.

Gene regulatory network (GRN) intricately encodes the interconnectedness of identities and functionalities of genes within cells, ultimately shaping to cellular specificity. Despite decades of endeavors, reverse engineering of GRN from gene expression profiling data remains a profound challenge, particularly when it comes to reconstructing cell specific GRN that are tailored to precise cellular and genetic contexts. For alternatively approaching network reconstruction from data, we propose a discrete diffusion generation model, called DigNet, capable of generating corresponding GRN from high-throughput single-cell RNA sequencing (scRNA-seq) data. DigNet embeds the network generation process into a multi-step recovery procedure with Markov properties. Each intermediate step has a specific model to recover a portion of the gene regulatory architectures. It thus can ensure compatibility between global network structures and regulatory modules through the unique multi-step diffusion procedure. Furthermore, through the meta-cell integration and non-Euclidean discrete space modeling, DigNet can robustly be resistant to the noise of scRNA-seq data and the sparsity of GRN. Benchmark evaluation results against dozens of state-of-the-art network inference methods demonstrate that DigNet achieves superior performances across various single-cell GRN reconstruction experiments. Furthermore, DigNet provides unique insights into the immune response in breast cancer, derived from differential gene regulations identified in T cells. As an open-source software, DigNet offers a powerful and effective tool for generating cell specific GRN from scRNA-seq data.

Bicontinuous Nanoparticles from Spontaneous Self-Assembly of Block Copolymer Prodrug in Aqueous Medium for Potential Cancer Therapy.

Despite having several advantages, bicontinuously structured polymeric nanoparticles (BSPNPs) are far less explored in the field of controlled drug delivery owing to the requirement of complex precursor copolymers and the associated multistep synthetic procedures. In this work, we report the synthesis of a redox-sensitive diblock copolymer (P1), which was subsequently utilized to prepare doxorubicin (DOX) containing a pH-labile prodrug (P2). P1 and P2 spontaneously self-assembled in aqueous media above their critical aggregation concentration, forming micellar nanoparticles with rare bicontinuous morphology that promotes loading of both hydrophobic and hydrophilic cargoes in different compartments. To the best of our knowledge, the formation of BSPNPs through direct self-assembly in aqueous media has not yet been reported. In vitro cellular studies asserted the higher safety profile of the nanoparticles against noncancerous cells (HEK293T) than free DOX, whereas they displayed higher drug-induced cytotoxicity against cancer cells (MCF-7) in comparison to free DOX, establishing them as promising cancer drug delivery systems.

Rapid and scalable ruthenium catalyzed meta-C–H alkylation enabled by resonant acoustic mixing

Synthetic chemistry approaches for direct C–H bond alkylation offers a promising alternative to traditional functional-group-centered strategies which often involve multi-step procedures and may suffer from a variety of challenges including scalability. Here, we introduce resonant mixing as an efficient method for meta-C–H alkylation of arenes using a Ru-catalyst, avoiding the need for bulk solvents, external temperature, or light. The described methodology is highly rapid, enabling multigram-scale synthesis of meta-alkylation products within a short reaction time and achieving a very high turnover frequency. The reaction operates via a radical mechanism and is characterized by its mild reaction conditions, substrate compatibility, and exceptional meta-selectivity, all while significantly reducing reaction times.

Favorable Symmetric Structures of Radiopharmaceutically Important Neutral Cyclen-Based Ligands

Cyclen-based ligands are prominent tools for transferring radioisotopes through the human body. A crucial criterion is the stability of their complexes, which is partly determined by the stabilization of the free ligand in solution. For the assessment of the later property, the favored conformation(s) in the solution must be known. In the present study, the conformational space of four neutral cyclen-based ligands was elucidated by a multi-step procedure: the survey of the conformational space using molecular mechanics (MM) was followed by Density Functional Theory (DFT) calculations on the low-energy conformers and evaluation of the solvent effects. The results revealed several low-energy conformers in aqueous solution. In terms of electronic energies, a significant preference of symmetric structures (C4 or C2—similar to the ligand arrangements in their metal complexes) was obtained. The thermal contributions to the Gibbs free energy (mainly the vibrational ones) tend to decrease this preference by several kJ/mol against non-symmetric structures. Nonetheless, the advantage of compact symmetric structures was confirmed in all the four studied cases.

FunBIalign: a hierachical algorithm for functional motif discovery based on mean squared residue scores

Motif discovery is gaining increasing attention in the domain of functional data analysis. Functional motifs are typical “shapes” or “patterns” that recur multiple times in different portions of a single curve and/or in misaligned portions of multiple curves. In this paper, we define functional motifs using an additive model and we propose funBIalign for their discovery and evaluation. Inspired by clustering and biclustering techniques, funBIalign is a multi-step procedure which uses agglomerative hierarchical clustering with complete linkage and a functional distance based on mean squared residue scores to discover functional motifs, both in a single curve (e.g., time series) and in a set of curves. We assess its performance and compare it to other recent methods through extensive simulations. Moreover, we use funBIalign for discovering motifs in two real-data case studies; one on food price inflation and one on temperature changes.

Fine Tuning the Glass Transition Temperature and Crystallinity by Varying the Thiophene-Quinoxaline Copolymer Composition.

In recent years, the design and synthesis of high-performing conjugated materials for the application in organic photovoltaics (OPVs) have achieved lab-scale devices with high power conversion efficiency. However, most of the high-performing materials are still synthesised using complex multistep procedures, resulting in high cost. For the upscaling of OPVs, it is also important to focus on conjugated polymers that can be made via fewer simple synthetic steps. Therefore, an easily synthesised amorphous thiophene-quinoxaline donor polymer, TQ1, has attracted our attention. An analogue, TQ-EH that has the same polymer backbone as TQ1 but with short branched side-chains, was previously reported as a donor polymer with increased crystallinity. We have synthesised copolymers with varied ratios between octyloxy and branched (2-ethylhexyl)oxy-substituted quinoxaline units having the same polymer backbone, with the aim to control the aggregation/crystallisation behaviour of the resulting copolymers. The optical properties, glass transition temperatures and degree of crystallinity of the new copolymers were systematically examined in relation to their copolymer composition, revealing that the composition can be used to fine-tune these properties of conjugated polymers. In addition, multiple sub-Tg transitions were found from some of the polymers, which are not commonly or clearly seen in other conjugated polymers. The new copolymers were tested in photovoltaic devices with a fullerene derivative as the acceptor, achieving slightly higher performances compared to the homopolymers. This work demonstrates that side-chain modification by copolymerisation can fine-tune the properties of conjugated polymers without requiring complex organic synthesis, thereby expanding the number of easily synthesised polymers for future upscaling of OPVs.

Creating a Psychological Test in a Few Seconds

Abstract: The development of Situational Judgment Tests (SJTs) is a multi-step procedure that often requires substantial resources and time. ChatGPT as a large language model has already proven helpful for many complex tasks, including the development of psychological questionnaires. However, ChatGPT has so far not been tested for its ability to develop SJT items, which could speed up SJT development and thus contribute to the dissemination of psychometrically well-designed SJTs in practice and also support research requiring specific SJTs or SJT versions. Thus, the current study ( N = 419) examined whether ChatGPT (2023, Feb 13 version using ChatGPT-3.5) can create SJT items, assessing a facet of personality (i.e., gregariousness), that show similar psychometric properties (examined through confirmatory factor, reliability, and correlational analyses) as human-created SJT items. Results revealed that the measurement model for all eleven SJT items that were created by ChatGPT showed a poor model fit, while a reduced set of eight items yielded an acceptable fit. Reliability estimates as well as convergent and discriminant validity evidence were similar for the human-created and the ChatGPT-created SJT. Implications as well as potential boundary conditions of SJT development through ChatGPT are discussed.

One-pot MCDA-CRISPR-Cas-based detection platform for point-of-care testing of severe acute respiratory syndrome coronavirus 2.

Compared to quantitative real-time PCR (q-PCR), CRISPR-Cas-mediated technology is more suitable for point-of-care testing (POCT) and has potential for wider application in the future. Generally, the operational procedure of CRISPR-Cas-mediated diagnostic method consists of two independent steps, the reaction of signal amplification and the CRISPR-Cas-mediated signal detection. Complex multi-step procedures can easily lead to cross-contamination. To develop a convenient and rapid method for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) detection, we propose a MCTOP method (Multiple cross displacement amplification-CRISPR-Cas12b-based testing in one-pot), which targets the open reading frame 1ab (ORF1ab) and nucleocapsid protein (N) gene of SARS-CoV-2. This method combines MCDA isothermal amplification and CRISPR-Cas-mediated sequence-specific detection into a one-pot reaction. The optimal reaction was achieved with isothermal amplification of 40 min and CRISPR-Cas-based detection of 15 min, both at 64°C. Then, the results can be visualized by the real-time fluorescence instrument and also lateral flow biosensor. The lowest detection limit of the proposed method is 10 copies of each of target sequences, and it has no cross-reactivity with non-SARS-CoV-2 templates. In a clinical test of 70 pharyngeal swab samples, MCTOP assay showed a specificity of 100% and sensitivities of 98 and 96% for the real-time fluorescence instrument and lateral flow biosensor, respectively. The MCTOP developed in this study is a rapid, convenient, highly sensitive, and specific method for SARS-CoV-2 nucleic acid detection. It can be used as an effective point-of-care testing (POCT) tool for clinical diagnosis and epidemiologic surveillance of SARS-CoV-2 infections, especially suitable for the basic, field and clinical laboratory.

H-infinity consensus control of fractional-order multi-agent systems

This paper considers the H-infinity consensus problem of fractional-order multi-agent systems (FOMASs) with external disturbances ( 0 < α < 1 ). Using matrix spectral decomposition and the algebra properties of the Kronecker product, the H-infinity consensus problem in FOMASs is converted to an H-infinity problem for a group of fractional-order systems (FOSs). Based on the bounded real lemma of H-infinity stability for FOS in term of linear matrix inequalities (LMIs), a necessary and sufficient condition for the existence of an H-infinity controller achieving consensus in FOMASs is derived. A multi-step procedure for constructing controller is further presented. In the simulation part of the paper, the viability of the proposed method is demonstrated by a simulation example. A comparative analysis of the performance of controllers designed based on integer-order models and fractional-order models is subsequently conducted to elucidate the distinct advantages of method in this paper.

Novel Pyrimidine Tethered Benzamide Derivatives as Potential Anticancer Agents: Synthesis, Characterization, Molecular Docking and In vitro Cytotoxicity Evaluation

Current research work presents the synthesis, characterization, molecular docking study and in vitro cytotoxicity evaluation of novel pyrimidine-tethered benzamide derivatives as potential anticancer agents. The synthesis involved multi-steps procedure, including the synthesis of various chalcones (3a-t) with corresponding ketones (1 and 2) and substituted aldehydes (a-j), followed by pyrimidine amines (5a-t) with condensation of chalcones and guanidine and finally pyrimidinyl benzamide derivatives (8a-t) from compounds 5a-t coupling with acid chloride (7) using DIBAL-H. Synthesized compounds characterized by NMR spectroscopy and HRMS. The molecular docking studies were conducted against EGFR (6LUD) and CDK-4 (7SJ3) receptors, revealing distinct binding affinities influenced by substituent groups. Additionally, the cytotoxicity of the synthesized compounds was evaluated using the MTT assay against various cancer cell lines, including A-549 (non-small cell lung cancer), HCT-116 (colorectal cancer), PANC-1 (pancreatic cancer) and HaLa (cervical cancer), along with one normal human embryonic kidney cell line (HEK-293). Among the synthesized compounds, derivatives 8f and 8j exhibited the best anticancer activity against A-549 cells, compounds 8j and 8e showed exceptional potency against HCT-116 cells, compounds 8f and 8j demonstrated high efficacy against PANC-1 cells and compound 8h displayed remarkable potency against HaLa cells. The findings highlight the potential of these pyrimidinyl benzamide derivatives as targeted anticancer agents.

BOATSv2: new ecological and economic features improve simulations of high seas catch and effort

Abstract. Climate change and industrial fishing are having profound effects on marine ecosystems. Numerical models of fish communities and their interaction with fishing can help assess the biogeochemical and socioeconomic dynamics of this coupled human–natural system and how it is changing. However, existing models have significant biases and do not include many processes known to be relevant. Here we describe an updated version of the BiOeconomic mArine Trophic Size-spectrum (BOATS) model for global fish and fishery studies. The model incorporates new ecological and economic features designed to ameliorate prior biases. Recent improvements include reduction of fish growth rates in iron-limited high-nutrient low-chlorophyll regions and the ability to simulate fishery management. Features added to BOATS here for the first time include (1) a separation of pelagic and demersal fish communities to provide an expanded representation of ecological diversity and (2) spatial variation of fishing costs and catchability for more realistic fishing effort dynamics. We also introduce a new set of observational diagnostics designed to evaluate the model beyond the boundary of large marine ecosystems (66 commonly adopted coastal ocean ecoregions). Following a multi-step parameter selection procedure, the updated BOATSv2 model shows comparable performance to the original model in coastal ecosystems, accurately simulating catch, biomass, and fishing effort, and markedly improves the representation of fisheries in the high seas, correcting for excessive high seas and deep-sea catches in the previous version. Improvements mainly stem from separating pelagic and demersal energy pathways, complemented by spatially variable catchability of pelagic fish and depth- and distance-dependent fishing costs. The updated model code is available for simulating both historical and future scenarios.

Arene Ring Expansion by Ruthenium η6‐Arene Complexes

AbstractTransition metal π‐arene complexes enable the dearomatization of benzene rings to access diversified unsaturated carbocycles through multistep synthetic procedures involving sequential addition of nucleophiles and electrophiles. This work details a single‐step dearomatization process by reaction of Ru(η6‐arene) complexes with enolates derived from α‐halo or α‐(tosyloxy)esters to directly transform π‐coordinated arenes to ring‐expanded cycloheptatrienes.

Arene Ring Expansion by Ruthenium η6-Arene Complexes.

Transition metal π-arene complexes enable the dearomatization of benzene rings to access diversified unsaturated carbocycles through multistep synthetic procedures involving sequential addition of nucleophiles and electrophiles. This work details a single-step dearomatization process by reaction of Ru(η6-arene) complexes with enolates derived from α-halo or α-(tosyloxy)esters to directly transform π-coordinated arenes to ring-expanded cycloheptatrienes.

All-in-one multiple extracellular vesicle miRNA detection on a miniaturized digital microfluidic workstation

Extracellular vesicles (EVs) and EV-derived microRNAs (EV-miRNAs) are emerging as promising circulating biomarkers for early detection of malignant tumors such as non-small cell lung cancer (NSCLC). However, utilization of the gold standard method of RNA detection, the reverse transcription - quantitative polymerase chain reaction (RT-qPCR), on EV-miRNAs is hindered by laborious sample purification requirements and time-consuming multi-step procedures. Herein, we propose and demonstrate a miniaturized digital microfluidic (DMF) workstation for all-in-one EV-miRNA detection based on RT-qPCR. In comparison with the previously reported DMF platform for EV isolation, the system further integrates parallel on-chip real-time PCR capability with a comparable detection sensitivity with in-vitro RT-qPCR (limit of detection = 2 copies/μL), realizing automated, miniaturized, and facile EV-miRNA detection. Meanwhile, major methodological improvements were made, including one-step stem-looped RT-qPCR for miRNAs with both high sensitivity and specificity, and a simplified DMF substrate rework strategy for cost-effectiveness. As a demonstration, the detection of NSCLC-related EV-miRNAs within 20 μL of plasma samples was implemented, indicating the potential applicability of the DMF workstation and its automated protocol on point-of-care diagnosis of a wide range of diseases.

Understanding the Dynamics of Cobalt Nitride-Based Thin Films for Electrocatalytic Oxygen Reduction

In both acidic and alkaline hydrogen fuel cells, platinum is the benchmark cathode catalyst, exhibiting high activity for the oxygen reduction reaction (ORR). Despite platinum’s exceptional performance, its low abundance and high cost are major factors limiting the widespread adoption of hydrogen fuel cells—a critical technology for the decarbonization of our energy systems. Cobalt nitrides (CoNx) along with cobalt-nitrogen-carbon macrocyclic compounds (Co-N-C) have emerged in literature as a promising subset of active, low-cost ORR catalysts in both acidic and alkaline media. However, the most active of these materials are carbon-supported nanoparticles or single atom catalysts with complex, non-uniform active sites, making it difficult to disentangle the impact of each element within the catalyst (Co, N, C, etc.) on performance. Additionally, the pH- and potential-dependent material stability of these materials is not well-understood.To broaden our fundamental understanding of the activity and dynamics of non-precious ORR electrocatalysts, we have devised a synthesis procedure for well-defined metallic and bimetallic X-ide (nitride, oxide, sulfide, etc.) thin film catalysts. Following a two-step procedure, we have used physical vapor deposition followed by thermal ammonia treatments to form CoxSbyNz films of various compositions. To develop a holistic understanding of the factors influencing ORR electrocatalysis on these materials, we utilize ex situ characterization along with in situ techniques to probe time- and potential-dependent material stability and selectivity during catalysis. With depth-profiling x-ray photoelectron spectroscopy, we observe uniform incorporation of N at approximately 10 atomic percent throughout the NH3-treated films below a native oxide layer. X-ray diffraction indicates that untreated films have a hexagonal close packed structure matching a Co standard, and the NH3-treated films have a hexagonal structure similar to Co3N.To understand catalyst dynamics in situ, we use on-line inductively coupled plasma mass spectrometry (ICP-MS) to measure Co and Sb dissolution, electrochemical mass spectrometry (EC-MS) to probe nitride degradation, and a rotating ring disk electrode (RRDE) to monitor peroxide selectivity. With each of these in situ techniques, we couple cyclic voltammetry experiments, demonstrating that activity and stability are dependent on catalyst composition, electrolyte pH, and the upper and lower potential limits. We find that the stability of CoxSbyNz is limited by Co dissolution in acidic electrolyte and nitride degradation in alkaline electrolyte. The individual incorporation of Sb and N help to prevent the anodic dissolution of cobalt in acidic media, and the metastable incorporation of N improves activity at both pH extremes. Although it is often too difficult to deconvolute the role of individual elements within highly active non-oxide electrocatalysts, our multistep synthesis procedure and comprehensive in situ and ex situ characterization techniques allow us to probe the impact of each element in various CoxSbyNz films on material stability and product-specific activity. Our results impart wisdom that will be useful in the development of low-cost, high-performance electrocatalysts across a broad set of reactions and applications.

An improved calibration procedure for accurate plastic scintillation dosimetry on an MR-linac.

Plastic scintillation dosimeters (PSDs) are highly suitable for real-time dosimetry on the MR-linac. For optimal performance, the primary signal (scintillation) needs to be separated from secondary optical effects (Cerenkov, fluorescence and optical fiber attenuation). This requires a spectral separation approach and careful calibration. Currently, the 'classic' calibration is a multi-step procedure using both kV and MV X-ray sources, requiring an uninterrupted optical connection between the dosimeter and read-out system, complicating efficient use of PSDs. Therefore, we present a more time-efficient and more practical novel calibration technique for PSDs suitable for MR-linac dosimetry.&#xD;Approach: The novel calibration relies on prior spectral information combined with two 10x10 cm2field irradiations on the 1.5 T MR-linac. Performance of the novel calibration technique was evaluated focusing on its reproducibility, performance characteristics (repeatability, linearity, dose rate dependency, output factors, angular response and detector angle dependency) and IMRT deliveries. To investigate the calibration stability over time, prior spectral information up to 315 days old was used. To quantify the time efficiency, each step of the novel and classic calibration was timed.&#xD;Main results: The novel calibration showed a high reproducibility with a maximum relative standard deviation of 0.3%. The novel method showed maximum differences of 1.2% compared to the gold-standard calibration, while reusing old classic calibrations after reconnecting fibers showed differences up to 3.0%. The novel calibration improved time efficiency from 105 to 30 minutes compared to the classic method.&#xD;Significance: The novel calibration method showed a gain in time efficiency and practicality while preserving the dosimetric accuracy. Therefore, this method can replace the traditional method for PSDs suitable for MR-linac dosimetry, using prior spectral information of up to a year. This novel calibration facilitates reconnecting the detector to the read-out system which would lead to unacceptable dosimetric results with the classic calibration method.

Synthesis, characterization and computational studies of a series of the thiazole-thiazolidinone hybrids

A multi-step synthetic procedure yielded novel polycyclic thiazole-thiazolidinone hybrids 3-(4-(4–4-chlorobenzyloxyphenyl)thiazol-2-yl)-2-(para-nitrophenyl)thiazolidin-4-one (4a), 3-(4-(4–4-chlorobenzyloxyphenyl)thiazol-2-yl)-2-(para-bromophenyl)thiazolidin-4-one (4b), 3-(4-(4–4-chlorobenzyloxyphenyl)thiazol-2-yl)-2-(para-methoxyphenyl)thiazolidin-4-one (4c), 3-(4-(4–4-chlorobenzyloxyphenyl)thiazol-2-yl)-2-(ortho-fluorophenyl)thiazolidin-4-one (4d) and 3-(4-(4–4-chlorobenzyloxyphenyl)thiazol-2-yl)-2-(meta-nitrophenyl)thiazolidin-4-one (4e). The DFT/B3LYP/6–31+G(d,p) based calculations in water were applied to study 4a–e and their corresponding protonated forms [4a–e + H]+. The cationic forms were considered as the most reliable species formed upon corrosion inhibition under acidic conditions as well as a salt-like drug form, while the aqueous medium mimics both the corrosion and the dominant component of body fluids. Compounds 4a–e as well as their protonated forms were found to be strong electrophiles. Molecules 4a–e as well as their protonated forms were probed in silico as potential inhibitors of corrosion for as a set of metals, which are the most abundant components of implants in biomedicine. The ADMET properties of compounds 4a–e as well as [4a–e + H]+ were computed. All the reported herein compounds were probed in silico as potential inhibitors of the Nonstructural protein 14 (Nsp14_N7-MTase), which is one of the most crucial proteins for the SARS-CoV-2 virus since this protein is of importance for viral replication and transcription. All the discussed compounds 4a–e as well as their protonated forms are highly active toward the applied protein, with the best binding energy found for compound 4c and the protonated form of 4b. All protein–ligand complexes are characterized by the parameters typical for a Hit, and complexes Nsp14_N7-MTase–4c and Nsp14_N7-MTase–[4b + H]+ are characterized by the parameters typical for a drug.