High-throughput Screening Strategy Research Articles

Fascinating Electrocatalysts with Dispersed Di-Metals in MN3 -M'N4 Moiety as Two Active Sites Separately for N2 and CO2 Reduction Reactions and Jointly for CN Coupling and Urea Production.

The idealized urea electrocatalyst is crucial to boost the CN coupling reaction and simultaneously suppress their isolated reduction process after adsorbing N2 and CO2 molecules. Therefore, the dispersed MN3 -M'N4 moiety is investigated systematically, including 26 homonuclear and 650 heteronuclear di-metal systems. After, 205 stable systems are selected using lowest-energy principle and ab initio molecular dynamics simulations. According to three possible pathways, NCON, CO, and OCOH to produce urea, a five-step high-throughput screening method for excellent catalytic activity and a five-aspect high-throughput screening strategy for outstanding catalytic selectivity are proposed, respectively. The potential determined steps and the limiting potential through three pathways are identified. The data indicates both CO pathway and OCOH pathway are more competitive at lower Gibbs free energy. Significantly, the most favorite RuN3 -CoN4 combination possesses an extremely low limiting potential of -0.80V for urea production, meanwhile it exists a strong foundation for experimental preparation. This work not only broadens electrocatalytic potentiality of developing di-metals as two active sites, but also provides a feasible high-throughput screening recipe for urea production.

Adaptive evolutionary strategy coupled with an optimized biosynthesis process for the efficient production of pyrroloquinoline quinone from methanol

BackgroundPyrroloquinoline quinone (PQQ), a cofactor for bacterial dehydrogenases, is associated with biological processes such as mitochondriogenesis, reproduction, growth, and aging. Due to the extremely high cost of chemical synthesis and low yield of microbial synthesis, the election of effective strains and the development of dynamic fermentation strategies for enhancing PQQ production are meaningful movements to meet the large-scale industrial requirements.ResultsA high-titer PQQ-producing mutant strain, Hyphomicrobium denitrificans FJNU-A26, was obtained by integrating ARTP (atmospheric and room‑temperature plasma) mutagenesis, adaptive laboratory evolution and high-throughput screening strategies. Afterward, the systematic optimization of the fermentation medium was conducted using a one-factor-at-a-time strategy and response surface methodology to increase the PQQ concentration from 1.02 to 1.37 g/L. The transcriptional analysis using qRT-PCR revealed that the expression of genes involved in PQQ biosynthesis were significantly upregulated when the ARTP-ALE-derived mutant was applied. Furthermore, a novel two-stage pH control strategy was introduced to address the inconsistent effects of the pH value on cell growth and PQQ production. These combined strategies led to a 148% increase in the PQQ concentration compared with that of the initial strain FJNU-6, reaching 1.52 g/L with a yield of 40.3 mg/g DCW after 144 h of fed-batch fermentation in a 5-L fermenter.ConclusionThe characteristics above suggest that FJNU-A26 represents an effective candidate as an industrial PQQ producer, and the integrated strategies can be readily extended to other microorganisms for the large-scale production of PQQ.

Rapid detection of traditional Chinese medicine with neuraminidase inhibitory activities based on high-throughput and virtual screening strategy

BackgroundOseltamivir, a neuraminidase inhibitor (NAI), is the primary and first-line anti-influenza drug. In recent years, more and more oseltamivir resistant strains appeared frequently. PurposeTo identify anti-influenza candidates to overcome oseltamivir resistance from traditional Chinese medicine. MethodsHigh-throughput screening platform was used to screen candidates with oseltamivir sensitive and resistant neuraminidase (NA) inhibitory activities from traditional Chinese medicine (TCM) and in validation of potential active components in vitro. Molecular docking is used for virtual screening of TCM compound libraries and analysis of the molecular mechanism of active compounds. ResultsSix out of 188 TCM formula granules were screened out with good inhibitory activities in oseltamivir sensitive (H5N1) and resistant NA (H274Y mutant). A compound library containing 679 candidates in the above six plants were docked into binding pockets of H5N1 NA and its H274Y mutant. The results indicated that diverse structural types, including flavonoids glycosides, alkaloids, caffeoylquinic acid derivatives, etc. showed good docking scores on the two NAs. Furthermore, 10 representative compounds tested in vitro showed inhibitory activities in both oseltamivir sensitive and resistant NA. The docking simulations revealed that natural TCM molecules bind with NA in an absolute different mode compared with oseltamivir, which occupy the extra cavities adjacent to the active site of NA, thus might make them non-susceptible to be influenced by mutation around the active sites. ConclusionsThe combination of high-throughput screening and molecular docking can efficiently screen out candidates that have the potential to overcome oseltamivir resistance from TCM.

High-Throughput Screening of Metal–Organic Frameworks Assisted by Machine Learning: Propane/Propylene Separation

The separation of a propane (C3H8)/propylene(C3H6) mixture is of paramount importance in the petrochemical industry. Metal–organic frameworks (MOFs), as a class of promising alternative to the traditional adsorbents, have garnered extensive interest. This study proposes a machine learning-assisted high-throughput screening strategy for the identification of suitable MOFs for C3H8/C3H6 separation, striving to accelerate the discovery of high-performance MOF candidates for this particular application. First, a chemical/geometric analysis-based prescreening is applied to a data set of 146 203 MOFs composed of an experimentally synthesized MOF database and a hypothetical MOF database, and MOFs with undesirable chemical/geometric features were excluded. Six structural and nine chemical descriptors were calculated for the remaining MOFs. Random Forest regression algorithm was applied to “learn” the relationship correlations between the features (chemical and/or structural) of MOFs and their C3H8/C3H6 separation capacity. Grand Canonical Monte Carlo (GCMC) simulations were applied to evaluate the C3H8/C3H6 separation performances of the randomly selected training and testing MOF samples. A performance prediction model based on chemical and structural descriptors was obtained with R2 equal to 0.96, which was employed for a separation performance prediction of the remaining MOFs. 2500 MOFs with potential to possess high C3H8/C3H6 separation performance were shortlisted by the prediction model. GCMC simulations were applied to calibrate the prediction results and validate of the machine learning model. MOFs with competitively high C3H8/C3H6 separation potential and regenerability were identified, and a comparison with MOFs reported in the literature was made, indicating that the proposed machine learning-assisted high-throughput screening approach is efficient and effective. Furthermore, structure–property correlation analysis was conducted.

Accelerating high-throughput screening of hydrogen peroxide productionviaDFT and machine learning

A high-throughput screening strategy, integrating DFT and machine learning, was developed for electrocatalysts in ORR to produce H2O2.

Immunohistochemistry (IHC) staining of in-vitro cancer cell-generated tumoroids

Targeting different pathways in combinational therapy may lead to synergistic effects with higher drug efficiency. Due to a large number of candidate drugs and the variability in the genomic landscape of the disease, conventional cell culture models have limited success. Three-dimensional (3D) cell culture platforms such as tumoroids not only provide a pathophysiological relevant condition but also allow for low-cost and high-throughput drug screening strategies. Immunostaining of targeted proteins within a tumoroid is challenging as the interior cells are difficult to access via a non-destructive method. Immunohistochemistry (IHC) is an important technique in clinical research to explore the expression of various biomarkers. IHC staining of tumoroids allows non-destructive detection of unstable proteins by direct fixation of cells at the state of tumor microenvironment (TME) context, providing two main advantages. First, the target protein can be fixed without dissociating cells and disintegration of tumoroids into a single-cell suspension. Second, staining the preserved structure of tumoroids helps identify the location of the target proteins as well as the spatial distribution throughout the tumoroid geometry. In this protocol, we describe the detailed methodology of a non-destructive IHC staining of cancer biomarkers which minimizes the manipulation of tumoroids prior to fixation by eliminating multiple centrifugations and shaking steps typically required for removing excess hydrogel and collecting tumoroids. The protocol can be used in studies involving prognostic and predictive biomarker investigations in new anti-tumor drug development strategies.

Chemical genetic approaches for the discovery of bacterial cell wall inhibitors.

Antimicrobial resistance (AMR) in bacterial pathogens is a worldwide health issue. The innovation gap in discovering new antibiotics has remained a significant hurdle in combating the AMR problem. Currently, antibiotics target various vital components of the bacterial cell envelope, nucleic acid and protein biosynthesis machinery and metabolic pathways essential for bacterial survival. The critical role of the bacterial cell envelope in cell morphogenesis and integrity makes it an attractive drug target. While a significant number of in-clinic antibiotics target peptidoglycan biosynthesis, several components of the bacterial cell envelope have been overlooked. This review focuses on various antibacterial targets in the bacterial cell wall and the strategies employed to find their novel inhibitors. This review will further elaborate on combining forward and reverse chemical genetic approaches to discover antibacterials that target the bacterial cell envelope.

Multiple mitochondria-targeted components screened from Sini decoction improved cardiac energetics and mitochondrial dysfunction to attenuate doxorubicin-induced cardiomyopathy.

Rationale: Sini decoction (SND) is an efficient formula against DOX-induced cardiomyopathy (DCM), but the active ingredient combination (AIC) and mechanisms of SND remain unclear. Therefore, the present study aimed to identify the AIC and elucidate the underlying mechanism of AIC on DCM. Methods: The AIC were screened by a novel comprehensive two-dimensional cardiac mitochondrial membrane chromatography (CMMC)-TOFMS analysis system and further validated by cell viability, reactive oxygen species (ROS) generation, ATP level, and mitochondrial membrane potential in DOX-induced H9c2 cell injury model. Then, an integrated model of cardiac mitochondrial metabolomics and proteomics were applied to clarify the underlying mechanism in vitro. Results: The CMMC column lifespan was significantly improved to more than 10 days. Songorine (S), neoline, talatizamine, 8-gingerol (G) and isoliquiritigenin (I), exhibiting stronger retention on the first-dimension CMMC column, were screened to have protective effects against DOX cardiotoxicity in the H9c2 cell model. S, G and I were selected as an AIC from SND according to the bioactivity evaluation and the compatibility theory of SND. The combined in vitro use of S, G and I produced more profound therapeutic effects than any component used individually on increasing ATP levels and mitochondrial membrane potential and suppressing intracellular ROS production. Moreover, SGI attenuated DCM might via regulating mitochondrial energy metabolism and mitochondrial dysfunction. Conclusions: The provided scientific evidence to support that SGI combination from SND could be used as a prebiotic agent for DCM. Importantly, the proposed two-dimensional CMMC-TOFMS analytical system provides a high-throughput screening strategy for mitochondria-targeted compounds from natural products, which could be applied to other subcellular organelle models for drug discovery.

CRISPRi-microfluidics screening enables genome-scale target identification for high-titer protein production and secretion

Genome-scale target identification promises to guide microbial cell factory engineering for higher-titer production of biomolecules such as recombinant proteins (r-protein), but challenges remain due to the need not only for comprehensive genotypic perturbation but also in conjunction with high-throughput phenotypic screening strategies. Here, we developed a CRISPRi-microfluidics screening platform to systematically identify crucial gene targets that can be engineered to enhance r-protein secretion in Corynebacterium glutamicum. We created a CRISPR interference (CRISPRi) library containing 46,549 single-guide RNAs, where we aimed to unbiasedly target all genes for repression. Meanwhile, we developed a highly efficient droplet-based microfluidics system integrating the FlAsH-tetracysteine assay that enables screening of millions of strains to identify potential knockdowns conducive to nanobody VHH secretion. Among our highest-ranking candidates are a slew of previously unknown targets involved in transmembrane transport, amino-acid metabolism and redox regulation. Guided by these findings, we eventually constructed a hyperproducer for multiple proteins via combinatorial engineering of redox-response transcription factors. As the near-universal applicability of CRISPRi technology and the FlAsH-based screening platform, this procedure might be expanded to include a varied variety of microbial species and recombinant proteins.

High-Performance Oxygen Evolution Reaction Electrocatalysts Discovered via High-Throughput Aerogel Synthesis

A combinatorial high-throughput aerogel synthesis method is developed to systematically study the synergistic catalytic effects of multiple non-noble metal elements. A large number of aerogels with different compositions were synthesized and the corresponding ternary phase diagrams of the composition–electrocatalytic performance were constructed. A batch of aerogel catalysts with outstanding catalytic performance and stability for the electrochemical oxygen evolution reaction (OER) were discovered. Amorphous Fe3Co3Ni2-(oxy)hydroxide with excellent electrocatalytic performance (Tafel slope of 49 mV dec–1 and η100 of 421.48 ± 6.41 mV) was found to achieve a mass activity of 611.23 A/gmetal at 1.63 V (vs RHE). X-ray photoemission spectroscopy (XPS) and X-ray absorption spectroscopy (XAS) studies reveal that the incorporation of a third atom (Ni and Ce) into the binary aerogels enlarges the Co–O atomic distance and reduces the average valence state of Co, which in turn speed up the oxygen evolution reaction kinetics. This work puts forward a strategy for high-throughput synthesis and screening of electrocatalysts and provides valuable data sets for future machine learning and prediction of high-performance electrocatalysts.

Machine learning driven drug repurposing strategy for identification of potential RET inhibitors against non-small cell lung cancer

Non-small cell lung cancer (NSCLC) remains the leading cause of mortality and morbidity worldwide accounting about 85% of total lung cancer cases. The receptor REarranged during Transfection (RET) plays an important role by ligand independent activation of kinase domain resulting in carcinogenesis. Presently, the treatment for RET driven NSCLC is limited to multiple kinase inhibitors. This situation necessitates the discovery of novel and potent RET specific inhibitors. Thus, we employed high throughput screening strategy to repurpose FDA approved compounds from DrugBank comprising of 2509 molecules. It is worth noting that the initial screening is accomplished with the aid of in-house machine learning model built using IC50 values corresponding to 2854 compounds obtained from BindingDB repository. A total of 497 compounds (19%) were predicted as actives by our generated model. Subsequent in silico validation process such as molecular docking, MMGBSA and density function theory analysis resulted in identification of two lead compounds named DB09313 and DB00471. The simulation study highlights the potency of DB00471 (Montelukast) as potential RET inhibitor among the investigated compounds. In the end, the half-minimal inhibitory activity of montelukast was also predicted against RET protein expressing LC-2/ad cell lines demonstrated significant anticancer activity. Collective analysis from our study highlights that montelukast could be a promising candidate for the management of RET specific NSCLC.

Engineering an SspB-mediated degron for novel controllable protein degradation

Elegant controllable protein degradation tools have great applications in metabolic engineering and synthetic biology designs. SspB-mediated ClpXP proteolysis system is well characterized, and SspB acts as an adaptor tethering ssrA-tagged substrates to the ClpXP protease. This degron was applied in metabolism optimization, but the efficiency was barely satisfactory. Limited high-quality tools are available for controllable protein degradation. By coupling structure-guided modeling and directed evolution, we establish state-of-the-art high-throughput screening strategies for engineering both degradation efficiency and SspB-ssrA binding specificity of this degron. The reliability of our approach is confirmed by functional validation of both SspB and ssrA mutants using fluorescence assays and metabolic engineering of itaconic acid or ferulic acid biosynthesis. Isothermal titration calorimetry analysis and molecular modeling revealed that an appropriate instead of excessively strong interaction between SspB and ssrA benefited degradation efficiency. Mutated SspB-ssrA pairs with 7–22-fold higher binding KD than the wild-type pair led to higher degradation efficiency, revealing the advantage of directed evolution over rational design in degradation efficiency optimization. Furthermore, an artificial SspB-ssrA pair exhibiting low crosstalk of interactions with the wild-type SspB-ssrA pair was also developed. Efforts in this study have demonstrated the plasticity of SspB-ssrA binding pocket for designing high-quality controllable protein degradation tools. The obtained mutated degrons enriched the tool box of metabolic engineering designs.

Accelerated discovery of novel high-performance zinc-ion battery cathode materials by combining high-throughput screening and experiments

Aqueous zinc ion batteries (AZIBs) have attracted much attention in recent years due to their high safety, low cost, and decent electrochemical performance. However, the traditional electrodes development process requires tedious synthesis and testing procedures, which reduces the efficiency of developing high-performance battery devices. Here, we proposed a high-throughput screening strategy based on first-principles calculations to aid the experimental development of high-performance spinel cathode materials for AZIBs. We obtained 14 spinel materials from 12,047 Mn/Zn-O based materials by examining their structures and whether they satisfy the basic properties of electrodes. Then their band structures and density of states, open circuit voltage and volume expansion rate, ionic diffusion coefficient and energy barrier were further evaluated by first-principles calculations, resulting in five potential candidates. One of the promising candidates identified, Mg2MnO4, was experimentally synthesized, characterized and integrated into an AZIB based cell to verify its performance as a cathode. The Mg2MnO4 cathode exhibits excellent cycling stability, which is consistent with the theoretically predicted low volume expansion. Moreover, at high current density, the Mg2MnO4 cathode still exhibits high reversible capacity and excellent rate performance, indicating that it is an excellent cathode material for AZIBs. Our work provides a new approach to accelerate the development of high-performance cathodes for AZIBs and other ion batteries.

AML-377 A “Designed” High-Throughput Drug Screening Strategy Identifies Aurora Kinase A Inhibitors as Promising Preclinical Candidates for the Treatment of NPM1-Mutated AML

Nucleophosmin (NPM1)-mutated acute myeloid leukemia (AML) accounts for one-third of adult AML and displays distinctive biological and clinical features. Despite its chemo-sensitivity and a relatively favorable prognosis, this leukemia lacks any specific therapies. Because NPM1 mutant protein is not druggable, targeting its non-oncogene addictions and its specific vulnerabilities can represent an alternative option for identifying a tailored therapy for NPM1-mutated AML. We performed a high-throughput screening (HTS) of different chemical compounds and drug libraries to find drugs or compounds 'synthetically lethal' with NPM1 mutation and/or showing more selective activity in AML with NPM1 mutation. In collaboration with the Screening Unit of Leibniz-Forschungsinstitut für Molekulare Pharmakologie (FMP; (Berlin, Germany), we performed an HTS of 38720 drugs/compounds in a 384-well format. The assay readout was cellular metabolism/viability by CellTiter Blue assay at 48 hours. The screening was properly designed in 2 steps to increase efficiency, exclude compounds with general toxicity, and identify compounds ('hits') selectively active against the only two available NPM1-mutated AML cell lines, OCI-AML3 and IMS-M2, but not against wild-type controls. Sixty-two hit compounds that met qHTS threshold criteria were rescreened in a dose-response experiment. From this analysis, we obtained 37 compounds able to selectively kill IMS-M2 (IMS-M2 %viability <50 vs. OCI-AML2 %viability >75). We focused on Aurora kinase A inhibitors (AURKAi) because 4 of the selective compounds were directed against this kinase. In the validation assay, 2 compounds (MK-5108 and MK-8745) were confirmed as more active against NPM1-mutated than NPM1-wild-type AML cells by either CyQUANT or apoptosis assay. Strikingly, we confirmed selectivity for two additional AURKAi (Alisertib and ENMD-2076, both isoform-selective inhibitors of AURKA) that were not included in the screening library. The AURKAi were also effective in reducing tumor burden in NPM1-mutated PDX cell models and provided a survival advantage compared with vehicle-treated mice (60 vs. 32 days, P=0.005). We demonstrated that using HTS can considerably shorten drug discovery time scales, leading to the identification of promising lead compounds. Our study is the first to provide preclinical evidence of the antileukemic activity of AURKAi, with particular focus on NPM1-mutated AML.

A novel high-throughput screening strategy for targeting alpha-synuclein and other long-lived proteins.

Small-molecule high-throughput screening (HTS) campaigns have frequently been used to identify lead molecules that can alter expression of disease-relevant proteins in cell-based assays. However, most cell-based HTS assays require short compound exposure periods to avoid toxicity and ensure that compounds are stable in media for the duration of the exposure. This limits the ability of HTS assays to detect inhibitors of the synthesis of target proteins with long half-lives, which can often exceed the exposure times utilized in most HTS campaigns. One such target is alpha-synuclein (α-syn)-a protein well-known for its pathological aggregation in Parkinson's Disease (PD) and other forms of neurodegeneration known collectively as synucleinopathies. Here, we report the development of an HTS assay using a CRISPR-engineered neuroblastoma cell line expressing a destabilized luciferase reporter inserted at the end of the coding region of the SNCA locus. The resultant destabilized fusion protein exhibited a significant reduction in half-life compared to the endogenous, unmodified α-syn protein, and accurately reported reductions in α-syn levels due to known protein translation inhibitors and specific α-syn siRNAs. The robustness and utility of this approach was shown by using the resulting cell line (dsLuc-Syn) to screen a focused library of 3,192 compounds for reduction of α-syn. These data demonstrate the general utility of converting endogenous loci into destabilized reporter genes capable of identifying inhibitors of gene expression of highly stable proteins even in short-term assays.

Predicting the maximum absorption wavelength of azo dyes using an interpretable machine learning strategy

The maximum absorption wavelength (λmax) is one of the most important properties of azo dyes. It is essential to obtain λmax of azo dyes for the development of new molecules in a short time. Herein, the machine learning algorithm “XGBoost” was used to establish a model for predicting λmax of azo dyes. It was found that the coefficient of determinations (R2) of leave-one-out cross-validation (LOOCV) and test set were 0.87, 0.73, respectively. According to SHapley Additive exPlanations (SHAP) analysis, the number of sulfur atoms of R2 group has a strong positive correlation with λmax. The more C–N pairs of topological distance 4 appear in R1 group, the more likely the molecular λmax is red-shifted. Further, the high-throughput screening strategy was adopted to screen out 26 azo molecules with larger λmax from nearly 20,000 virtual samples. These molecular λmax are expected to be red shifted from the 610 nm in the dataset. Our study provides a convenient way to search for azo dyes with larger λmax.

High-throughput screening of metal–organic frameworks for hydrogen purification

In industry, the separation/purification of hydrogen from methane impurity is of significant importance, especially under the background of carbon dioxide mitigation towards carbon neutrality. Adsorption separation is an effective route for this application regarding energy cost and separation efficiency, for which the selection of adsorbents is the key. This work proposes a computer-aided high-throughput screening strategy for the selection of metal–organic frameworks (MOFs) for CH4/H2 separation, striving to accelerate the practice of discovering better adsorbents for this particular application. Firstly, a prescreening was conducted on 12,020 computation-ready three-dimensional MOFs based on intuitive structure and chemical analysis, shortlisting 5,096 candidates. Secondly, a molecular simulation-assisted structure–property relationship analysis was performed based on small sampled MOFs, from which two structure criteria were derived for high-performing MOFs. Based on the criteria, a screening procedure was then carried out, narrowing the candidate MOFs down to 2,408. The rigorous molecular simulation was again applied to assess the separation performance metrics of these MOFs, covering adsorption selectivity, working capacity, adsorbent performance score, regenerability and separation potential. By analysing the simulated performance indicators, top-performing MOFs were identified and a more detailed analysis of the structure-performance relationship was carried out. Additionally, the impact of several practical factors, covering humidity, operation temperature and inlet gas composition, on the performance of the selected adsorbents in practice was explored. It was observed that the robustness of MOFs at humidity depends on their hydrophilicity and preferential adsorption sites. Meanwhile, operation temperature can be manipulated to accommodate flexible production scenarios.

High-throughput virtual screening of metal–organic frameworks for xenon recovery from exhaled anesthetic gas mixture

Xenon (Xe) is a rather precious noble gas with applications in various important areas. Yet its rarity, high cost and constrained production hindered its large-scale application in clinical anesthetic purposes. Recovery of Xe after usage is recognized as a promising way to reduce its cost as anesthetic. This study proposes a high-throughput virtual screening strategy for the selection of metal-organic frameworks (MOFs) for Xe recovery from exhaled anesthetic gas mixture, striving to accelerate the discovery of high-performing MOF candidates for this particular application. A prescreening process was performed on 12,020 computation-ready experimental (CoRE) MOFs based on intuitive geometric analysis, narrowing down to a list of 6,300 candidates. This was followed by grand canonical Monte Carlo simulations to assess the adsorption behaviours of the shortlisted MOFs for the targeted application. Performance evaluation metrics, including adsorption selectivity, working capacity, regenerability and adsorbent performance score, were calculated to evaluate the performances of the MOFs as adsorbents. Ten top-performing MOFs for Xe recovery were identified from the CoRE database; followed by a structure-performance relationship analysis of the MOFs with satisfactory regenerability. Furthermore, three design strategies covering adjusting topology, replacing metal node and changing organic linker were proposed in order to provide guidance for the development of high-performing new MOFs for Xe recovery.

High-throughput screening strategies for space-based radiation countermeasure discovery

As humanity begins to venture further into space, approaches to better protect astronauts from the hazards found in space need to be developed. One particular hazard of concern is the complex radiation that is ever present in deep space. Currently, it is unlikely enough spacecraft shielding could be launched that would provide adequate protection to astronauts during long-duration missions such as a journey to Mars and back. In an effort to identify other means of protection, prophylactic radioprotective drugs have been proposed as a potential means to reduce the biological damage caused by this radiation. Unfortunately, few radioprotectors have been approved by the FDA for usage and for those that have been developed, they protect normal cells/tissues from acute, high levels of radiation exposure such as that from oncology radiation treatments. To date, essentially no radioprotectors have been developed that specifically counteract the effects of chronic low-dose rate space radiation. This review highlights how high-throughput screening (HTS) methodologies could be implemented to identify such a radioprotective agent. Several potential target, pathway, and phenotypic assays are discussed along with potential challenges towards screening for radioprotectors. Utilizing HTS strategies such as the ones proposed here have the potential to identify new chemical scaffolds that can be developed into efficacious radioprotectors that are specifically designed to protect astronauts during deep space journeys. The overarching goal of this review is to elicit broader interest in applying drug discovery techniques, specifically HTS towards the identification of radiation countermeasures designed to be efficacious towards the biological insults likely to be encountered by astronauts on long duration voyages.

High-throughput computational screening of Covalent−Organic framework membranes for helium purification

Currently, helium purification is carried out by the heat-driven cryogenic distillation process, which is prohibitively energy-intensive and capital-intensive. As an alternative, covalent-organic frameworks (COFs)-based membrane separation technology is considered to be cost-efficient and technically feasible. In this study, a high-throughput computational screening strategy was proposed for the rapid determination of high-performing COF membranes for this particular application. A geometric analysis-based prescreening process was carried out on 688 experimentally synthesized COFs, resulting in 665 COF candidates with pore limited diameter (PLD) no less than 3.8 Å. Subsequently, molecular simulations were performed to estimate the henry coefficients and self-diffusion coefficients of N2, H2, and CH4 in each COF. Based on the results, the membrane selectivity and permeability of all the COFs were calculated, from which 23 high-performing COF membranes were identified. Additionally, the performances of these 23 COF membranes for a ternary mixture of He/N2/CH4 were computationally examined, five of which were highly preferable to permeate He over N2 or CH4. The results indicated that a large number of COF membranes exceeded the performance upper limit of polymer membranes owing to their high He permeability. Additionally, the relation between the PLD of COFs and the corresponding separation performances was investigated. Finally, He permeability, as well as He/CH4 and He/N2 selectivity of mixed matrix membranes were estimated by using the Maxwell model. The results revealed that the addition of COFs into pure polymers significantly improved the permeability of He up to 11689 Barrer.