High-throughput Screening Approach Research Articles

Enhanced Design of Kesterite Solar Cells through High-Throughput Screening and Machine Learning Approaches

Enhanced Design of Kesterite Solar Cells through High-Throughput Screening and Machine Learning Approaches

Correlating NAD(P)H lifetime shifts to tamoxifen resistance in breast cancer cells: A metabolic screening study with time-resolved flow cytometry

Time-resolved flow cytometry (TRFC) was used to measure metabolic differences in estrogen receptor-positive breast cancer cells. This specialty cytometry technique measures fluorescence lifetimes as a single-cell parameter thereby providing a unique approach for high-throughput cell counting and screening. Differences in fluorescence lifetime were detected and this was associated with sensitivity to the commonly prescribed therapeutic tamoxifen. Differences in fluorescence lifetime are attributed to the binding states of the autofluorescent metabolite NAD(P)H. The function of NAD(P)H is well described and in general involves cycling from a reduced to oxidized state to facilitate electron transport for the conversion of pyruvate to lactate. NAD(P)H fluorescence lifetimes depend on the bound or unbound state of the metabolite, which also relates to metabolic transitions between oxidative phosphorylation and glycolysis. To determine if fundamental metabolic profiles differ for cells that are sensitive to tamoxifen compared to those that are resistant, large populations of MCF-7 breast cancer cells were screened and fluorescence lifetimes were quantified. Additionally, metabolic differences associated with tamoxifen sensitivity were measured with a Seahorse HS mini metabolic analyzer (Agilent Technologies Inc. Santa Clara, CA) and confocal imaging. Results show that tamoxifen-resistant breast cancer cells have increased utilization of glycolysis for energy production compared to tamoxifen-sensitive breast cancer cells. This work is impacting because it establishes an early step toward developing a reliable screening technology in which large cell censuses can be differentiated for drug sensitivity in a label-free fashion.

Innovative Approach of High Throughput Screening in the Drug Discovery Quest for Chronic Bronchitis Treatment

Innovative Approach of High Throughput Screening in the Drug Discovery Quest for Chronic Bronchitis Treatment

Prediction of chemical-induced acute toxicity using in vitro assay data and chemical structure

Exposure to various chemicals found in the environment and in the context of drug development can cause acute toxicity. To provide an alternative to in vivo animal toxicity testing, the U.S. Tox21 consortium developed in vitro assays to test a library of approximately 10,000 drugs and environmental chemicals (Tox21 10 K compound library) in a quantitative high-throughput screening (qHTS) approach. In this study, we assessed the utility of Tox21 assay data in comparison with chemical structure information in predicting acute systemic toxicity. Prediction models were developed using four machine learning algorithms, namely Random Forest, Naïve Bayes, eXtreme Gradient Boosting, and Support Vector Machine, and their performance was assessed using the area under the receiver operating characteristic curve (AUC-ROC). The chemical structure-based models as well as the Tox21 assay data demonstrated good predictive power for acute toxicity, achieving AUC-ROC values ranging from 0.83 to 0.93 and 0.73 to 0.79, respectively. We applied the models to predict the acute toxicity potential of the compounds in the Tox21 10 K compound library, most of which were found to be non-toxic. In addition, we identified the Tox21 assays that contributed the most to acute toxicity prediction, such as acetylcholinesterase (AChE) inhibition and p53 induction. Chemical features including organophosphates and carbamates were also identified to be significantly associated with acute toxicity. In conclusion, this study underscores the utility of in vitro assay data in predicting acute toxicity.

Multiplexed Microfluidic Platform for Parallel Bacterial Chemotaxis Assays.

The sensing of and response to ambient chemical gradients by microorganisms via chemotaxis regulates many microbial processes fundamental to ecosystem function, human health, and disease. Microfluidics has emerged as an indispensable tool for the study of microbial chemotaxis, enabling precise, robust, and reproducible control of spatiotemporal chemical conditions. Previous techniques include combining laminar flow patterning and stop-flow diffusion to produce quasi-steady chemical gradients to directly probe single-cell responses or loading micro-wells to entice and ensnare chemotactic bacteria in quasi-steady chemical conditions. Such microfluidic approaches exemplify a trade-off between high spatiotemporal resolution of cell behavior and high-throughput screening of concentration-specific chemotactic responses. However, both aspects are necessary to disentangle how a diverse range of chemical compounds and concentrations mediate microbial processes such as nutrient uptake, reproduction, and chemorepulsion from toxins. Here, we present a protocol for the multiplexed chemotaxis device (MCD), a parallelized microfluidic platform for efficient, high-throughput, and high-resolution chemotaxis screening of swimming microbes across a range of chemical concentrations. The first layer of the two-layer polydimethylsiloxane (PDMS) device comprises a serial dilution network designed to produce five logarithmically diluted chemostimulus concentrations plus a control from a single chemical solution input. Laminar flow in the second device layer brings a cell suspension and buffer solution into contact with the chemostimuli solutions in each of six separate chemotaxis assays, in which microbial responses are imaged simultaneously over time. The MCD is produced via standard photography and soft lithography techniques and provides robust,repeatable chemostimulus concentrations across each assay in the device. This microfluidic platform provides a chemotaxis assay that blends high-throughput screening approaches with single-cell resolution to achieve a more comprehensive understanding of chemotaxis-mediated microbial processes. Key features • Microchannel master molds are fabricated using photolithography techniques in a clean room with a mask aligner to fabricate multilevel feature heights. • The microfluidic device is fabricated from PDMS using standard soft lithography replica molding from the master molds. • The resulting microchannel requires a one-time calibration of the driving inlet pressures, after which devices from the same master molds have robust performance. • The microfluidic platform is optimized and tested for measuring chemotaxis of swimming prokaryotes.

An AIE-based ratiometric fluorescent probe for highly selective detection of H2S in plant stress responses

Hydrogen sulfide (H2S) has emerged as a crucial signaling molecule in plant stress responses, playing a significant role in regulating various physiological and biochemical processes. In this study, we report an aggregation-induced emission (AIE)-based ratiometric fluorescent probe TPN-H2S for the highly selective detection of H2S in plant tissues. The probe exhibited excellent sensitivity and selectivity towards H2S over other analytes, enabling real-time monitoring of H2S dynamics in living cell. Furthermore, the AIE-based ratiometric probe TPN-H2S allowed for accurate quantification of H2S levels, providing valuable insights into the spatiotemporal distribution of Cys metabolism produces H2S. Importantly, the physiological pathways and signaling mechanisms of H2S production of was investigated in plant tissues under Cr and nano-plastics stress. Utilizing a high-throughput screening approach, we identified exogenous substances such as calcium chloride (CaCl2) and abscisic acid (ABA) that could induce higher level of H2S production during the stress response in plants. Overall, those findings demonstrate the potential of the AIE-based ratiometric fluorescent probe TPN-H2S as a powerful tool for unraveling the role of H2S in plant stress responses and pave the way for further exploration of H2S-mediated signaling pathways in plants.

Module-based machine learning models using sigma profiles of organic linkers to predict gaseous adsorption in metal-organic frameworks

BackgroundMetal-organic frameworks (MOFs) have drawn considerable attention for their potential in adsorption applications, such as gas separation and storage. Machine learning (ML) augmented high-throughput screening approaches have emerged as an effective strategy to expedite the materials search. Traditionally, ML models developed to predict the adsorption properties of MOFs rely on various geometrical and chemical descriptors. While these descriptors are effective, they tend to be specific to each MOF's unique structure, completely omitting the modular nature of MOFs. MethodsA new approach is proposed in this study: a modular descriptor based on the sigma profile of MOF organic linkers. These sigma profiles effectively represent the chemical environment of organic linkers. With these profiles as input features, we train extreme gradient boosting (XGBoost) models to predict the Henry's coefficient (KH) of adsorption for hydrocarbons and acid gases in MOFs. FindingsThe results show that sigma profiles enhance the prediction accuracy and emerge as the most important features for hydrocarbon gases. This study highlights the potential of sigma profiles in developing accurate ML models for identifying optimal MOF adsorbents. Such an approach could also facilitate an inverse design of MOFs with targeted properties.

Large-Scale Exploration of the LiSiPON System Using a High Throughput Experimental Method

Amorphous thin film materials in the LiPON(1) or LiSiPON(2) systems have been prepared for the first time in the early 1990s using magnetron sputtering. Since then, LiPON materials have been used as state-of-the-art solid electrolytes into all-solid-state thin film micro-batteries thanks to their outstanding properties (electrochemical stability vs Li°, wide electrochemical stability window of ~[4-0] V vs. Li+/Li0, isotropic and homogeneous medium, beneficial mechanical properties(3), very low electronic conductivity). The ionic conductivity of LiPON prepared from Li3PO4 targets remains rather moderate, reaching a maximum of 3.10-6 S.cm-1 at room temperature. However, it has recently been demonstrated that the introduction of SiO2 as a second glass former can increase this value up to 2.10-5 S.cm-1, and also that the ionic conductivity of these compounds does not increase steadily with their Li content.(4)This highlights the convoluted effects of mixed formers(5) (Si, P, B...), mixed anions(6) (O, N, S...) and Li concentration on the ionic conductivity. Nevertheless, the latter are usually observed on limited series of compositions, and barely not studied on large sets of samples, which can limit the understanding of composition-structure-conductivity relationships.In this context the aim of our work was to build a High Throughput Screening (HTS) approach to explore the LixSiyPzOuNw system as a case study (Fig.1). Indeed, HTS approaches aim to accelerate material breakthrough discovery, and to understand beyond the material mechanisms. In material science, the goal is to accelerate the discovery of new keys organic, inorganic or composite materials. It may also bring beyond ‘classical’ iterative method regarding the study of complex systems (ternary, quaternary...)(7). To do so, wide ranges of material libraries are synthesized, prior to be tested through an automated characterization workflow.Our specific approach starts with the preparation of material libraries by combinatorial synthesis using magnetron co-sputtering with tilted targets, then goes through automated and fast characterizations targeting specific properties, relevant to its application as ionic conductor.Deposition of thin films displaying composition and thickness gradients at the surface of a 4’’ silicon substrate is achieved by co-sputtering three target materials in a pure Ar or N2 atmosphere. Deposition through a shadow mask allows to discretize the continuum and to prepare a library of 76 separate LixSiyPzOuNw samples in one experiment. Then, by tuning the sputtering parameters (i.e discharge gas, gas pressure, incident power, target tilt, target-to-substrate distance,...) different compositional domains can be explored.Relevant automated characterization techniques are then applied to the material library spread on the 4’’ substrate. For the assessment of these amorphous ionic conducting films, dimensional (thickness), physical-chemical (composition & local structure) and functional properties (ionic/electronic conductivities) are studied.In this sequence, determining the composition of the thin film is a real challenge due to a number of requirements :(i) light elements analysis, especially lithium; (ii) need of localized analysis (mapping) on few mm² over large substrate (4” wafer); (iii) scarce material quantity available (~ μg) and finally (iv) fast analysis. Over lab-scale techniques available, only Laser Induced Breakdown Spectroscopy technique (LIBS) seems to fulfil all these requirements. To this purpose, LIBS is developed as a HTS mapping technique for chemical analysis. A sample calibration approach is implemented through coupling with robust chemical analysis techniques (RBS, NRA, ICP-OES, SEM-EDS).In short, this specific HTS procedure will be discussed and results about composition-structure-conductivity relationships in the LiSiPON system will be presented (Fig.2).References J. B. Bates et al., J. Power Sources. 43, 103–110 (1993).N. J. Dudney, J. B. Bates, J. D. Robertson, J. Vac. Sci. Technol. A. 11, 377–389 (1993).A. S. Westover et al., Chem. Mater. 35, 2730–2739 (2023).T. Famprikis, J. Galipaud, O. Clemens, B. Pecquenard, F. Le Cras, ACS Appl. Energy Mater. 2, 4782–4791 (2019).Y. Su et al., Phys. Status Solidi B. 254, 1600088 (2017).N. Mascaraque, J. L. G. Fierro, A. Durán, F. Muñoz, Solid State Ion. 233, 73–79 (2013).E. J. Amis, X.-D. Xiang, J.-C. Zhao, MRS Bull. 27, 295–300 (2002). Figure 1

Discovery and In Vivo Efficacy of AZ-PRMT5i-1, a Novel PRMT5 Inhibitor with High MTA Cooperativity.

PRMT5, a type 2 arginine methyltransferase, has a critical role in regulating cell growth and survival in cancer. With the aim of developing MTA-cooperative PRMT5 inhibitors suitable for MTAP-deficient cancers, herein we report our efforts to develop novel "MTA-cooperative" compounds identified through a high-throughput biochemical screening approach. Optimization of hits was achieved through structure-based design with a focus on improvement of oral drug-like properties. Bioisosteric replacement of the original thiazole guanidine headgroup, spirocyclization of the isoindolinone amide scaffold to both configurationally and conformationally lock the bioactive form, and fine-tuning of the potency, MTA cooperativity, and DMPK properties through specific substitutions of the azaindole headgroup were conducted. We have identified an orally available in vivo lead compound, 28 ("AZ-PRMT5i-1"), which shows sub-10 nM PRMT5 cell potency, >50-fold MTA cooperativity, suitable DMPK properties for oral dosing, and significant PRMT5-driven in vivo efficacy in several MTAP-deficient preclinical cancer models.

Molecular docking, MD simulation, and MMGBSA-binding free energy estimation study identify antibiotic analogs as potential antimalarials targeting housekeeping proteins of Plasmodium falciparum apicoplast

ABSTRACT Plasmodium falciparum exclusively harbours a non-photosynthetic plastid-like organelle called ‘apicoplast,’ crucial for survival. The apicoplast genome resembles chloroplasts, albeit much reduced at 35 kb. The housekeeping functions of the apicoplast are similar to prokaryotes, and several antibiotics have been reported to kill the malaria parasite by targeting its housekeeping functions. However, a significant structural difference between bacterial and apicoplast housekeeping proteins promotes the discovery of antibiotic analogs specifically targeting apicoplast proteins. In this study, we used a high-throughput virtual screening approach to analyze 80% of structurally similar antibiotic analog libraries against five protein targets, including histone-like protein (PfHU), gyrase A subunit (PfGyrA), RNA polymerase β subunit (PfRpoβ), elongation factor G (PfEF-G), and elongation factor Tu (PfEF-Tu) involved in different housekeeping functions of the apicoplast. This computational screening identified molecules with better binding affinities than those antibiotics shown to have antimalarial activities, suggesting they may be more potent antimalarial compounds. The molecular dynamics simulations study revealed stable conformational changes with strong hydrogen bonds between the protein–ligand complexes. The top-hit antibiotic analogs exhibit favourable drug-like characteristics, making them promising candidates for future clinical studies. We propose further biochemical and phenotypic testing of these antibiotic analogs to develop more effective apicoplast-targeting antimalarials.

The sea urchin embryo and the cell stress responses: new perspectives

In addition to many industrial activities that release pollutants in coastal areas, numerous human behaviors contribute to climate change, inducing global warming, which can also reshape the environmental impacts of some pollutants. Therefore, it is extremely important to develop new tools that can detect pollutants and environmental changes quickly and easily with high levels of sensitivity. The sea urchin embryo is a well-known model used worldwide in many research fields, including marine ecotoxicology, as a huge range of contaminants can affect its embryonic development with species-specific sensitivity. Morphological abnormalities are already considered biomarkers to evaluate the effects of pollutants, and, indeed, the sea urchin has long been used as one of the key species in a battery of bioassays to assess the toxicity of many pollutants and dredged sediments. At the cellular level, the molecular mechanisms activated against a stress agent constitute what is known as the “cell stress response,” analyzed here within a whole organism, namely, the sea urchin embryo. In this minireview, we have reported the available molecular biomarkers linked to morphological abnormalities and the genes affected by environmental changes and emerging pollutants, highlighting those studies that use high-throughput screening approaches to evaluate the effects of environmental conditions on sea urchin embryos.

National-scale antimicrobial resistance surveillance in wastewater: A comparative analysis of HT qPCR and metagenomic approaches

Wastewater serves as an important reservoir of antimicrobial resistance (AMR), and its surveillance can provide insights into population-level trends in AMR to inform public health policy. This study compared two common high-throughput screening approaches, namely (i) high-throughput quantitative PCR (HT qPCR), targeting 73 antimicrobial resistance genes, and (ii) metagenomic sequencing. Weekly composite samples of wastewater influent were taken from 47 wastewater treatment plants (WWTPs) across Wales, as part of a national AMR surveillance programme, alongside 4 weeks of daily wastewater effluent samples from a large municipal hospital. Metagenomic analysis provided more comprehensive resistome coverage, detecting 545 genes compared to the targeted 73 genes by HT qPCR. It further provided contextual information critical to risk assessment (i.e. potential bacterial hosts). In contrast, HT qPCR exhibited higher sensitivity, quantifying all targeted genes including those of clinical relevance present at low abundance. When limited to the HT qPCR target genes, both methods were able to reflect the spatiotemporal dynamics of the complete metagenomic resistome, distinguishing that of the hospital and the WWTPs. Both approaches revealed correlations between resistome compositional shifts and environmental variables like ammonium wastewater concentration, though differed in their interpretation of some potential influencing factors. Overall, metagenomics provides more comprehensive resistome profiling, while qPCR permits sensitive quantification of genes significant to clinical resistance. We highlight the importance of selecting appropriate methodologies aligned to surveillance aims to guide the development of effective wastewater-based AMR monitoring programmes.

High-throughput assessment of stability and mechanical properties of medium- and high-entropy carbides: Bridging empirical criteria and ab-initio calculations

This study assesses the effectiveness of empirical stability descriptors — the normalized geometric packing parameter, lattice size difference, electronegativities mismatch, and the convex hull analysis based on information on mutually dissolving components, and ab-initio calculated entropy forming ability (EFA) for high-throughput materials discovery in high-entropy ceramics. A sample containing 53,130 medium and high-entropy carbide combinations from the Materials Project database has been analyzed, comparing solid solutions selected based on empirical descriptors and EFA calculations. The suitability of various electronegativity scales is evaluated, while Automatic FLOW (AFLOW) partial occupation (AFLOW-POCC) and special quasirandom structures (SQS) calculations provide insights into enthalpies, bulk moduli, and lattice parameters. It is shown that local distortions play a role in determining the stability of high-entropy ceramics and estimated lattice parameters, Young’s modulus, fracture toughness, and Vickers hardness by computational and experimental approaches. Our analysis demonstrates the efficacy of density functional theory (DFT) approaches for high-throughput screening and highlights the need for the further exploration of the cocktail effect on high-entropy ceramics’ mechanical properties.

Construction and application of H1R ligand screening materials based on SMA stabilization and his-tag covalent immobilization of membrane proteins

The histamine H1 receptor (H1R) plays a pivotal role in allergy initiation and undergoes the necessity of devising a high-throughput screening approach centered on H1R to screen novel ligands effectively. This study suggests a method employing styrene maleic acid (SMA) extraction and His-tag covalent bonding to immobilize H1R membrane proteins, minimizing the interference of nonspecific proteins interference while preserving native protein structure and maximizing target exposure. This approach was utilized to develop a novel material for high-throughput ligand screening and implemented in cell membrane chromatography (CMC). An H1R-His-SMALPs/CMC model was established and its chromatographic performance (selectivity, specificity and lifespan) validated, demonstrating a significant enhancement in lifespan compared to previous CMC models. Subsequently, this model facilitated high-throughput screening of H1R ligands in the compound library and preliminary activity verification of potential H1R antagonists. Identification of a novel H1R antagonist laid the foundation for further development in this area.

Synthesis and Characterization of a Novel PET Tracer for Noninvasive Evaluation of FGL1 Status in Tumors.

Fibrinogen-like protein 1 (FGL1) is a potential novel immune checkpoint target for malignant tumor diagnosis and therapy. Accurate detection of FGL1 levels in tumors via noninvasive PET imaging might be beneficial for managing the disease. To achieve this, multiple FGL1-targeting peptides (FGLP) were designed, and a promising candidate, 68Ga-NOTA-FGLP2, was identified through a high-throughput screening approach using microPET imaging of 68Ga-labeled peptides. Subsequent in vitro cell experiments showed that uptake values of 68Ga-NOTA-FGLP2 in FGL1 positive Huh7 tumor cells were significantly higher than those in FGL1 negative U87 MG tumor cells. Further microPET imaging showed that the Huh7 xenografts were clearly visualized with a favorable contrast. ROI analysis showed that the uptake values of the tracer in Huh7 xenografts were 2.63 ± 0.07% ID/g at 30 min p.i.. After treatment with an excess of unlabeled FGLP2, the tumor uptake significantly decreased to 0.54 ± 0.05% ID/g at 30 min p.i.. Moreover, the uptake in U87 MG xenografts was 0.44 ± 0.06% ID/g at the same time point. The tracer was excreted mainly through the renal system. 18F-FDG PET imaging was also performed in mice bearing Huh7 and U87 MG xenografts, respectively. However, there was no significant difference in the uptake between the tumors with different FGL1 expressions. Preclinical data indicated that 68Ga-NOTA-FGLP2 might be a suitable radiotracer for in vivo noninvasive visualization of tumors with abundant expression of FGL1. Further investigation of 68Ga-NOTA-FGLP2 for tumor diagnosis and therapy is undergoing.

Coupling thermotolerance and high production of recombinant protein by CYR1N1546K mutation via cAMP signaling cascades

In recombinant protein-producing yeast strains, cells experience high production-related stresses similar to high temperatures. It is possible to increase recombinant protein production by enhancing thermotolerance, but few studies have focused on this topic. Here we aim to identify cellular regulators that can simultaneously activate thermotolerance and high yield of recombinant protein. Through screening at 46 °C, a heat-resistant Kluyveromyces marxianus (K. marxianus) strain FDHY23 is isolated. It also exhibits enhanced recombinant protein productivity at both 30 °C and high temperatures. The CYR1N1546K mutation is identified as responsible for FDHY23’s improved phenotype, characterized by weakened adenylate cyclase activity and reduced cAMP production. Introducing this mutation into the wild-type strain greatly enhances both thermotolerance and recombinant protein yields. RNA-seq analysis reveals that under high temperature and recombinant protein production conditions, CYR1 mutation-induced reduction in cAMP levels can stimulate cells to improve its energy supply system and optimize material synthesis, meanwhile enhance stress resistance, based on the altered cAMP signaling cascades. Our study provides CYR1 mutation as a novel target to overcome the bottleneck in achieving high production of recombinant proteins under high temperature conditions, and also offers a convenient approach for high-throughput screening of recombinant proteins with high yields.

Constructing and Evaluating Machine-Learned Interatomic Potentials for Li-Based Disordered Rocksalts.

Lithium-based disordered rocksalts (LDRs), which are an important class of positive electrode materials that can increase the energy density of current Li-ion batteries, represent a significantly complex chemical and configurational space for conventional density functional theory (DFT)-based high-throughput screening approaches. Notably, atom-centered machine-learned interatomic potentials (MLIPs) are a promising pathway to accurately model the potential energy surface of highly disordered chemical spaces, such as LDRs, where the performance of such MLIPs has not been rigorously explored yet. Here, we represent a comprehensive evaluation of the accuracy, transferability, and ease of training of five atom-centered MLIPs, including the artificial neural network potentials developed by the atomic energy network (AENET), the Gaussian approximation potential (GAP), the spectral neighbor analysis potential (SNAP) and its quadratic extension (qSNAP), and the moment tensor potential (MTP), in modeling a 11-component LDR chemical space. Specifically, we generate a DFT-calculated data set of 10,842 configurations of disordered LiTMO2 and TMO2 compositions, where TM = Sc, Ti, V, Cr, Mn, Fe, Co, Ni, and/or Cu. To provide a point-of-comparison on the performance of atom-centered MLIPs, we also trained the neural equivariant interatomic potential (NequIP) on a subset of our data. Importantly, we find AENET to be the best potential in terms of accuracy and transferability for energy predictions, while MTP is the best for atomic forces. While AENET is the fastest to train among the MLIPs considered at low number of epochs (300), the training time increases significantly as epochs increase (3300), with a corresponding reduction in training errors (∼60%). Note that AENET and GAP tend to overfit in small data sets, with the extent of overfitting reducing with larger data sets. Finally, we observe AENET to provide reasonable predictions of average Li-intercalation voltages in layered, single-TM LiTMO2 frameworks, compared to DFT (∼10% error on average). Our study should pave the way both for discovering novel disordered rocksalt electrodes and for modeling other configurationally complex systems, such as high-entropy ceramics and alloys.

LigandDiff: de Novo Ligand Design for 3D Transition Metal Complexes with Diffusion Models.

Transition metal complexes are a class of compounds with varied and versatile properties, making them of great technological importance. Their applications cover a wide range of fields, either as metallodrugs in medicine or as materials, catalysts, batteries, solar cells, etc. The demand for the novel design of transition metal complexes with new properties remains of great interest. However, the traditional high-throughput screening approach is inherently expensive and laborious since it depends on human expertise. Here, we present LigandDiff, a generative model for the de novo design of novel transition metal complexes. Unlike the existing methods that simply extract and combine ligands with the metal to get new complexes, LigandDiff aims at designing configurationally novel ligands from scratch, which opens new pathways for the discovery of organometallic complexes. Moreover, it overcomes the limitations of current methods, where the diversity of new complexes highly relies on the diversity of available ligands, while LigandDiff can design numerous novel ligands without human intervention. Our results indicate that LigandDiff designs unique and novel ligands under different contexts, and these generated ligands are synthetically accessible. Moreover, LigandDiff shows good transferability by generating successful ligands for any transition metal complex.

High-Throughput Multi-Principal Element Alloy Exploration Using a Novel Composition Gradient Sintering Technique

This work demonstrates the capabilities and advantages of a novel sintering technique to fabricate bulk composition gradient materials. Pressure distribution calculations were used to compare several tooling geometries for use with current-activated, pressure-assisted densification or spark plasma sintering to densify a gradient along the long dimension of the specimen. The selected rectangular tooling design retains a low aspect ratio to ensure a uniform pressure distribution during consolidation by using a side loading configuration to form the gradient along the longest dimension. Composition gradients of NixCu1−x, MoxNb1−x, and MoNbTaWHfx (x from 0 to 1) were fabricated with the tooling. The microstructure, composition, and crystal structure were characterized along the gradient in the as-sintered condition and after annealing to partially homogenize the layers. The successful fabrication of a composition gradient in a difficult-to-process material like the refractory multi-principal element alloy system MoNbTaWHfx shows the utility of this approach for high-throughput screening of large material composition spaces.

A novel high-throughput screening method for identifying compounds that inhibit plasmid conjugation

Plasmid conjugation is an important contributing factor to the spread of antibiotic resistance among bacteria, posing a significant global health threat. Our method introduces an innovative high-throughput screening approach to identify compounds that inhibit or reduce conjugation, addressing the need for new strategies against the spread of antimicrobial resistance.Using Escherichia coli strains as donor and recipient, we screened 3500 compounds from a library provided by ABAC Therapeutics. Each 96 –well plate was loaded with 88 different compounds and bacterial cultures. Every plate also included negative and positive controls of conjugation. After an hour, cultures from wells were spotted on agar plates and assessed visually. Compounds that showed a visible effect on conjugation were retested. Six compounds targeting conjugation were found, showing promise for further analysis.