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AbstractThe identification of potential epigenetic targets for a known bioactive compound is essential and promising as more and more epigenetic drugs are used in cancer clinical treatment and the availability of chemogenomic data related to epigenetics increases. In this study, we introduce a novel epigenetic target identification strategy (ETI-Strategy) that integrates a multi-task graph convolutional neural network prior model and a protein-ligand interaction classification discriminating model using large-scale bioactivity data for a panel of 55 epigenetic targets. Our approach utilizes machine learning techniques to achieve an AUC value of 0.934 for the prior model and 0.830 for the discriminating model, outperforming inverse docking in predicting protein-ligand interactions. When comparing with other open-source target identification tools, it was found that only our tool was able to accurately predict all the targets corresponding to each compound. This further demonstrates the ability of our strategy to take full advantage of molecular-level information as well as protein-level information in molecular activity prediction. Our work highlights the contribution of machine learning in the identification of potential epigenetic targets and offers a novel approach for epigenetic drug discovery and development.Communicated by Ramaswamy H. Sarma

In drug candidate design, clearance is one of the most crucial pharmacokinetic parameters to consider. Recent advancements in machine learning techniques coupled with the growing accumulation of drug data have paved the way for the construction of computational models to predict drug clearance. However, concerns persist regarding the reliability of data collected from public sources, and a majority of current in silico quantitative structure–property relationship models tend to neglect the influence of molecular chirality. In this study, we meticulously examined human liver microsome (HLM) data from public databases and constructed two distinct data sets with varying HLM data quantity and quality. Two baseline models (RF and DNN) and three chirality-focused GNNs (DMPNN, TetraDMPNN, and ChIRo) were proposed, and their performance on HLM data was evaluated and compared with each other. The TetraDMPNN model, which leverages chirality from 2D structure, exhibited the best performance with a test R2of 0.639 and a test root-mean-squared error of 0.429. The applicability domain of the model was also defined by using a molecular similarity-based method. Our research indicates that graph neural networks capable of capturing molecular chirality have significant potential for practical application and can deliver superior performance.

NIK plays a crucial role in the noncanonical NF-κB signaling pathway associated with diverse inflammatory and autoimmune diseases. Our study presents compound 54, a novel NIK inhibitor, designed through a structure-based scaffold-hopping approach from the previously identified B022. Compound 54demonstrates remarkable selectivity and potency against NIK both in vitro and in vivo, effectively suppressing pro-inflammatory cytokines and nitric oxide production. In mouse models, compound 54protected against LPS-induced systemic sepsis, reducing AST, ALT, and AKP liver injury markers. Additionally, it also attenuates sepsis-induced lung and kidney damage. Mechanistically, compound 54blocks the noncanonical NF-κB signaling pathway by targeting NIK, preventing p100 to p52 processing. This work reveals a novel class of NIK inhibitors with significant potential for sepsis therapy.

Retrosynthesis prediction is crucial in organic synthesis and drug discovery, aiding chemists in designing efficient synthetic routes for target molecules. Data-driven deep retrosynthesis prediction has gained importance due to new algorithms and enhanced computing power. Although existing models show certain predictive power on the USPTO-50K benchmark data set, no one considers the effects of byproducts during the prediction process, which may be due to the lack of byproduct information in the benchmark data set. Here, we propose a novel two-stage retrosynthesis reaction prediction framework based on byproducts called RPBP. First, RPBP predicts the byproduct involved in the reaction based on the product molecule. Then, it handles an end-to-end prediction problem based on the prediction of reactants by product and byproduct. Unlike other methods that first identify the potential reaction center and then predict reactant molecules, RPBP considers additional information from byproducts, such as reaction reagents, conditions, and sites. Interestingly, adding byproducts reduces model learning complexity in natural language processing (NLP). Our RPBP model achieves 54.7% and 66.6% top-1 retrosynthesis prediction accuracy when the reaction class is unknown and known, respectively. It outperforms existing methods for known-class reactions, thanks to the rich chemical information in byproducts. The prediction of four kinase drugs from the literature demonstrates the model’s practicality and potential to accelerate drug discovery.

The cyclin-dependent protein kinases (CDKs) are protein-serine/threonine kinases with crucial effects on the regulation of cell cycle and transcription. CDKs can be a hallmark of cancer since their excessive expression could lead to impaired cell proliferation. However, the selectivity profile of most developed CDK inhibitors is not enough, which have hindered the therapeutic use of CDK inhibitors. In this study, we propose a multitask deep learning framework called BiLAT based on SMILES representation for the prediction of the inhibitory activity of molecules on eight CDK subtypes (CDK1, 2, 4–9). The framework is mainly composed of an improved bidirectional long short-term memory module BiLSTM and the encode layer of the Transformer framework. Additionally, the data enhancement method of SMILES enumeration is applied to improve the performance of the model. Compared with baseline predictive models based on three conventional machine learning methods and two multitask deep learning algorithms, BiLAT achieves the best performance with the highest average AUC, ACC, F1-score, and MCC values of 0.938, 0.894, 0.911, and 0.715 for the test set. Moreover, we constructed a targeted external data set CDK-Dec for the CDK family, which mainly contains bait values screened by 3D similarity with active compounds. This dataset was utilized in the subsequent evaluation of our model. It is worth mentioning that the BiLAT model is interpretable and can be used by chemists to design and synthesize compounds with improved activity. To further verify the generalization ability of the multitask BiLAT model, we also conducted another evaluation on three public datasets (Tox21, ClinTox, and SIDER). Compared with several currently popular models, BiLAT shows the best performance on two datasets. These results indicate that BiLAT is an effective tool for accelerating drug discovery.

Photoisomerization kinetics of the near-infrared (NIR) fluorophore Sulfo-Cyanine7 (SCy7) was studied by a combination of fluorescence correlation spectroscopy (FCS) and transient state (TRAST) excitation modulation spectroscopy. A photoisomerized state with redshifted emission was identified, with kinetics consistent with a three-state photoisomerization model. Combining TRAST excitation modulation with spectrofluorimetry (spectral-TRAST) further confirmed an excitation-induced redshift in the emission spectrum of SCy7. We show how this red-emissive photoisomerized state contributes to the blinking kinetics in different emission bands of NIR cyanine dyes, and how it can influence single-molecule, super-resolution, as well as Förster resonance energy transfer (FRET) and multicolor readouts. Since this state can also be populated at moderate excitation intensities, it can also more broadly influence fluorescence readouts, also readouts not relying on high excitation conditions. However, this additional red-emissive state and its photodynamics, as identified and characterized in this work, can also be used as a strategy to push the emission of NIR cyanine dyes further into the NIR and to enhance photosensitization of nanoparticles with absorption spectra further into the NIR. Finally, we show that the photoisomerization kinetics of SCy7 and the formation of its redshifted photoisomer depend strongly on local environmental conditions, such as viscosity, polarity, and steric constraints, which suggests the use of SCy7 and other NIR cyanine dyes as environmental sensors. Such environmental information can be monitored by TRAST, in the NIR, with low autofluorescence and scattering conditions and on a broad range of samples and experimental conditions.

Protein kinases are important drug targets for the treatment of several diseases. The interaction between kinases and ligands is vital in the process of small-molecule kinase inhibitor (SMKI) design. In this study, we propose a method to extract fragments and amino acid residues from crystal structures for kinase–ligand interactions. In addition, core fragments that interact with the important hinge region of kinases were extracted along with their decorations. Based on the superimposed structural data of kinases from the kinase–ligand interaction fingerprint and structure database, we obtained two libraries, namely, a hinge-unfocused fragment–amino acid pair library (FAP Lib) that contains 6672 pairs of fragments and corresponding amino-acids, and a hinge-focused hinge binder library (HB Lib) of 3560 pairs of hinge-binding scaffolds with their corresponding decorations. These two libraries constitute a kinase-focused interaction database (KID). In depth analysis was conducted on KID to explore important characteristics of fragments in the design of SMKIs. With KID, we built two kinase-focused molecule databases, one called Recomb_DB, which contains 1,72,346 molecules generated through fragment recombination based on the FAP Lib, and another called RsdHB_DB, which contains 93,030 molecules generated based on our HB Lib using molecular generation methods. Compared with five databases both commercial and non-commercial, these two databases both ranked top 3 in scaffold diversity, top 4 in molecule fingerprint diversity, and are more focused on the chemical space of kinase inhibitors. Hence, KID presents a useful addition to existing databases for the exploration of novel SMKIs.

The assessment of compound blood‐brain barrier (BBB) permeability poses a significant challenge in the discovery of drugs targeting the central nervous system. Conventional experimental approaches to measure BBB permeability are labor‐intensive, cost‐ineffective, and time‐consuming. In this study, we constructed six machine learning classification models by combining various machine learning algorithms and molecular representations. The model based on ExtraTree algorithm and random partitioning strategy obtains the best prediction result, with AUC value of 0.932±0.004 and balanced accuracy (BA) of 0.837±0.010 for the test set. We employed the SHAP method to identify important features associated with BBB permeability. In addition, matched molecular pair (MMP) analysis and representative substructure derivation method were utilized to uncover the transformation rules and distinctive structural features of BBB permeable compounds. The machine learning models proposed in this work can serve as an effective tool for assessing BBB permeability in the drug discovery for central nervous system disease.

A photon avalanche (PA) effect that occurs in lanthanide-doped solids gives rise to a giant nonlinear response in the luminescence intensity to the excitation light intensity. As a result, much weaker lasers are needed to evoke such PAs than for other nonlinear optical processes. Photon avalanches are mostly restricted to bulk materials and conventionally rely on sophisticated excitation schemes, specific for each individual system. Here we show a universal strategy, based on a migrating photon avalanche (MPA) mechanism, to generate huge optical nonlinearities from various lanthanide emitters located in multilayer core/shell nanostructrues. The core of the MPA nanoparticle, composed of Yb3+and Pr3+ions, activates avalanche looping cycles, where PAs are synchronously achieved for both Yb3+and Pr3+ions under 852 nm laser excitation. These nanocrystals exhibit a 26th-order nonlinearity and a clear pumping threshold of 60 kW cm−2. In addition, we demonstrate that the avalanching Yb3+ions can migrate their optical nonlinear response to other emitters (for example, Ho3+and Tm3+) located in the outer shell layer, resulting in an even higher-order nonlinearity (up to the 46th for Tm3+) due to further cascading multiplicative effects. Our strategy therefore provides a facile route to achieve giant optical nonlinearity in different emitters. Finally, we also demonstrate applicability of MPA emitters to bioimaging, achieving a lateral resolution of ~62 nm using one low-power 852 nm continuous-wave laser beam.

Reaction-based de novodesign is the computational generation of novel molecular structures by linking building blocks using reaction vectors derived from chemistry knowledge. In this work, we first adopted a recurrent neural network (RNN) model to generate three groups of building blocks with different functional groups and then constructed an in silico target-focused combinatorial library based on chemical reaction rules. Mer tyrosine kinase (MERTK) was used as a study case. Combined with a scaffold enrichment analysis, 15 novel MERTK inhibitors covering four scaffolds were achieved. Among them, compound 5aobtained an IC50value of 53.4 nM against MERTK without any further optimization. The efficiency of hit identification could be significantly improved by shrinking the compound library with the fragment iterative optimization strategy and enriching the dominant scaffold in the hinge region. We hope that this strategy can provide new insights for accelerating the drug discovery process.

Lead halide perovskites hold promise for photovoltaics, lasers, and light-emitting diode (LED) applications, being known as light-harvesting or -emitting materials. Here we show that colloidal lead halide CsPbCl3perovskite quantum dots (PQDs), when incorporating divalent manganese (Mn2+) ions, are able to produce spin-paired singlet oxygen molecules with over-unit quantum yield (∼1.08) in air conditions. Our mechanistic studies and atomic-level density functional theory calculations endorse an energy-migration-mediated quantum cutting process favoring multiple singlet oxygen generation (MSOG), in which one exciton-activated bulk Mn2+ion (∼2.0 eV) inside the nanocrystal migrates its energy among the Mn2+sublattice to two surface Mn2+defect states (∼1.0 eV), followed by nonradiative energy transfers to two surrounding oxygen molecules. Moreover, superhydrophobicization of MSOG PQDs through silica-mediated polystyrene encapsulation prevents them from disintegrating in aqueous medium, enabling photodegradation of methyl orange at a rate even higher than that of the canonical titanium oxide photocatalyst. The observation of ultraefficient singlet oxygen generation in PQDs has implications for fields ranging from photodynamic therapy to photocatalytic applications.

Kinase inhibitors are widely used in antitumor research, but there are still many problems such as drug resistance and off-target toxicity. A more suitable solution is to design a multitarget inhibitor with certain selectivity. Herein, computational and experimental studies were applied to the discovery of dual inhibitors against FGFR4 and EGFR. A quantitative structure–property relationship (QSPR) study was carried out to predict the FGFR4 and EGFR activity of a data set consisting of 843 and 5088 compounds, respectively. Four different machine learning methods including support vector machine (SVM), random forest (RF), gradient boost regression tree (GBRT), and XGBoost (XGB) were built using the most suitable features selected by the mutual information algorithm. As for FGFR4 and EGFR, SVM showed the best performance with R2test-FGFR4= 0.80 and R2test-EGFR= 0.75, demonstrating excellent model stability, which was used to predict the activity of some compounds from an in-house database. Finally, compound 1was selected, which exhibits inhibitory activity against FGFR4 (IC50= 86.2 nM) and EGFR (IC50= 83.9 nM) kinase, respectively. Furthermore, molecular docking and molecular dynamics simulations were performed to identify key amino acids for the interaction of compound 1with FGFR4 and EGFR. In this paper, the machine-learning-based QSAR models were established and effectively applied to the discovery of dual-target inhibitors against FGFR4 and EGFR, demonstrating the great potential of machine learning strategies in dual inhibitor discovery.

AbstractApoptosis signal-regulating Kinase 1 (ASK1) has been confirmed as a potential therapeutic target for the treatment of non-alcoholic steatohepatitis (NASH) disorder and the discovery of ASK1 inhibitors has attracted increasing attention. In this work, a series of in silico methods including pharmacophore screening, docking binding site analysis, protein-ligand interaction fingerprint (PLIF) similarity investigation and molecular docking were applied to find the potential hits from commercial compound databases. Five compounds with potential inhibitory activity were purchased and submitted to biological activity validation. Thus, one hit compound was discovered with micromolar IC50value (10.59 μM) against ASK1. Results demonstrated that the integration of computation methods and biological test was quite reliable for the discovery of potent ASK1 inhibitors and the strategy could be extended to other similar targets of interest.

A series of N-phenyl-7H-pyrrolo[2,3-d]pyrimidin-4-amine derivatives with NF-κB inducing kinase (NIK) inhibitory activity were obtained through structure-based drug design and synthetic chemistry. Among them, 4-(3-((7H-pyrrolo[2,3-d]pyrimidin-4-yl)amino)-4-morpholinophenyl)-2-(thiazol-2-yl)but-3-yn-2-ol (12f) was identified as a highly potent NIK inhibitor, along with satisfactory selectivity. The pharmacokinetics of 12fand its ability to inhibit interleukin 6 secretion in BEAS-2B cells were better than compound 1developed by Amgen. Oral administration of different doses of 12fin an imiquimod-induced psoriasis mouse model showed effective alleviation of psoriasis, including invasive erythema, swelling, skin thickening, and scales. The underlying pathological mechanism involved attenuation of proinflammatory cytokine and chemokine gene expression, and the infiltration of macrophages after the treatment of 12f. This work provides a foundation for the development of NIK inhibitors, highlighting the potential of developing NIK inhibitors as a new strategy for the treatment of psoriasis.

AbstractAs an effective target in abnormal angiogenesis-related tumor treatment, VEGFR-2 has small-molecule inhibitors of various scaffolds being approved for treating diseases such as renal carcinoma, non-small cell lung cancer, etc. However, endogenous and acquired drug resistance are still considered to be the main contributors for the failure of VEGFR-2 clinical candidates. Therefore, development of novel VEGFR-2 inhibitors is still urgently needed in the market but also challenging. In this work, residues including Asp1046, Ile1025, HIS1026, Cys919 and Lys868 were identified as the most important residues for Hbonded interaction, while His1026, Asp1046, Glu885, Ile1025 and Leu840 exhibited critical role for the nonbonded interactions through a comprehensive analysis of protein–ligand interactions, which plays critical roles in the binding of compounds and targets. Guided by the analysis of binding interactions, a total of 10 novel VEGFR-2 inhibitors based on N-methyl-4-oxo-N-propyl-1,4-dihydroquinoline-2-carboxamide scaffold were discovered through fragment-based drug design and structure-based virtual screening, which expands the chemical space of current VEGFR-2 inhibitors. Biological activity evaluation showed that even though the enzymatic activity of these compounds against VEGFR-2 were inferior to that of the positive controls sorafenib and motesanib, compound I-10 showed moderate HepG2 cell inhibitory activity with an IC50value of 33.65 μM and eight compounds exhibited moderate or higher HUVEC inhibitory activity in the range of 19.54–57.98 μM compared to the controls. Particularly, the HUVEC inhibitory activity of compound I-6 (IC50= 19.54 μM) outperformed motesanib and can be used as starting points for further optimization and development for cancer treatment.Communicated by Ramaswamy H. Sarma

A structurally diverse, high-quality, and kinase-focused database plays a critical role in finding hits or leads in kinase drug discovery. Here, we propose a workflow for designing a virtual kinase-focused combinatorial library using existing structures. Based on the analysis of known protein kinase inhibitors (PKIs), detailed fragment optimization, fragment selection, fragment linking, and a molecular filtering scheme were defined. Quick recognition of core fragments that can possibly form dual hydrogen bonds with the hinge region of the ATP-pocket was proposed. Furthermore, three diversity and four quality metrics were chosen for compound library analysis, which can be applied to databases with over 30 million structures. Compared with 13 commercial libraries, our protocol demonstrates a special advantage in terms of good skeleton diversity, acceptable fingerprint diversity, balanced scaffold distribution, and high quality, which can work well not only on existing PKIs, but also on four chosen commercial libraries. Overall, the strategy can greatly facilitate the expansion of a desirable chemical space for kinase drug discovery.

Fragment‐based drug design (FBDD) has emerged as a captivating subject in the realm of computer‐aided drug design, enabling the generation of novel molecules through the rearrangement of ring systems within known compounds. The construction of focused fragment library plays a pivotal role in FBDD, necessitating the compilation of all potential bioactive ring systems capable of interacting with a specific target. In our study, we propose a workflow for the development of a focused fragment library and combinatorial compound library. The fragment library comprises seed fragments and collected fragments. The extraction of seed fragments is guided by receptor information, serving as a prerequisite for establishing a focused libraries. Conversely, collected fragments are obtained using the feature graph method, which offers a simplified representation of fragments and strikes a balance between diversity and similarity when categorizing different fragments. The utilization of feature graph facilitates the rational partitioning of chemical space at fragment level, enabling the exploration of desired chemical space and enhancing the efficiency of screening compound library. Analysis demonstrates that our workflow enables the enumeration of a greater number of entirely new potential compounds, thereby aiding in the rational design of drugs.

Pure organic ultralong room temperature phosphorescent (URTP) materials have garnered significant attention for applications in luminescent materials, biosensing, and information encryption. These materials offer advantages over heavy metal phosphorescent materials, such as lower cost, reduced biological toxicity, and minimal environmental impact. Herein, for the first time, we demonstrate a series of organic RTP materials based on spiro[fluorene-9,9′-xanthene] (SFX) hole-transporting molecules, specifically X59and X55. Our research presents that incorporating more rigid SFX units significantly extends RTP lifetime and enhances photoluminescence quantum yield (PLQY). The large steric hindrance of the rigid SFX structures suppresses nonradiative molecular motions, thereby prolonging phosphorescence emission. Compared to the baseline molecule X1, experimental results show that molecule X59extends the phosphorescence lifetime by 230 ms, while X55achieves an extension of 260 ms. Furthermore, we highlight the potential of this series of RTP molecules for use in transparent, programmable luminescent tags. Our work not only expands the molecular types of organic RTP materials but also provides innovative strategies for designing long-lived, high-quantum-yield RTP molecules. We envision that this will advance the smart device field of organic phosphorescent materials and their practical applications, such as intelligent labels, tags, and optical sensors.

Understanding the intricate relationships between symptom dimensions, clusters, and cognitive impairments is crucial for early detection and intervention in individuals at clinical high risk for psychosis. This study delves into this complex interplay in a clinical high risk sample with the aim of predicting the conversion to psychosis.

Restricted scan path mode is hypothesized to explain abnormal scanning patterns in patients with schizophrenia. Here, we calculated entropy scores (drawing upon gaze data to measure the statistical randomness of eye movements) to quantify how strategical and random participants were to process image stimuli.

Noncovalent interactions between small-molecule drugs and protein targets assume a pivotal role in drug design. Moreover, the design of covalent inhibitors, forming covalent bonds with amino acid residues, requires rational reactivity for their covalent warheads, presenting a key challenge as well. Understanding the intricacies of these interactions provides a more comprehensive understanding of molecular binding mechanisms, thereby guiding the rational design of potent inhibitors. In this study, we adopted the fragment-based drug design approach, introducing a novel methodology to extract noncovalent and covalent fragments according to distinct three-dimensional (3D) interaction modes from noncovalent and covalent compound libraries. Additionally, we systematically replaced existing ligands with rational fragment substitutions, based on the spatial orientation of fragments in 3D space. Furthermore, we adopted a molecular generation approach to create innovative covalent inhibitors. This process resulted in the recombination of a noncovalent compound library and several covalent compound libraries, constructed by two commonly encountered covalent amino acids: cysteine and serine. We utilized noncovalent ligands in KLIFS and covalent ligands in CovBinderInPDB as examples to recombine noncovalent and covalent libraries. These recombined compound libraries cover a substantial portion of the chemical space present in the original compound libraries and exhibit superior performance in terms of molecular scaffold diversity compared to the original compound libraries and other 11 commercial libraries. We also recombined BTK-focused libraries, and 23 compounds within our libraries have been validated by former researchers to possess potential biological activity. The establishment of these compound libraries provides valuable resources for virtual screening of covalent and noncovalent drugs targeting similar molecular targets.

Protein kinases are important drug targets in several therapeutic areas ,and structure-based virtual screening (SBVS) is an important strategy in discovering lead compounds for kinase targets. However, there are multiple crystal structures available for each target, and determining which one is the most favorable is a key step in molecular docking for SBVS due to the ligand induce-fit effect. This work aimed to find the most desirable crystal structures for molecular docking by a comprehensive analysis of the protein kinase database which covers 190 different kinases from all eight main kinase families. Through an integrated self-docking and cross-docking evaluation, 86 targets were eventually evaluated on a total of 2608 crystal structures. Results showed that molecular docking has great capability in reproducing conformation of crystallized ligands and for each target, the most favorable crystal structure was selected, and the AGC family outperformed the other family targets based on RMSD comparison. In addition, RMSD values, GlideScore, and corresponding bioactivity data were compared and demonstrated certain relationships. This work provides great convenience for researchers to directly select the optimal crystal structure in SBVS-based kinase drug design and further validates the effectiveness of molecular docking in drug discovery.

Machine intelligence has been greatly developed in the past decades and has been widely used in many fields. In the recent years, many reports have shown its satisfactory effect in drug discovery. In this study, machine intelligence methods were explored to assist the cell activity prediction. Multiple machine intelligence methods including support vector machine, decision tree, random forest, extra trees, gradient boosting machine, convolutional neural network, long short-term memory network, and gated recurrent unit network were employed to separate compounds based on their cell activity. Different from some reported classification models, compounds were expressed as a string by the simplified molecular input line entry system and directly used as input rather than any chemical descriptors, which mimicked natural language processing. Both the single cell strain and whole data set under the balanced and imbalanced data distributions were discussed, respectively. Different activity cutoffs were set for the single (Z-score = 3) and the whole (Z-score = 5 and 6) data set. Nine metrics were used to evaluate the models including accuracy, precision, recall, f1-score, area under the receiver operating characteristic curve score, Cohen’s κ, Brier score, Matthews correlation coefficient, and balanced accuracy. The results show that the gradient boosting machine is competent at balanced data distribution, and convolutional neural network is qualified for the imbalanced one. The results demonstrate that both classic machine learning methods and deep learning methods have potential in classification of compound cell activity.

Severe spinal cord injury (SCI) is caused by external mechanical injury, resulting in unrecoverable neurological injury. Recent studies have shown that exosomes derived from bone marrow mesenchymal stem cells (BMSCs-Exos) might be valuable paracrine molecules in the treatment of SCI. In this study, we designed SCI models in vivo and in vitro and then investigated the possible mechanism of successful repair by BMSCs-Exos. In vivo, we established one Sham group and two SCI model groups. The Basso, Beattie, Bresnahan (BBB) scores showed that BMSCs-Exos could effectively promote the recovery of spinal cord function. The results of the Nissl staining, immunohistochemistry, and TUNEL/NeuN/DAPI double staining showed that BMSCs-Exos inhibited neuronal apoptosis. Western blot analysis showed that the protein expression level of Bcl-2 was significantly increased in the BMSCs-Exos group compared with the PBS group, while the protein expression levels of Bax, cleaved caspase-3, and cleaved caspase-9 were significantly decreased. The results of western bolt and qRT-PCR demonstrated that BMSCs-Exos could activate the Wnt/β-catenin signaling pathway effectively. In vitro, we found that inhibition of the Wnt/β-catenin signaling pathway could promote neuronal apoptosis following lipopolysaccharide (LPS) induction. These results demonstrated that BMSCs-Exos may be a promising therapeutic for SCI by activating the Wnt/β-catenin signaling pathway.

Human pharmacokinetics is of great significance in the selection of drug candidates, and in silico estimation of pharmacokinetic parameters in the early stage of drug development has become the trend of drug research owing to its time- and cost-saving advantages. Herein, quantitative structure–property relationship studies were carried out to predict four human pharmacokinetic parameters including volume of distribution at steady state (VDss), clearance (CL), terminal half-life (t1/2), and fraction unbound in plasma (fu), using a data set consisting of 1352 drugs. A series of regression models were built using the most suitable features selected by Boruta algorithm and four machine learning methods including support vector machine (SVM), random forest (RF), gradient boosting machine (GBM), and XGBoost (XGB). For VDss, SVM showed the best performance with R2test= 0.870 and RMSEtest= 0.208. For the other three pharmacokinetic parameters, the RF models produced the superior prediction accuracy (for CL, R2test= 0.875 and RMSEtest= 0.103; for t1/2, R2test= 0.832 and RMSEtest= 0.154; for fu, R2test= 0.818 and RMSEtest= 0.291). Assessed by 10-fold cross validation, leave-one-out cross validation, Y-randomization test and applicability domain evaluation, these models demonstrated excellent stability and predictive ability. Compared with other published models for human pharmacokinetic parameters estimation, it was further confirmed that our models obtained better predictive ability and could be used in the selection of preclinical candidates.

Background: B-Raf has become an important and exciting therapeutic cancer target. Methods: In the present work, molecular modeling protocols like molecular docking, MM/GBSA calculations, 3D-QSAR and binding site detection were performed on a dataset of 41 Type II inhibitors. Molecular docking was applied to explore the detailed binding process between the inhibitors and B-Raf kinase. Furthermore, the good linear relationships between G-Scores and MM/GBSA calculated and the experimental activity were shown. The satisfactory CoMFA and CoMSIA were constructed based on the conformations obtained by molecular docking. Results: The key structural requirements for increasing biological activity were verified by analyzing 3D contour maps of the 3D-QSAR models. FTMap and SiteMap were also used to detect the more efficient active binding site. Conclusion: New inhibitors were synthesized and the biological activities were evaluated, the results further validated our design strategy.

In order to investigate treatment effects of ultra filtration membranes (UF) fouling by combined PAC treatment, two sets of processes were constructed: UF and powdered activated carbon (PAC) + UF. The efficiency of the treatment schemes was evaluated in terms of organic matter, ultraviolet absorbance at 254 nm (UV254), turbidity removal as well as chemical oxygen demand (COD Mn). The foulants were evaluated using Fourier transform infrared spectroscopy (FTIR) analysis. Micro-polluted surface water was treated using powdered activated carbon-ultra filtration membrane. The results showed that the removal rate of turbidity, UV254and CODMnreached 91.7%, 84.9% and 77.5% respectively. Membrane fouling in ultra filtration membrane is attributable to proteins and humus. These materials deposit onto the membrane surface and into the membrane pores, clogging the pores, and leading to a decline in the permeability of the membrane. The trans-membrane pressure(TMP) can be recovered by chemical cleaning. It is evidenced that combined PAC technology could better enhance the rejection of contaminants and alleviate UF fouling.

Perovskite photovoltaics have recently attracted extensive attention due to their unprecedented high power conversion efficiencies (PCEs) in combination with primitive manufacturing conditions. However, the inherent polycrystalline nature of perovskite films renders an exceptional density of structural defects, especially at the grain boundaries (GBs) and film surfaces, representing a key challenge that impedes the further performance improvement of perovskite solar cells (PSCs) and large solar module ambitions towards commercialization. Here, a novel strategy is presented utilizing a simple ethylammonium chloride (EACl) additive in combination with a facile solvent bathing approach to achieve high quality methyammonium lead iodide (MAPbI3) films. Well-oriented, micron-sized grains were observed, which contribute to an extended carrier lifetime and reduced trap density. Further investigations unraveled the distinctively prominent effects of EACl in modulating the perovskite film quality. The EACl was found to promote the perovskite grain growing without undergoing the formation of intermediate phases. Moreover, the EACl was also revealed to deplete at relative low temperature to enhance the film quality without compromising the beneficial bandgap for solar cell applications. This new strategy boosts the power conversion efficiency (PCE) to 20.9% and 19.0% for devices with effective areas of 0.126 cm2and 1.020 cm2, respectively, with negligible current hysteresis and enhanced stability. Besides, perovskite films with a size of 10 × 10 cm2, and an assembled 16 cm2(5 × 5 cm2module) perovskite solar module with a PCE of over 11% were constructed.