ChemMORT: an automatic ADMET optimization platform using deep learning and multi-objective particle swarm optimization.

Drug discovery and development constitute a laborious and costly undertaking. The success of a drug hinges not only good efficacy but also acceptable absorption, distribution, metabolism, elimination, and toxicity (ADMET) properties. Overall, up to 50% of drug development failures have been contributed from undesirable ADMET profiles. As a multiple parameter objective, the optimization of the ADMET properties is extremely challenging owing to the vast chemical space and limited human expert knowledge. In this study, a freely available platform called Chemical Molecular Optimization, Representation and Translation (ChemMORT) is developed for the optimization of multiple ADMET endpoints without the loss of potency (https://cadd.nscc-tj.cn/deploy/chemmort/). ChemMORT contains three modules: Simplified Molecular Input Line Entry System (SMILES) Encoder, Descriptor Decoder and Molecular Optimizer. The SMILES Encoder can generate the molecular representation with a 512-dimensional vector, and the Descriptor Decoder is able to translate the above representation to the corresponding molecular structure with high accuracy. Based on reversible molecular representation and particle swarm optimization strategy, the Molecular Optimizer can be used to effectively optimize undesirable ADMET properties without the loss of bioactivity, which essentially accomplishes the design of inverse QSAR. The constrained multi-objective optimization of the poly (ADP-ribose) polymerase-1 inhibitor is provided as the case to explore the utility of ChemMORT.

BRD9 promotes the progression of gallbladder cancer via CST1 upregulation and interaction with FOXP1 through the PI3K/AKT pathway and represents a therapeutic target.

Gallbladder cancer (GBC) is highly aggressive and has poor prognosis, with most patients only diagnosed at an advanced stage. Furthermore, treatment options are limited, and their effect is unsatisfactory. Bromodomain-containing protein (BRD) is an epigenetic regulator that plays a carcinogenic role in several tumors, including squamous cell lung cancer, acute myeloid leukemia, synovial sarcoma, and malignant rhabdomyosarcoma. However, the expression, biological function, and molecular mechanisms of action of BRD9 in GBC are still unknown. Kaplan-Meier analysis, qRT-PCR, and analysis of clinical features were used to assess the clinical significance of BRD9 in GBC. Cell Counting Kit-8 and colony formation assays were performed to determine the effects of BRD9 on cell growth. The functional role of BRD9 in GBC was explored using qRT-PCR, western blotting, siRNA, and CHIP-qPCR. mRNA sequencing was performed to explore the underlying mechanisms of BRD9, and a nude mouse model of GBC was established to explore the anti-tumor effects of the BRD9 inhibitor I-BRD9 in vivo. BRD9 expression was elevated in GBC tissues compared with adjacent non-tumor tissues, and high BRD9 expression was associated with poor prognosis in patients with GBC. BRD9 knockdown by siRNA significantly decreased cell growth. Targeting BRD9 with I-BRD9 inhibited the proliferation of GBC cells without significant toxic effects. Additionally, I-BRD9 treatment suppressed CST1 expression in GBC cell lines, thereby inhibiting the PI3K-AKT pathway. The transcription factor FOXP1 was found to interact with BRD9 to regulate CST1 expression. Collectively, these results suggest that BRD9 may be a promising biomarker and therapeutic target for GBC.

Identification of COQ2 as a regulator of proliferation and lipid peroxidation through genome-scale CRISPR-Cas9 screening in myeloma cells.

Multiple myeloma (MM) is the second most common malignant haematological disease with a poor prognosis. The limit therapeutic progress has been made in MM patients with cancer relapse, necessitating deeper research into the molecular mechanisms underlying its occurrence and development. A genome-wide CRISPR-Cas9 loss-of-function screening was utilized to identify potential therapeutic targets in our research. We revealed that COQ2 plays a crucial role in regulating MM cell proliferation and lipid peroxidation (LPO). Knockout of COQ2 inhibited cell proliferation, induced cell cycle arrest and reduced tumour growth in vivo. Mechanistically, COQ2 promoted the activation of the MEK/ERK cascade, which in turn stabilized and activated MYC protein. Moreover, we found that COQ2-deficient MM cells increased sensitivity to the LPO activator, RSL3. Using an inhibitor targeting COQ2 by 4-CBA enhanced the sensitivity to RSL3 in primary CD138+ myeloma cells and in a xenograft mouse model. Nevertheless, co-treatment of 4-CBA and RSL3 induced cell death in bortezomib-resistant MM cells. Together, our findings suggest that COQ2 promotes cell proliferation and tumour growth through the activation of the MEK/ERK/MYC axis and targeting COQ2 could enhance the sensitivity to ferroptosis in MM cells, which may be a promising therapeutic strategy for the treatment of MM patients.

SPLDExtraTrees: robust machine learning approach for predicting kinase inhibitor resistance.

Drug resistance is a major threat to the global health and a significant concern throughout the clinical treatment of diseases and drug development. The mutation in proteins that is related to drug binding is a common cause for adaptive drug resistance. Therefore, quantitative estimations of how mutations would affect the interaction between a drug and the target protein would be of vital significance for the drug development and the clinical practice. Computational methods that rely on molecular dynamics simulations, Rosetta protocols, as well as machine learning methods have been proven to be capable of predicting ligand affinity changes upon protein mutation. However, the severely limited sample size and heavy noise induced overfitting and generalization issues have impeded wide adoption of machine learning for studying drug resistance. In this paper, we propose a robust machine learning method, termed SPLDExtraTrees, which can accurately predict ligand binding affinity changes upon protein mutation and identify resistance-causing mutations. Especially, the proposed method ranks training data following a specific scheme that starts with easy-to-learn samples and gradually incorporates harder and diverse samples into the training, and then iterates between sample weight recalculations and model updates. In addition, we calculate additional physics-based structural features to provide the machine learning model with the valuable domain knowledge on proteins for these data-limited predictive tasks. The experiments substantiate the capability of the proposed method for predicting kinase inhibitor resistance under three scenarios and achieve predictive accuracy comparable with that of molecular dynamics and Rosetta methods with much less computational costs.

Prognostic Impact of Neoadjuvant Chemotherapy in Localized or Locoregionally Advanced Gallbladder Cancer: A Population-Based and Propensity Score Matched SEER Analysis.

BACKGROUND: The effect of neoadjuvant chemotherapy (NACT) in gallbladder cancer (GBC) patients remains controversial. The aim of this study was to assess the impact of NACT on overall survival (OS) and cancer specific survival (CSS) in patients with localized or locoregionally advanced GBC, and to explore possible protective predictors for prognosis. METHODS: Data for patients with localized or locoregionally advanced GBC (i.e., categories cTx-cT4, cN0-2, and cM0) from 2004 to 2020 were collected from the Surveillance, Epidemiology, and End Results (SEER) database. Patients in the NACT and non-NACT groups were propensity score matched (PSM) 1:3, and the Kaplan-Meier method and log-rank test were performed to analyze the impact of NACT on OS and CSS. Univariable and multivariable Cox regression models were applied to identify the possible prognostic factors. Subgroup analysis was conducted to identify patients who would benefit from NACT. RESULTS: Of the 2676 cases included, 78 NACT and 234 non-NACT patients remained after PSM. In localized or locoregionally advanced GBC patients, the median OS of the NACT and non-NACT was 31 and 16 months (log-rank P < 0.01), and the median CSS of NACT and non-NACT was 32 and 17 months (log-rank P < 0.01), respectively. Longer median OS (31 vs 17 months, log-rank P < 0.01) and CSS (32 vs 20 months, log-rank P < 0.01) was associated with NACT compared with surgery alone. Multivariable Cox regression analysis showed that NACT, stage, and surgery type were prognostic factors for OS and CSS in GBC patients. Subgroup analysis revealed that the survival hazard ratios (HRs) of NACT vs non-NACT for localized or locoregionally advanced GBC patients were significant in most subgroups. CONCLUSIONS: NACT may provide therapeutic benefits for localized or locoregionally advanced GBC patients, especially for those with advanced stage, node-positive, poorly differentiated or undifferentiated disease. NACT combined with radical surgery was associated with a survival advantage. Therefore, NACT combined with surgery may provide a better treatment option for resectable GBC patients.

Scopy: an integrated negative design python library for desirable HTS/VS database design.

BACKGROUND: High-throughput screening (HTS) and virtual screening (VS) have been widely used to identify potential hits from large chemical libraries. However, the frequent occurrence of 'noisy compounds' in the screened libraries, such as compounds with poor drug-likeness, poor selectivity or potential toxicity, has greatly weakened the enrichment capability of HTS and VS campaigns. Therefore, the development of comprehensive and credible tools to detect noisy compounds from chemical libraries is urgently needed in early stages of drug discovery. RESULTS: In this study, we developed a freely available integrated python library for negative design, called Scopy, which supports the functions of data preparation, calculation of descriptors, scaffolds and screening filters, and data visualization. The current version of Scopy can calculate 39 basic molecular properties, 3 comprehensive molecular evaluation scores, 2 types of molecular scaffolds, 6 types of substructure descriptors and 2 types of fingerprints. A number of important screening rules are also provided by Scopy, including 15 drug-likeness rules (13 drug-likeness rules and 2 building block rules), 8 frequent hitter rules (four assay interference substructure filters and four promiscuous compound substructure filters), and 11 toxicophore filters (five human-related toxicity substructure filters, three environment-related toxicity substructure filters and three comprehensive toxicity substructure filters). Moreover, this library supports four different visualization functions to help users to gain a better understanding of the screened data, including basic feature radar chart, feature-feature-related scatter diagram, functional group marker gram and cloud gram. CONCLUSION: Scopy provides a comprehensive Python package to filter out compounds with undesirable properties or substructures, which will benefit the design of high-quality chemical libraries for drug design and discovery. It is freely available at https://github.com/kotori-y/Scopy.

PySmash: Python package and individual executable program for representative substructure generation and application.

BACKGROUND: Substructure screening is widely applied to evaluate the molecular potency and ADMET properties of compounds in drug discovery pipelines, and it can also be used to interpret QSAR models for the design of new compounds with desirable physicochemical and biological properties. With the continuous accumulation of more experimental data, data-driven computational systems which can derive representative substructures from large chemical libraries attract more attention. Therefore, the development of an integrated and convenient tool to generate and implement representative substructures is urgently needed. RESULTS: In this study, PySmash, a user-friendly and powerful tool to generate different types of representative substructures, was developed. The current version of PySmash provides both a Python package and an individual executable program, which achieves ease of operation and pipeline integration. Three types of substructure generation algorithms, including circular, path-based and functional group-based algorithms, are provided. Users can conveniently customize their own requirements for substructure size, accuracy and coverage, statistical significance and parallel computation during execution. Besides, PySmash provides the function for external data screening. CONCLUSION: PySmash, a user-friendly and integrated tool for the automatic generation and implementation of representative substructures, is presented. Three screening examples, including toxicophore derivation, privileged motif detection and the integration of substructures with machine learning (ML) models, are provided to illustrate the utility of PySmash in safety profile evaluation, therapeutic activity exploration and molecular optimization, respectively. Its executable program and Python package are available at https://github.com/kotori-y/pySmash.

QSAR-assisted-MMPA to expand chemical transformation space for lead optimization.

Matched molecular pairs analysis (MMPA) has become a powerful tool for automatically and systematically identifying medicinal chemistry transformations from compound/property datasets. However, accurate determination of matched molecular pair (MMP) transformations largely depend on the size and quality of existing experimental data. Lack of high-quality experimental data heavily hampers the extraction of more effective medicinal chemistry knowledge. Here, we developed a new strategy called quantitative structure-activity relationship (QSAR)-assisted-MMPA to expand the number of chemical transformations and took the logD7.4 property endpoint as an example to demonstrate the reliability of the new method. A reliable logD7.4 consensus prediction model was firstly established, and its applicability domain was strictly assessed. By applying the reliable logD7.4 prediction model to screen two chemical databases, we obtained more high-quality logD7.4 data by defining a strict applicability domain threshold. Then, MMPA was performed on the predicted data and experimental data to derive more chemical rules. To validate the reliability of the chemical rules, we compared the magnitude and directionality of the property changes of the predicted rules with those of the measured rules. Then, we compared the novel chemical rules generated by our proposed approach with the published chemical rules, and found that the magnitude and directionality of the property changes were consistent, indicating that the proposed QSAR-assisted-MMPA approach has the potential to enrich the collection of rule types or even identify completely novel rules. Finally, we found that the number of the MMP rules derived from the experimental data could be amplified by the predicted data, which is helpful for us to analyze the medicinal chemical rules in local chemical environment. In summary, the proposed QSAR-assisted-MMPA approach could be regarded as a very promising strategy to expand the chemical transformation space for lead optimization, especially when no enough experimental data can support MMPA.

ChemFLuo: a web-server for structure analysis and identification of fluorescent compounds.

BACKGROUND: Fluorescent detection methods are indispensable tools for chemical biology. However, the frequent appearance of potential fluorescent compound has greatly interfered with the recognition of compounds with genuine activity. Such fluorescence interference is especially difficult to identify as it is reproducible and possesses concentration-dependent characteristic. Therefore, the development of a credible screening tool to detect fluorescent compounds from chemical libraries is urgently needed in early stages of drug discovery. RESULTS: In this study, we developed a webserver ChemFLuo for fluorescent compound detection, based on two large and high-quality training datasets containing 4906 blue and 8632 green fluorescent compounds. These molecules were used to construct a group of prediction models based on the combination of three machine learning algorithms and seven types of molecular representations. The best blue fluorescence prediction model achieved with balanced accuracy (BA) = 0.858 and area under the receiver operating characteristic curve (AUC) = 0.931 for the validation set, and BA = 0.823 and AUC = 0.903 for the test set. The best green fluorescence prediction model achieved the prediction accuracy with BA = 0.810 and AUC = 0.887 for the validation set, and BA = 0.771 and AUC = 0.852 for the test set. Besides prediction model, 22 blue and 16 green representative fluorescent substructures were summarized for the screening of potential fluorescent compounds. The comparison with other fluorescence detection tools and theapplication to external validation sets and large molecule libraries have demonstrated the reliability of prediction model for fluorescent compound detection. CONCLUSION: ChemFLuo is a public webserver to filter out compounds with undesirable fluorescent properties, which will benefit the design of high-quality chemical libraries for drug discovery. It is freely available at http://admet.scbdd.com/chemfluo/index/.

Inhibition of XPO1 with KPT-330 induces autophagy-dependent apoptosis in gallbladder cancer by activating the p53/mTOR pathway.

BACKGROUND: Gallbladder cancer (GBC) is a highly aggressive malignant cancer in the biliary system with poor prognosis. XPO1 (chromosome region maintenance 1 or CRM1) mediates the nuclear export of several proteins, mainly tumor suppressors. Thus, XPO1 functions as a pro-oncogenic factor. KPT-330 (Selinexor) is a United States Food and Drug Administration approved selective inhibitor of XPO1 that demonstrates good therapeutic effects in hematologic cancers. However, the function of XPO1 and the effect of KPT-330 have not been reported in GBC. METHODS: We analyzed the correlation between XPO1 expression levels by q-PCR and clinical features of GBC patients. Cell proliferation assays were used to analyze the in vitro antitumor effects of XPO1 inhibitor KPT-330. mRNA sequencing was used to explore the underlying mechanisms. Western blot was performed to explore the relationship between apoptosis and autophagy. The in vivo antitumor effect of KPT-330 was investigated in a nude mouse model of gallbladder cancer. RESULTS: We found that high expression of XPO1 was related to poor prognosis of GBC patients. We observed that XPO1 inhibitor KPT-330 inhibited the proliferation of GBC cells in vitro. Furthermore, XPO1 inhibitor KPT-330 induced apoptosis by reducing the mitochondrial membrane potential and triggering autophagy in NOZ and GBC-SD cells. Indeed, XPO1 inhibitor KPT-330 led to nuclear accumulation of p53 and activated the p53/mTOR pathway to regulate autophagy-dependent apoptosis. Importantly, KPT-330 suppressed tumor growth with no obvious toxic effects in vivo. CONCLUSION: XPO1 may be a promising prognostic indicator for GBC, and KPT-330 appears to be a potential drug for treating GBC effectively and safely.

Gemcitabine and XCT790, an ERRα inverse agonist, display a synergistic anticancer effect in pancreatic cancer.

Pancreatic cancer (PC) is one of the most fatal and chemoresistant malignancies with a poor prognosis. The current therapeutic options for PC have not achieved satisfactory results due to drug resistance. Therefore, it is urgent to develop novel treatment strategies with enhanced efficacy. This study sought to investigate the anticancer effect of gemcitabine and XCT790, an estrogen-related receptor alpha (ERRα) inverse agonist, as monotherapies or in combination for the treatment of PC. Here we demonstrated that the drug combination synergistically suppressed PC cell viability, its proliferative, migratory, invasive, apoptotic activities, and epithelial-to-mesenchymal transition (EMT), and it triggered G0/G1 cell cycle arrest and programmed cell death in vitro. In addition, in vivo assays using xenograft and mini-PDX (patient-derived xenograft) models further confirmed the synergistic antitumor effect between gemcitabine and XCT790 on PC. Mechanistically, gemcitabine and XCT790 suppressed PC by inhibiting ERRα and MEK/ERK signaling pathway. In conclusion, our current study demonstrated for the first time that gemcitabine combined with XCT790 displayed synergistic anticancer activities against PC, suggesting that their combination might be a promising treatment strategy for the therapy of PC.

Beyond the limitation of targeted therapy: Improve the application of targeted drugs combining genomic data with machine learning.

Precision oncology involves effectively selecting drugs for cancer patients and planning an effective treatment regimen. However, for Molecular targeted drug, using genomic state of the drug target to select drugs has limitations. Many patients who could benefit from molecularly targeted drugs, but they are being missed due to the insufficient labelling ability of the existing target genes. For non-specific chemotherapy drugs, most of the first-line anticancer drugs do not have biomarkers to guide doctor make treatment regimen. Furthermore, it is important to determine a long-term treatment plan based on the patient's genomic data during tumor evolution. Therefore, it is necessary to establish a tumor drug sensitivity prediction model, which can assist doctors in designing a personalized tumor treatment regimen. This paper proposed a novel model to predict tumor drug sensitivity including targeted drugs and non-specific chemotherapy drugs. This model uses statistical methods based on Bimodal distribution to select multimodal genetic data to solve dimensional challenges and reduce noise and to establish a classification model to predict the effectiveness of the drug in the tumor cell line using machine learning. The experimental test 87 molecular targeted drugs and non-specific chemotherapy drugs. The results show that the method can effectively predict the sensitivity of tumor drugs with an average sensitivity of 0.98 and specificity of 0.97. This model is worth to promotion. If it can be successfully used in clinical trials, it will effectively assist doctors to develop personalized cancer treatment programs and expand the application of molecularly targeted drugs.

Structural Analysis and Identification of False Positive Hits in Luciferase-Based Assays.

Luciferase-based bioluminescence detection techniques are highly favored in high-throughput screening (HTS), in which the firefly luciferase (FLuc) is the most commonly used variant. However, FLuc inhibitors can interfere with the activity of luciferase, which may result in false positive signals in HTS assays. In order to reduce the unnecessary cost of time and money, an in silico prediction model for FLuc inhibitors is highly desirable. In this study, we built an extensive data set consisting of 20 888 FLuc inhibitors and 198 608 noninhibitors, and then developed a group of classification models based on the combination of three machine learning (ML) algorithms and four types of molecular representations. The best prediction model based on XGBoost and ECFP4 and MOE2d descriptors yielded a balanced accuracy (BA) of 0.878 and an area under the receiver operating characteristic curve (AUC) value of 0.958 for the validation set, and a BA of 0.886 and an AUC of 0.947 for the test set. Three external validation sets, including set 1 (3231 FLuc inhibitors and 69 783 noninhibitors), set 2 (695 FLuc inhibitors and 75 913 noninhibitors), and set 3 (1138 FLuc inhibitors and 8155 noninhibitors), were used to verify the predictive ability of our models. The BA values for the three external validation sets given by the best model are 0.864, 0.845, and 0.791, respectively. In addition, the important features or structural fragments related to FLuc inhibitors were recognized by the Shapley additive explanations (SHAP) method along with their influences on predictions, which may provide valuable clues to detecting undesirable luciferase inhibitors. Based on the important and explanatory features, 16 rules were proposed for detecting FLuc inhibitors, which can achieve a correction rate of 70% for FLuc inhibitors. Furthermore, a comparison with existing prediction rules and models for FLuc inhibitors used in virtual screening verified the high reliability of the models and rules proposed in this study. We also used the model to screen three curated chemical databases, and almost 10% of the molecules in the evaluated databases were predicted as inhibitors, highlighting the potential risk of false positives in luciferase-based assays. Finally, a public web server called ChemFLuc was developed (http://admet.scbdd.com/chemfluc/index/), and it offers a free available service to predict potential FLuc inhibitors.

MicroRNA-1275 inhibits cell migration and invasion in gastric cancer by regulating vimentin and E-cadherin via JAZF1.

BACKGROUND: Emerging evidence has shown that miR-1275 plays a critical role in tumour metastasis and the progression of various types of cancer. In this study, we analysed the role and mechanism of miR-1275 in the progression and prognosis of gastric cancer (GC). METHODS: Target genes of miR-1275 were identified and verified by luciferase assay and Western blotting. The function of miR-1275 in invasion and metastasis was analysed in vitro and in vivo in nude mice. The signal pathway regulated by miR-1275 was examined by qRT-PCR, Western blotting and chromatin immunoprecipitation analyses. The expression of miR-1275and JAZF1 were measured in specimens of GC and adjacent non cancerous tissues. RESULTS: We identified JAZF1 as a direct miR-1275 target. miR-1275 supresses migration and invasion of GC cells in vitro and in vivo, which was restored by JAZF1 overexpression. Moreover, JAZF1 was recognized as a direct regulator of Vimentin. Knocking-down miR-1275 or overexpressing JAZF1 resulted in upregulation of Vimentin but downregulation of E-cadherin. Meanwhile, we validated in 120 GC patients specimens that low miR-1275expression and high JAZF1 mRNA expression levels were closely associated with lymph node metastasis and poor prognosis. The expression of JAZF1 in protein level displayed the correlations with Vimentin but inversely with E-cadherin. CONCLUSIONS: Increased miR-1275 expression inhibited GC metastasis by regulating vimentin/E-cadherin via direct suppression of JAZF1expression, suggesting that miR-1275 is a tumour-suppressor miRNA with the potential as a prognostic biomarker or therapeutic target in GC.

Improved Prediction of Drug-Target Interactions Using Self-Paced Learning with Collaborative Matrix Factorization.

Identifying drug-target interactions (DTIs) plays an important role in the field of drug discovery, drug side-effects, and drug repositioning. However, in vivo or biochemical experimental methods for identifying new DTIs are extremely expensive and time-consuming. Recently, in silico or various computational methods have been developed for DTI prediction, such as ligand-based approaches and docking approaches, but these traditional computational methods have several limitations. This work utilizes the chemogenomic-based approaches for efficiently identifying potential DTI candidates, namely, self-paced learning with collaborative matrix factorization based on weighted low-rank approximation (SPLCMF) for DTI prediction, which integrates multiple networks related to drugs and targets into regularized least-squares and focuses on learning a low-dimensional vector representation of features. The SPLCMF framework can select samples from easy to complex into training by using soft weighting, which is inclined to more faithfully reflect the latent importance of samples in training. Experimental results on synthetic data and five benchmark data sets show that our proposed SPLCMF outperforms other existing state-of-the-art approaches. These results indicate that our proposed SPLCMF can provide a useful tool to predict unknown DTIs, which may provide new insights into drug discovery, drug side-effect prediction, and repositioning existing drug.

Structural Analysis and Identification of Colloidal Aggregators in Drug Discovery.

Aggregation has been posing a great challenge in drug discovery. Current computational approaches aiming to filter out aggregated molecules based on their similarity to known aggregators, such as Aggregator Advisor, have low prediction accuracy, and therefore development of reliable in silico models to detect aggregators is highly desirable. In this study, we built a data set consisting of 12 119 aggregators and 24 172 drugs or drug candidates and then developed a group of classification models based on the combination of two ensemble learning approaches and five types of molecular representations. The best model yielded an accuracy of 0.950 and an area under the curve (AUC) value of 0.987 for the training set, and an accuracy of 0.937 and an AUC of 0.976 for the test set. The best model also gave reliable predictions to the external validation set with 5681 aggregators since 80% of molecules were predicted to be aggregators with a prediction probability higher than 0.9. More importantly, we explored the relationship between colloidal aggregation and molecular features, and generalized a set of simple rules to detect aggregators. Molecular features, such as log D, the number of hydroxyl groups, the number of aromatic carbons attached to a hydrogen atom, and the number of sulfur atoms in aromatic heterocycles, would be helpful to distinguish aggregators from nonaggregators. A comparison with numerous existing druglikeness and aggregation filtering rules and models used in virtual screening verified the high reliability of the model and rules proposed in this study. We also used the model to screen several curated chemical databases, and almost 20% of molecules in the evaluated databases were predicted as aggregators, highlighting the potential high risk of aggregation in screening. Finally, we developed an online Web server of ChemAGG ( http://admet.scbdd.com/ChemAGG/index ), which offers a freely available tool to detect aggregators.

Spontaneous renal hemorrhage secondary to choriocarcinoma in a man with congenital hypospadias and cryptorchidism: a case report and literature review.

BACKGROUND: Choriocarcinoma is a rare malignant germ-cell tumour, most commonly found in adult women. It infrequently presents as spontaneous renal haemorrhage (SRH). Genital malformation and SRH secondary to choriocarcinoma has previously been only reported in females. We present what we believe to be the first case of a male patient with genital malformation (hypospadias and cryptorchidism) and SRH at presentation of choriocarcinoma. CASE PRESENTATION: A 25-year-old man presented to the department with intense pain in the right flank region and lower back. Initial investigations showed spontaneous renal haemorrhage, for which an emergency partial nephrectomy was performed. Clinical, radiological, and pathological investigations suggested a diagnosis of testicular choriocarcinoma with metastases to the right kidney, both lungs, and brain. Initial treatment was with a chemotherapy regimen of cisplatin, etoposide and bleomycin and whole brain radiotherapy; however, 6 months after diagnosis the patient developed liver metastasis, after which time the BEP protocol was switched to ITP with oral apatinib. Despite best efforts, the liver and lung metastasis continued to grow and a decision was made to discontinue active treatment and provide only palliative care until the patient passed away. CONCLUSION: Choriocarcinoma is a difficult cancer to diagnose pre-operatively. In male patients with early metastasis, prognosis may be much poorer than in the commoner gestational choriocarcinoma. A multidisciplinary with comprehensive post-surgical intervention is of great importance in the treatment of these patients.

Utilization of microdroplets as optical lenses for surface-enhanced Raman spectroscopy (SERS) enhancement on localized silver nanoparticle-decorated porous silicon substrates.

Surface-enhanced Raman spectroscopy (SERS) is a widely used analytical technique known for its high sensitivity and broad applicability. Despite its potential, SERS faces challenges related to detection sensitivity and reproducibility. This study proposes an innovative method to enhance SERS performance by employing water microdroplets as optical lenses on localized silver nanoparticle-decorated porous silicon (LocAg-PS) substrates. The hydrophobic nature of the LocAg-PS substrate not only ensures precise positioning of the microdroplet lenses on the silver nanoparticle grafted pad (AgNP pad) but also forms a plano-convex-like microdroplet lens for the focusing of the excitation laser and the collection of scattered light. Experimental results demonstrate that using microdroplet lenses enhances the SERS signal intensity and reproducibility, providing a rapid and cost-effective solution for advanced SERS analysis.

Design and analysis of a lower limb assistive exoskeleton robot.

BACKGROUND: In recent years, exoskeleton robot technology has developed rapidly. Exoskeleton robots that can be worn on a human body and provide additional strength, speed or other abilities. Exoskeleton robots have a wide range of applications, such as medical rehabilitation, logistics and disaster relief and other fields. OBJECTIVE: The study goal is to propose a lower limb assistive exoskeleton robot to provide extra power for wearers. METHODS: The mechanical structure of the exoskeleton robot was designed by using bionics principle to imitate human body shape, so as to satisfy the coordination of man-machine movement and the comfort of wearing. Then a gait prediction method based on neural network was designed. In addition, a control strategy according to iterative learning control was designed. RESULTS: The experiment results showed that the proposed exoskeleton robot can produce effective assistance and reduce the wearer's muscle force output. CONCLUSION: A lower limb assistive exoskeleton robot was introduced in this paper. The kinematics model and dynamic model of the exoskeleton robot were established. Tracking effects of joint angle displacement and velocity were analyzed to verify feasibility of the control strategy. The learning error of joint angle can be improved with increase of the number of iterations. The error of trajectory tracking is acceptable.

[Spatiotemporal Evolution and Quantitative Attribution Analysis of Vegetation NDVI in Greater Khingan Mountains Forest-Steppe Ecotone].

It is of great significance to explore the dynamic variations in vegetation cover and to identify its driving factors for the restoration and sustainable development of the regional ecological environment. Based on MODIS NDVI data from 2000 to 2020 and contemporaneous meteorological, DEM, land use type, and other data, the spatiotemporal variation characteristics of vegetation in the Greater Khingan Mountains forest-steppe ecotone were deeply analyzed, and its future evolution pattern was predicted by using the methods of Sen+Mann-Kendall trend analysis and Hurst index. At the same time, the influence degree and mechanism of each detection factor and its interaction on vegetation spatial differentiation at the scale of the whole area and different physical geographic divisions were quantitatively revealed by introducing the GeoDetector model. The results showed that:① In terms of spatiotemporal variation, the spatiotemporal heterogeneity of NDVI in the Greater Khingan Mountains forest-steppe ecotone was obvious from 2000 to 2020. Temporally, NDVI fluctuated growth at a rate of 0.002 a-1 (P < 0.05) and underwent an upward mutation in 2011. Spatially, NDVI showed a distribution pattern of "increasing from southwest to northeast," and the NDVI grade transfer was mainly "medium vegetation cover→medium-high vegetation cover" during the 21 years, and the area of vegetation improvement was much larger than that of degradation. ② In terms of trend prediction, the future variation trend of NDVI in the Greater Khingan Mountains forest-steppe ecotone was mainly continuous improvement, accounting for 37%, but was mostly weakly sustained. ③ In terms of driving mechanism, the wind speed, evaporation, and relative humidity had the most significant influence on the spatial differentiation of NDVI over the whole area. The influence of natural factors has been decreasing over the past 21 years, whereas the influence of human factors has been increasing, and the main driving factors of NDVI spatial differentiation were quite different in different vegetation, climate, soil, and geomorphic zones. The synergistic effect between each factor at different spatial scales all showed two-factor or non-linear enhancement relationships, which was significantly enhanced compared with the single-factor effect. This study contributes to clarifying the causes of ecological fragility in the forest-steppe ecotone in the northern cold region and provides scientific support for formulating differentiated protection and management plans for vegetation resources under different environmental conditions.