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.

Circular RNA RRM2 alleviates metabolic dysfunction-associated steatotic liver disease by targeting miR-142-5p to increase NRG1 expression.

Metabolic dysfunction-associated steatotic liver disease (MASLD) is a prevalent chronic liver condition worldwide, demanding further investigation into its pathogenesis. Circular RNAs (circRNAs) are emerging as pivotal regulators in MASLD processes, yet their pathological implications in MASLD remain poorly understood. This study focused on elucidating the role of circular RNA ribonucleotide reductase subunit M2 (circRRM2) in MASLD progression. In this study, we used both in vitro and in vivo MASLD models using long-chain-free fatty acid (FFA)-treated hepatocytes and high-fat diet (HFD)-induced MASLD in mice, respectively. We determined the expression patterns of circRRM2, microRNA-142-5p (miR-142-5p), and neuregulin 1 (NRG1) in livers of MASLD-afflicted mice and MASLD hepatocytes by RT-qPCR. Dual-luciferase reporter assays verified the binding relationships among circRRM2, miR-142-5p, and NRG1. We conducted further analyses of their roles in MASLD hepatocytes and modulated circRRM2, miR-142-5p, and NRG1 expression in vitro by transfection. Our findings were validated in vivo. The results demonstrated reduced levels of circRRM2 and NRG1, along with elevated miR-142-5p expression in MASLD livers and hepatocytes. Overexpression of circRRM2 downregulated lipogenesis-related genes and decreased triglycerides accumulation in livers of MASLD mice. MiR-142-5p, which interacts with circRRM2, effectively counteracted the effects of circRRM2 in MASLD hepatocytes. Furthermore, NRG1 was identified as a miR-142-5p target, and its overexpression mitigated the regulatory impact of miR-142-5p on MASLD hepatocytes. In conclusion, circRRM2, via its role as a miR-142-5p sponge, upregulating NRG1, possibly influenced triglycerides accumulation in both in vitro and in vivo MASLD models.NEW & NOTEWORTHY CircRRM2 expression was downregulated in free fatty acid (FFA)-challenged hepatocytes and high-fat diet (HFD) fed mice. Overexpressed circular RNA ribonucleotide reductase subunit M2 (circRRM2) attenuated metabolic dysfunction-associated steatotic liver disease (MASLD) development by suppressing FFA-induced triglycerides accumulation. CircRRM2 targeted microRNA-142-5p (miR-142-5p), which served as an upstream inhibitor of neuregulin 1 (NRG1) and collaboratively regulated MASLD progression.

H/D Exchange of Aromatic Sulfones via Base Promotion and Silver Catalysis.

Aromatic sulfones are the prevailing scaffolds in pharmaceutical and material sciences. However, compared to their widespread application, the selective deuterium labeling of these structures is restricted due to their electron-deficient properties. This study presents two comprehensive strategies for the deuteration of aromatic sulfones. The base-promoted deuteration uses DMSO-d6 as the deuterium source, resulting in a rapid H/D exchange within 2 h. Meanwhile, a silver-catalyzed protocol offers a much milder option by using economical D2O to furnish the labeled sulfones.

Diagnosis of membranous supravalvular aortic stenosis with severe aortic valve insufficiency.

Although the clinical manifestations of membranous supravalvular aortic stenosis (SVAS) are distinctive, its diagnosis remains challenging. Failure to initiate surgical treatment in a timely manner greatly increases the risk of sudden cardiac death. We report a case of membranous SVAS, detailing the clinical presentation and imaging manifestations.

Analysis of the effect of changes in serum osteopontin levels on patients with acute cerebral infarction.

Objective: To investigate the correlation of serum osteopontin levels with disease severity and prognosis in patients with acute cerebral infarction. Methods: This retrospective analysis included forty patients with acute cerebral infarction (ACI) admitted to the Department of Neurology of Baoding Children's Hospital from May, 2019 to May, 2022 within 24 hours of onset were selected as the observation group, while 40 healthy subjects in our hospital during the same period were selected as the control group. The correlation between serum Osteopontin (OPN) levels and risk factors on one day, seven days and 14 days was analyzed. Patients in the observation group were subdivided into the good prognosis group and the poor prognosis group according to mRS score, and the serum OPN levels of the two groups were compared. The correlation between serum OPN and disease severity and prognosis of patients with ACI was analyzed. Results: The serum OPN levels in the observation group were significantly higher than those in control group (P< 0.05), and its level was positively correlated with NIHSS score and infarct size. The proportion of patients with hyperlipidemia, smoking, drinking, hypertension and OPN level on seven day in the poor prognosis group were higher than those in the good prognosis group (P<0.05). The OPN level > 8.720 ng/ml on seven days was an independent risk factor for poor prognosis of cerebral infarction. Conclusion: OPN is involved in the entire pathophysiological process of ACI, and its level can predict the severity of the disease in patients with ACI, and can be used as an important indicator for evaluating their clinical prognosis.

Learning to SMILES: BAN-based strategies to improve latent representation learning from molecules.

Computational methods have become indispensable tools to accelerate the drug discovery process and alleviate the excessive dependence on time-consuming and labor-intensive experiments. Traditional feature-engineering approaches heavily rely on expert knowledge to devise useful features, which could be costly and sometimes biased. The emerging deep learning (DL) methods deliver a data-driven method to automatically learn expressive representations from complex raw data. Inspired by this, researchers have attempted to apply various deep neural network models to simplified molecular input line entry specification (SMILES) strings, which contain all the composition and structure information of molecules. However, current models usually suffer from the scarcity of labeled data. This results in a low generalization ability of SMILES-based DL models, which prevents them from competing with the state-of-the-art computational methods. In this study, we utilized the BiLSTM (bidirectional long short term merory) attention network (BAN) in which we employed a novel multi-step attention mechanism to facilitate the extracting of key features from the SMILES strings. Meanwhile, SMILES enumeration was utilized as a data augmentation method in the training phase to substantially increase the number of labeled data and enlarge the probability of mining more patterns from complex SMILES. We again took advantage of SMILES enumeration in the prediction phase to rectify model prediction bias and provide a more accurate prediction. Combined with the BAN model, our strategies can greatly improve the performance of latent features learned from SMILES strings. In 11 canonical absorption, distribution, metabolism, excretion and toxicity-related tasks, our method outperformed the state-of-the-art approaches.

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.

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.

MG-BERT: leveraging unsupervised atomic representation learning for molecular property prediction.

MOTIVATION: Accurate and efficient prediction of molecular properties is one of the fundamental issues in drug design and discovery pipelines. Traditional feature engineering-based approaches require extensive expertise in the feature design and selection process. With the development of artificial intelligence (AI) technologies, data-driven methods exhibit unparalleled advantages over the feature engineering-based methods in various domains. Nevertheless, when applied to molecular property prediction, AI models usually suffer from the scarcity of labeled data and show poor generalization ability. RESULTS: In this study, we proposed molecular graph BERT (MG-BERT), which integrates the local message passing mechanism of graph neural networks (GNNs) into the powerful BERT model to facilitate learning from molecular graphs. Furthermore, an effective self-supervised learning strategy named masked atoms prediction was proposed to pretrain the MG-BERT model on a large amount of unlabeled data to mine context information in molecules. We found the MG-BERT model can generate context-sensitive atomic representations after pretraining and transfer the learned knowledge to the prediction of a variety of molecular properties. The experimental results show that the pretrained MG-BERT model with a little extra fine-tuning can consistently outperform the state-of-the-art methods on all 11 ADMET datasets. Moreover, the MG-BERT model leverages attention mechanisms to focus on atomic features essential to the target property, providing excellent interpretability for the trained model. The MG-BERT model does not require any hand-crafted feature as input and is more reliable due to its excellent interpretability, providing a novel framework to develop state-of-the-art models for a wide range of drug discovery tasks.

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.

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/.

Systematic comparison of ligand-based and structure-based virtual screening methods on poly (ADP-ribose) polymerase-1 inhibitors.

The poly (ADP-ribose) polymerase-1 (PARP1) has been regarded as a vital target in recent years and PARP1 inhibitors can be used for ovarian and breast cancer therapies. However, it has been realized that most of PARP1 inhibitors have disadvantages of low solubility and permeability. Therefore, by discovering more molecules with novel frameworks, it would have greater opportunities to apply it into broader clinical fields and have a more profound significance. In the present study, multiple virtual screening (VS) methods had been employed to evaluate the screening efficiency of ligand-based, structure-based and data fusion methods on PARP1 target. The VS methods include 2D similarity screening, structure-activity relationship (SAR) models, docking and complex-based pharmacophore screening. Moreover, the sum rank, sum score and reciprocal rank were also adopted for data fusion methods. The evaluation results show that the similarity searching based on Torsion fingerprint, six SAR models, Glide docking and pharmacophore screening using Phase have excellent screening performance. The best data fusion method is the reciprocal rank, but the sum score also performs well in framework enrichment. In general, the ligand-based VS methods show better performance on PARP1 inhibitor screening. These findings confirmed that adding ligand-based methods to the early screening stage will greatly improve the screening efficiency, and be able to enrich more highly active PARP1 inhibitors with diverse structures.

Awareness, Attitude, and Knowledge Among the Healthcare Workers in China at the Onset of the Oversea Monkeypox Outbreak.

BACKGROUND: The outbreak of monkeypox in several nonendemic countries has been reported since May 2022. In the context of the COVID-19 pandemic, it is important to examine how healthcare workers (HCWs) respond to the monkeypox epidemic. Having been involved in the fight against COVID-19 resurgence for nearly 3 years, how HCWs in China respond to the oversea monkeypox outbreak remains unclear. OBJECTIVE: To investigate the awareness, perceived risk, attitude and knowledge about monkeypox among HCWs in China. DESIGN: A cross-sectional survey. PARTICIPANTS: Physicians and nurses from 13 hospitals in Suizhou, China, were contacted through membership of the Physicians' and Nurses' Association. MAIN MEASURES: Responses regarding their awareness, risk perception, attitude, behavior, and knowledge about the outbreak of monkeypox were collected anonymously during the second month of the outbreak between 15 and 21 June 2022. KEY RESULTS: Of the 395 physician and 1793 nurse respondents, most had heard of the oversea monkeypox outbreak (physicians 93%, nurses 88%). More than 30% thought there existed an infection risk for themselves or family members (physicians 42%, nurses 32%). Most agreed that HCWs should pay attention to the outbreak (physicians 98%, nurses 98%). More than half had actively sought expertise (physicians 62%, nurses 52%). Approximately half believed that monkeypox may be transmitted through sexual activity or respiratory droplets, or from mother to fetus in utero (physicians 50%, 62%, 55%; nurses 40%, 60%, and 48%, respectively). Some believed that mask-wearing, hand-washing, and glove-wearing can prevent monkeypox transmission (physicians 78%, 89%, 83%; nurses 77%, 86%, 76%, respectively). CONCLUSIONS: This study identified high awareness, high perceived risk, and pro-prevention attitudes among HCWs in China at the onset of the oversea multi-country monkeypox outbreak, but low levels of monkeypox-related knowledge. Immediate efforts are needed to fill in their knowledge gap, particularly regarding the transmission routes and prevention measures.

Pd-Catalyzed Regioselective Deuteration of Indole's C4-Position with Transient Directing Groups.

As a representative scaffold of alkaloids, indoles have been extensively subjected to deuteration, but the regioselective C4 labeling has not been achieved due to its low reactivity. In this work, a Pd-catalyzed deuterium labeling at the indole's C4 position has been developed under the strategy of transient directing, using D2O as a deuterium source. The substituent effect is found to be crucial in facilitating this H/D exchange process, where the reversing C-D bond formation favors an electron-enriched ligation contrary to its C-H halogenation counterpart.

Multideuteration of Nitroaromatics by Silver-Catalyzed Hydrogen-Isotope Exchange.

Silver-catalyzed deuteration of nitroaromatics has been achieved using D2O as the deuterium source. Distinct from the well-established directing group-guided hydrogen-isotope exchange, this protocol showed an interesting deuteration pattern, where considerable deuterium accumulation was observed around the aromatic rings. Controlling experiments suggested that the deuteration was initiated by a silver-promoted C-H activation. Therefore, a tentative two-stage deuteration mechanism involving aryl-silver species was proposed to explain the deuteration on meta- and para-positions.

Fe1-xNix(PO3)2/Ni2P Heterostructure for Boosting Alkaline Oxygen Evolution Reaction in Fresh Water and Real Seawater at High Current Density.

Oxygen evolution reaction (OER) is a limiting reaction for highly efficient water electrolysis. Thus, the development of cost-effective and highly efficient OER catalysts is the key to large-scale water electrolysis for hydrogen production. Herein, by using an interfacial engineering strategy, a unique nanoflower-like Fe1-xNix(PO3)2/Ni2P/NF heterostructure with abundant heterogeneous interfaces is successfully fabricated. The catalyst exhibits excellent OER catalytic activity in alkaline fresh water and alkaline natural seawater at high current densities, which only, respectively, requires overpotentials of 318 and 367 mV to drive 1000 mA cm-2 in fresh water and natural seawater both containing 1 M KOH. Furthermore, Fe1-xNix(PO3)2/Ni2P/NF demonstrates excellent durability, which can basically remain stable for 80 h during the electrocatalytic OER processes, respectively, in alkaline fresh water and natural seawater. This work provides a new construction strategy for designing highly efficient electrocatalysts for OER at high current densities both in alkaline fresh water and in natural seawater.

The confusing pelvic cystic mass: A case of giant prostatic utricle cyst diagnosed by ultrasound.

The giant prostatic utricle cyst, located behindthe bladder with removable irregular mixed echo, communicating with the urethraat the level of the seminal colliculus, was diagnosed by ultrasound andverified by pathology and surgery.

Multimodal imaging of morning glory syndrome with persistent hyperplastic primary vitreous.

Morning glory syndrome (MGS) and persistent hyperplastic primary vitreous (PHPV) are congenital abnormity, which may be related to the increased incidence of systemic abnormalities and retinal detachment，diagnosed by ultrasound, identified by CT, MRI, and with the confirmation of fundus examination.

Honey Bee Habitat Sharing Enhances Gene Flow of the Parasite Nosema ceranae.

Host-parasite co-evolution is a process of reciprocal, adaptive genetic change. In natural conditions, parasites can shift to other host species, given both host and parasite genotypes allow this. Even though host-parasite co-evolution has been extensively studied both theoretically and empirically, few studies have focused on parasite gene flow between native and novel hosts. Nosema ceranae is a native parasite of the Asian honey bee Apis cerana, which infects epithelial cells of mid-guts. This parasite successfully switched to the European honey bee Apis mellifera, where high virulence has been reported. In this study, we used the parasite N. ceranae and both honey bee species as model organisms to study the impacts of two-host habitat sharing on parasite diversity and virulence. SNVs (Single Nucleotide Variants) were identified from parasites isolated from native and novel hosts from sympatric populations, as well as novel hosts from a parapatric population. Parasites isolated from native hosts showed the highest levels of polymorphism. By comparing the parasites isolated from novel hosts between sympatric and parapatric populations, habitat sharing with the native host significantly enhanced parasite diversity, suggesting there is continuing gene flow of parasites between the two host species in sympatric populations.

The involvement of a floral scent in plant-honeybee interaction.

Volatile odors from flowers play an important role in plant-pollinator interaction. The honeybee is an important generalist pollinator of many plants. Here, we explored whether any components of the odors of a range of honeybee-pollinated plants are commonly involved in the interaction between plants and honeybees. We used a needle trap system to collect floral odors, and GC-MS analysis revealed nonanal was the only component scent detected in 12 different honeybee-pollinated flowers and not present in anemophilous plant species. For Ligustrum compactum, blooming flowers released significantly more nonanal than buds and faded flowers. For Sapium sebiferum, nonanal release through the day correlated with nectar secretion. Experimentally increasing nectar load in flowers of Sapium sebiferum, Ligustrum compactum, and Castanea henryi increased nonanal levels also. Nonanal was also detected in flower nectar and honeys from experimental colonies. Electroantennogram recordings and behavioral observations showed that untrained honeybees could detect and were strongly attracted to nonanal. We argue that nonanal persists in both honey and nectar odors facilitating a learned association between nonanal and food reward in honeybees.