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Molecular property prediction (MPP) is a crucial and fundamental task for AI-aided drug discovery (AIDD). Recent studies have shown great promise of applying self-supervised learning (SSL) to producing molecular representations to cope with the widely-concerned data scarcity problem in AIDD. As some specific substructures of molecules play important roles in determining molecular properties, molecular representations learned by deep learning models are expected to attach more importance to such substructures implicitly or explicitly to achieve better predictive performance. However, few SSL pre-trained models for MPP in the literature have ever focused on such substructures. To challenge this situation, this paper presents a C hemistry- A ware F ragmentation for E ffective MPP (CAFE-MPP in short) under the self-supervised contrastive learning framework. First, a novel fragment-based molecular graph (FMG) is designed to represent the topological relationship between chemistry-aware substructures that constitute a molecule. Then, with well-designed hard negative pairs, a is pre-trained on fragment-level by contrastive learning to extract representations for the nodes in FMGs. Finally, a Graphormer model is leveraged to produce molecular representations for MPP based on the embeddings of fragments. Experiments on 11 benchmark datasets show that the proposed CAFE-MPP method achieves state-of-the-art performance on 7 of the 11 datasets and the second-best performance on 3 datasets, compared with six remarkable self-supervised methods. Further investigations also demonstrate that CAFE-MPP can learn to embed molecules into representations implicitly containing the information of fragments highly correlated to molecular properties, and can alleviate the over-smoothing problem of graph neural networks. [ABSTRACT FROM AUTHOR]

Motivation Contrastive learning has been widely used as pretext tasks for self-supervised pre-trained molecular representation learning models in AI-aided drug design and discovery. However, existing methods that generate molecular views by noise-adding operations for contrastive learning may face the semantic inconsistency problem, which leads to false positive pairs and consequently poor prediction performance. Results To address this problem, in this article, we first propose a semantic-invariant view generation method by properly breaking molecular graphs into fragment pairs. Then, we develop a Fragment-based Semantic-Invariant Contrastive Learning (FraSICL) model based on this view generation method for molecular property prediction. The FraSICL model consists of two branches to generate representations of views for contrastive learning, meanwhile a multi-view fusion and an auxiliary similarity loss are introduced to make better use of the information contained in different fragment-pair views. Extensive experiments on various benchmark datasets show that with the least number of pre-training samples, FraSICL can achieve state-of-the-art performance, compared with major existing counterpart models. Availability and implementation The code is publicly available at https://github.com/ZiqiaoZhang/FraSICL. [ABSTRACT FROM AUTHOR]

The current evaluation of M&A performance lacks consideration of M&A motives. In this paper, we theoretically analyse and empirically test the effect of network synergy generated by M&A on the degree of realization of corporate M&A motives and the mechanism of its effect by constructing an equity network between a listed company and its subsidiaries within the company. The results show that the greater the variation of internal network node degree and strength, the more beneficial it is to promote the degree of realization of corporate M&A motivation; the results of further mechanism of action tests show that the variation of network node degree and strength has significant effects on economies of scale, economies of scope, and transaction costs; Furthermore, the heterogeneity test finds that the effect of variation of network node degree and strength to promote the degree of realization of corporate M&A motivation is more significant in the case of non-cash payment method and related M&A. This paper extends the study of complex networks to the field of M&A and uniquely explains the paradox of the "high failure rate" of M&A and the increasing activity of M&A activities from the perspective of network synergy, which helps to rationalize the M&A behavior of enterprises and further regulate the M&A behavior of listed companies by regulatory authorities. [ABSTRACT FROM AUTHOR]

Motivation Accurately predicting drug–target interaction (DTI) is a crucial step to drug discovery. Recently, deep learning techniques have been widely used for DTI prediction and achieved significant performance improvement. One challenge in building deep learning models for DTI prediction is how to appropriately represent drugs and targets. Target distance map and molecular graph are low dimensional and informative representations, which however have not been jointly used in DTI prediction. Another challenge is how to effectively model the mutual impact between drugs and targets. Though attention mechanism has been used to capture the one-way impact of targets on drugs or vice versa, the mutual impact between drugs and targets has not yet been explored, which is very important in predicting their interactions. Results Therefore, in this article we propose MINN-DTI, a new model for DTI prediction. MINN-DTI combines an interacting-transformer module (called Interformer) with an improved Communicative Message Passing Neural Network (CMPNN) (called Inter-CMPNN) to better capture the two-way impact between drugs and targets, which are represented by molecular graph and distance map, respectively. The proposed method obtains better performance than the state-of-the-art methods on three benchmark datasets: DUD-E, human and BindingDB. MINN-DTI also provides good interpretability by assigning larger weights to the amino acids and atoms that contribute more to the interactions between drugs and targets. Availability and implementation The data and code of this study are available at https://github.com/admislf/MINN-DTI. [ABSTRACT FROM AUTHOR]

Motivation Molecular property prediction is a hot topic in recent years. Existing graph-based models ignore the hierarchical structures of molecules. According to the knowledge of chemistry and pharmacy, the functional groups of molecules are closely related to its physio-chemical properties and binding affinities. So, it should be helpful to represent molecular graphs by fragments that contain functional groups for molecular property prediction. Results In this article, to boost the performance of molecule property prediction, we first propose a definition of molecule graph fragments that may be or contain functional groups, which are relevant to molecular properties, then develop a fragment-oriented multi-scale graph attention network for molecular property prediction, which is called FraGAT. Experiments on several widely used benchmarks are conducted to evaluate FraGAT. Experimental results show that FraGAT achieves state-of-the-art predictive performance in most cases. Furthermore, our case studies show that when the fragments used to represent the molecule graphs contain functional groups, the model can make better predictions. This conforms to our expectation and demonstrates the interpretability of the proposed model. Availability and implementation The code and data underlying this work are available in GitHub, at https://github.com/ZiqiaoZhang/FraGAT. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

• The first work using structural and physiochemical features to predict toxicity. • A novel end-to-end framework for toxicity prediction is proposed. • A data augmentation method is developed to enrich the training data. • Extensive experiments are conducted to evaluate the proposed method. At the early stages of the drug discovery, molecule toxicity prediction is crucial to excluding drug candidates that are likely to fail in clinical trials. In this paper, we presented a novel molecular representation method and developed a corresponding deep learning-based framework called TOP (the abbreviation of TOxicity Prediction). TOP integrates specifically designed data preprocessing methods, an RNN based on bidirectional gated recurrent unit (BiGRU), and fully connected neural networks for end-to-end molecular representation learning and chemical toxicity prediction. TOP can automatically learn a mixed molecular representation from not only SMILES contextual information that describes the molecule structure, but also physiochemical properties. Therefore, TOP can overcome the drawbacks of existing methods that use either of them, thus greatly promotes toxicity prediction accuracy. We conducted extensive experiments over 14 classic toxicity prediction tasks on three different benchmark datasets, including balanced and imbalanced ones. The results show that, with the help of the novel molecular representation method, TOP significantly outperforms not only three baseline machine learning methods, but also five state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Community structure is an important structure feature of complex networks. Due to its speed, effectiveness and simplicity, the Louvain algorithm is widely used to detect community structure planted in the network topology. Speeding up the Louvain algorithm, enabling the analysis of larger graphs in a shorter time and maintaining the accuracy of result, can benefit the research of networks in many fields. We here propose the Random Self-adaptive Neighbors Louvain algorithm as a new improved Louvain algorithm. The principle of small probability event is used to infer the number of neighbors to pick up randomly. The accuracy, speed and fluctuation of our method are compared with those of the original Louvain algorithm and the Random Neighbor Louvain algorithm. The results show that the RSNL can obtain as good partition as that of the original Louvain in a faster speed. On the networks without distinct community structures, the RSNL is faster and more accurate than the RNL. A new measure, equivalent computing time, is proposed to show the expectation runtime of the algorithm to obtain a relatively good partition. The comparison of this measure shows that the RSNL algorithm can make the best performance among the three algorithms in most cases. [ABSTRACT FROM AUTHOR]

In the process of manufacturing, a large amount of manufacturing data is produced by different departments and different domain. In order to realise data sharing and linkage among supply chains, master data management method has been used. Through master data management, the key data can be shared and distributed uniformly. However, since these cross-domain data form a data network through the association of master data, how to evaluate the effectiveness and rationality of this network becomes the major issue in the proposed method. In this paper, a model of the master data network is built based on the theory of set pair analysis. In order to verify the master data, an evaluation method for the network is proposed. Finally, a case was presented to validate this network model and evaluation method. [ABSTRACT FROM AUTHOR]

Element doping strategies are commonly employed to achieve effective photocatalysts. In this work, a novel form of P and Ce co-doped g-C 3 N 4 nanosheets was synthesized using a one-step thermal polymerization technique, exhibiting remarkable efficiency in the degradation of organic pollutants under visible light. The prepared catalysts underwent comprehensive characterization through XRD, FT-IR, SEM, TEM, BET, XPS, UV–vis, PL, and electrochemical testing. The electronic and optical properties of the catalysts were further elucidated using first-principles density functional theory (DFT). In the photocatalytic experiment of degrading Rhodamine B (RhB), the photocatalytic ability of g-C 3 N 4 nanosheets was improved at different degrees, among which g-C 3 N 4 nanosheets co-doped with P and Ce showed the highest photocatalytic activity, with a degradation efficiency of 90.58 % for RhB. Furthermore, the photocatalytic mechanism was confirmed through free radical capture experiments. This study presents a novel approach for the development of efficient photocatalysts to remove pollutants. [Display omitted] • A new type of P and Ce co -doped g-C 3 N 4 nanosheets were prepared by one-step thermal polymerization method. • P, Ce/CN photocatalysts exhibit fast charge transfer rate and high photocatalytic activity in RhB degradation. • Improved light absorption and reduced electron-hole recombination are crucial to improving photocatalytic activity. • The possible photocatalytic mechanisms were proposed though free radical capture experiments and DFT analysis. [ABSTRACT FROM AUTHOR]

Glaucoma is an eye disease with a high rate of blindness and a complex pathogenesis. Ocular hypertension (OHT) is a critical risk factor, and retinal ischemia/reperfusion (I/R) is an important pathophysiological basis. This study was designed to investigate the retinal neuroprotective effect of oral naringenin in an acute retinal I/R model and a chronic OHT model and the possible mechanism involved. After the I/R and OHT models were established, mice were given vehicle or naringenin (100 mg/kg or 300 mg/kg). Hematoxylin-eosin (HE) staining and immunostaining of RBPMS and glial fibrillary acidic protein (GFAP) were used to evaluate retinal injury. GFAP, CD38, Sirtuin1 (SIRT1), and NOD-like receptor protein 3 (NLRP3) expression levels were measured by Western blotting. In the OHT model, intraocular pressure (IOP) was dynamically maintained at approximately 20–25 mmHg after injury. The retinal structure was damaged, and retinal ganglion cells (RGCs) were lost in both models. Naringenin ameliorated the abovementioned indications but also demonstrated that high concentrations of naringenin significantly inhibited retinal astrocyte activation and inhibited damage-induced increases in the expression of GFAP, NLRP3, and CD38 proteins, while SIRT1 protein expression was upregulated. This study showed for the first time that naringenin can reduce microbead-induced IOP elevation in the OHT model, providing new evidence for the application of naringenin in glaucoma. Naringenin may mediate the CD38/SIRT1 signaling pathway, inhibit astrocyte activation, and ultimately exert an anti-inflammatory effect to achieve retinal neuroprotection. • Naringenin reduces IOP. • Naringenin protects retinal structures and retinal ganglion cell survival in I/R and OHT models. • Naringenin decreased astrocyte activation in the I/R and OHT models. • Naringenin affects GFAP, CD38, SIRT1, and NLRP3 protein expression in I/R and OHT models. [ABSTRACT FROM AUTHOR]

Online Social Networks generate a prodigious wealth of real-time information at an incessant rate. In this paper we study the empirical data that crawled from Twitter to describe the topology and information spreading dynamics of Online Social Networks. We propose a measurement with three measures to state the efforts of users on Twitter to get their information spreading, based on the unique mechanisms for information retransmission on Twitter. It is noticed that small fraction of users with special performance on participation can gain great influence, while most other users play a role as middleware during the information propagation. Thus a community analysis is performed and four categories of users are found with different kinds of participation that cause the information dissemination dynamics. These suggest that exiting topological measures alone may reflect little about the influence of individuals and provide new insights for information spreading. [ABSTRACT FROM AUTHOR]