Traditional Chinese Medicine (TCM) has a long history of widespread clinical applications, especially in East Asia, and is becoming frequently used in Western countries. However, owing to extreme complicacy in both chemical ingredients and mechanism of action, a deep understanding of TCM is still difficult. To accelerate the modernization and popularization of TCM, a single comprehensive database is required, containing a wealth of TCM-related information and equipped with complete analytical tools. Here we present YaTCM (Yet another Traditional Chinese Medicine database), a free web-based toolkit, which provides comprehensive TCM information and is furnished with analysis tools. YaTCM allows a user to (1) identify the potential ingredients that are crucial to TCM herbs through similarity search and substructure search, (2) investigate the mechanism of action for TCM or prescription through pathway analysis and network pharmacology analysis, (3) predict potential targets for TCM molecules by multi-voting chemical similarity ensemble approach, and (4) explore functionally similar herb pairs. All these functions can lead to one systematic network for visualization of TCM recipes, herbs, ingredients, definite or putative protein targets, pathways, and diseases. This web service would help in uncovering the mechanism of action of TCM, revealing the essence of TCM theory and then promoting the drug discovery process. YaTCM is freely available at http://cadd.pharmacy.nankai.edu.cn/yatcm/home.

Human immunodeficiency virus type-1 and hepatitis C virus (HIV/HCV) coinfection occurs when a patient is simultaneously infected with both human immunodeficiency virus type-1 (HIV-1) and hepatitis C virus (HCV), which is common today in certain populations. However, the treatment of coinfection is a challenge because of the special considerations needed to ensure hepatic safety and avoid drug–drug interactions. Multitarget inhibitors with less toxicity may provide a promising therapeutic strategy for HIV/HCV coinfection. However, the identification of one molecule that acts on multiple targets simultaneously by experimental evaluation is costly and time-consuming. In silico target prediction tools provide more opportunities for the development of multitarget inhibitors. In this study, by combining Naïve Bayes (NB) and support vector machine (SVM) algorithms with two types of molecular fingerprints, MACCS and extended connectivity fingerprints 6 (ECFP6), 60 classification models were constructed to predict compounds that were active against 11 HIV-1 targets and four HCV targets based on a multiple quantitative structure–activity relationships (multiple QSAR) method. Five-fold cross-validation and test set validation were performed to measure the performance of the 60 classification models. Our results show that the 60 multiple QSAR models appeared to have high classification accuracy in terms of the area under the ROC curve (AUC) values, which ranged from 0.83 to 1 with a mean value of 0.97 for the HIV-1 models and from 0.84 to 1 with a mean value of 0.96 for the HCV models. Furthermore, the 60 models were used to comprehensively predict the potential targets of an additional 46 compounds, including 27 approved HIV-1 drugs, 10 approved HCV drugs and nine selected compounds known to be active against one or more targets of HIV-1 or HCV. Finally, 20 hits, including seven approved HIV-1 drugs, four approved HCV drugs, and nine other compounds, were predicted to be HIV/HCV coinfection multitarget inhibitors. The reported bioactivity data confirmed that seven out of nine compounds actually interacted with HIV-1 and HCV targets simultaneously with diverse binding affinities. The remaining predicted hits and chemical-protein interaction pairs with the potential ability to suppress HIV/HCV coinfection are worthy of further experimental investigation. This investigation shows that the multiple QSAR method is useful in predicting chemical-protein interactions for the discovery of multitarget inhibitors and provides a unique strategy for the treatment of HIV/HCV coinfection.

The reciprocating pump plays an important role in the petrochemical industry procedure, it is crucial in ensuring the systematic safety and stability. Since the useful feature information of the vibration signal from the reciprocating pump tends to be overwhelmed by the background ingredients, it is tough to realize the recognition on typical modes. Aiming at the extraction of reciprocating mechanical fault features and mode recognition, this paper proposes Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise and LSTM (Long Short-Term Memory) deep neural network algorithm. Firstly, the IMF components are obtained by decomposing the vibration signals from the reciprocating pump with the Improved CEEMDAN algorithm, in which the key parameter β k is improved and redefined, for optimizing SNRs (Signal Noise Ratio) of the IMF (Intrinsic Mode Function) components. Then the corresponding singular spectral entropy is calculated and the feature vector is constructed. The classification modal based on LSTM deep network is developed in the data dividing-training and the final mode recognition process.The study shows that the proposed method can effectively extract the fault features of vibration signal of the reciprocating pump, and the testing modes could be accurately recognized with the developed classification model.

To better leverage the accumulated bioactivity data in the ChEMBL database, we have developed Bioactivity-explorer, a web application for interactive visualization and exploration of the large-scale bioactivity data in ChEMBL. Mining and integration of the Therapeutic Target Database disease-target mapping into the ChEMBL database has enabled Bioactivity-explorer to include 493,430 scaffolds, 31,400,000 matched molecular pairs, 1330,220 target–target interactions in terms of shared active compounds, 4526,718 target–target interactions in terms of shared active scaffolds, 97,041,700 molecule–molecule interactions and 14,974 disease-target mappings. This web tool is available at http://cadd.pharmacy.nankai.edu.cn/b17r . The source codes of the front end and back end, released under MIT license, can be found at GitHub. Electronic supplementary material The online version of this article (10.1186/s13321-019-0370-7) contains supplementary material, which is available to authorized users.

Although intensive studies have been conducted on layered transition metal oxide(TMO)-based cathode materials and metal oxide-based anode materials for Li-ion batteries, their precursors generally follow different or even complex synthesis routes. To share one route for preparing precursors of the cathode and anode materials, herein, we demonstrate a facile co-precipitation method to fabricate Ni-rich hydroxide precursors of Ni0.8Co0.1Mn0.1(OH)2. Ni-rich layered oxide of LiNi0.8Co0.1Mn0.1O2 is obtained by lithiation of the precursor in air. An NiO-based anode material is prepared by calcining the precursor or multi-walled carbon nanotubes (MWCNTs) incorporated precursors. The pre-addition of ammonia solution can simplify the co-precipitation procedures and the use of an air atmosphere can also make the heat treatment facile. LiNi0.8Co0.1Mn0.1O2 as the cathode material delivers a reversible capacity of 194 mA h g-1 at 40 mA g-1 and a notable cycling retention of 88.8% after 100 cycles at 200 mA g-1. This noticeable performance of the cathode arises from a decent particle morphology and high crystallinity of the layered oxides. As the anode material, the MWCNTs-incorporated oxides deliver a much higher reversible capacity of 811.1 mA h g-1 after 200 cycles compared to the pristine oxides without MWCNTs. The improvement on electrochemical performance can be attributed to synergistic effects from MWCNTs incorporation, including reinforced electronic conductivity, rich meso-pores and an alleviated volume effect. This facile and sharing method may offer an integrated and economical approach for commercial production of Ni-rich electrode materials for Li-ion batteries.