Drug combinations have demonstrated high efficacy and low adverse side effects compared to single drug administration in cancer therapies and thus have drawn intensive attention from researchers and pharmaceutical enterprises. Due to the rapid development of high-throughput screening (HTS), the number of drug combination datasets available has increased tremendously in recent years. Therefore, there is an urgent need for a comprehensive database that is crucial to both experimental and computational screening of synergistic drug combinations. In this paper, we present DrugCombDB, a comprehensive database devoted to the curation of drug combinations from various data sources: (i) HTS assays of drug combinations; (ii) manual curations from the literature; and (iii) FDA Orange Book and external databases. Specifically, DrugCombDB includes 448 555 drug combinations derived from HTS assays, covering 2887 unique drugs and 124 human cancer cell lines. In particular, DrugCombDB has more than 6000 000 quantitative dose responses from which we computed multiple synergy scores to determine the overall synergistic or antagonistic effects of drug combinations. In addition to the combinations extracted from existing databases, we manually curated 457 drug combinations from thousands of PubMed publications. To benefit the further experimental validation and development of computational models, multiple datasets that are ready to train prediction models for classification and regression analysis were constructed and other significant related data were gathered. A website with a user-friendly graphical visualization has been developed for users to access the wealth of data and download prebuilt datasets. Our database is available at http://drugcombdb.denglab.org/.
Motivation Drug combination therapy has become an increasingly promising method in the treatment of cancer. However, the number of possible drug combinations is so huge that it is hard to screen synergistic drug combinations through wet-lab experiments. Therefore, computational screening has become an important way to prioritize drug combinations. Graph neural network has recently shown remarkable performance in the prediction of compound–protein interactions, but it has not been applied to the screening of drug combinations. Results In this paper, we proposed a deep learning model based on graph neural network and attention mechanism to identify drug combinations that can effectively inhibit the viability of specific cancer cells. The feature embeddings of drug molecule structure and gene expression profiles were taken as input to multilayer feedforward neural network to identify the synergistic drug combinations. We compared DeepDDS (Deep Learning for Drug–Drug Synergy prediction) with classical machine learning methods and other deep learning-based methods on benchmark data set, and the leave-one-out experimental results showed that DeepDDS achieved better performance than competitive methods. Also, on an independent test set released by well-known pharmaceutical enterprise AstraZeneca, DeepDDS was superior to competitive methods by more than 16% predictive precision. Furthermore, we explored the interpretability of the graph attention network and found the correlation matrix of atomic features revealed important chemical substructures of drugs. We believed that DeepDDS is an effective tool that prioritized synergistic drug combinations for further wet-lab experiment validation. Availability and implementation Source code and data are available at https://github.com/Sinwang404/DeepDDS/tree/master
A new nanostructure of magnetic-fluorescent bifunctional Janus nanobelts with Fe3O4/PMMA as one half and Tb(BA)3phen/PMMA as the other half has been successfully fabricated by a specially designed parallel spinneret electrospinning technology. The morphology and properties of the final products were investigated in detail by X-ray diffractometry (XRD), scanning electron microscopy (SEM), energy dispersive spectrometry (EDS), biological microscopy (BM), vibrating sample magnetometry (VSM) and fluorescence spectroscopy. The results revealed that the [Fe3O4/PMMA]//[Tb(BA)3phen/PMMA] magnetic-fluorescent bifunctional Janus nanobelts possess superior magnetic and fluorescent properties due to their special nanostructure. Compared with Fe3O4/Tb(BA)3phen/PMMA composite nanobelts, the magnetic-fluorescent bifunctional Janus nanobelts provided better performance. The new magnetic-fluorescent bifunctional Janus nanobelts have potential applications in novel nano-bio-label materials, drug target delivery materials and future nanodevices due to their excellent magnetic-fluorescent properties, flexibility and insolubility. Moreover, the construction technique for the Janus nanobelts is of universal significance for the fabrication of other multifunctional Janus nanobelts.
A new type of flexible Janus nanoribbons array with anisotropic electrical conductivity, magnetism, and photoluminescence has been successfully fabricated by electrospinning technology using a specially designed parallel spinneret. Every single Janus nanoribbon in the array consists of a half side of Fe3O4 nanoparticles/polyaniline/polymethylmethacrylate (PMMA) conductive‐magnetic bifunctionality and the other half side of Tb(BA)3phen/PMMA insulative‐photoluminescent characteristics, and all the Janus nanoribbons are aligned to form array. Owing to the unique nanostructure, the conductance along with the length direction of nanoribbons reaches up to eight orders of magnitude higher than that along with perpendicular direction, which is by far the most excellent conductive anisotropy for anisotropic conductive materials. The Janus nanoribbons array is also simultaneously endowed with magnetic and photoluminescent characteristics. The obtained Janus nanoribbons array will have important applications in the future subminiature electronic equipments owing to its high electrical anisotropy and multifunctionality. Furthermore, the design concept and fabrication technique for the flexible Janus nanoribbons array provide a new and facile approach for the preparation of anisotropic conductive films with multifunctionality.
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