Motivation While drug combination therapies are a well-established concept in cancer treatment, identifying novel synergistic combinations is challenging due to the size of combinatorial space. However, computational approaches have emerged as a time- and cost-efficient way to prioritize combinations to test, based on recently available large-scale combination screening data. Recently, Deep Learning has had an impact in many research areas by achieving new state-of-the-art model performance. However, Deep Learning has not yet been applied to drug synergy prediction, which is the approach we present here, termed DeepSynergy. DeepSynergy uses chemical and genomic information as input information, a normalization strategy to account for input data heterogeneity, and conical layers to model drug synergies. Results DeepSynergy was compared to other machine learning methods such as Gradient Boosting Machines, Random Forests, Support Vector Machines and Elastic Nets on the largest publicly available synergy dataset with respect to mean squared error. DeepSynergy significantly outperformed the other methods with an improvement of 7.2% over the second best method at the prediction of novel drug combinations within the space of explored drugs and cell lines. At this task, the mean Pearson correlation coefficient between the measured and the predicted values of DeepSynergy was 0.73. Applying DeepSynergy for classification of these novel drug combinations resulted in a high predictive performance of an AUC of 0.90. Furthermore, we found that all compared methods exhibit low predictive performance when extrapolating to unexplored drugs or cell lines, which we suggest is due to limitations in the size and diversity of the dataset. We envision that DeepSynergy could be a valuable tool for selecting novel synergistic drug combinations. Availability and implementation DeepSynergy is available via www.bioinf.jku.at/software/DeepSynergy. Contact klambauer@bioinf.jku.at. Supplementary information Supplementary data are available at Bioinformatics online.

DeepSynergy: predicting anti-cancer drug synergy with Deep Learning.

A media device allows users to watch and capture portions from a media stream. Users may then share the captured media content with other users. In one embodiment, the media device receives a media stream, plays the media stream, and caches a portion of the media stream as it is being played. A user can define a media clip by selecting its boundaries in the cached portion of the media stream. The media device creates the media clip based on the user's input and enables the user to transmit the media clip to another system, such as a community website for sharing it with other users.

Capturing and sharing media content

Sustainable utilization of agri-food wastes and by-products for producing value-added products (for cosmetic, pharmaceutical or food industrial applications) provides an opportunity for earning additional income for the dependent industrial sector. Besides, effective valorisation of wastes/by-products can efficiently help in reducing environmental stress by decreasing unwarranted pollution. The major focus of this review is to provide comprehensive information on valorisation of agri-food wastes and by-products with focus laid on bioactive compounds and bioactivity. The review covers the bioactives identified from wastes and by-products of plants (fruits, exotic fruits, vegetables and seeds), animals (dairy and meat) and marine (fish, shellfish seaweeds) resources. Further, insights on the present status and future challenges of sustainably utilizing agri-food wastes/by-products for value addition will be highlighted.

https://www.mdpi.com/1420-3049/25/3/510/pdf

Bioactives From Agri-Food Wastes: Present Insights and Future Challenges.

Media content is paired with context-relevant supplemental content, and the media and supplemental content are provided to a user. A media stream containing the media content may be received from a source system, and context information about the media content is determined from information about the media stream. The supplemental content may be selected based on the determined context information. This may enable a business model in which third parties can register advertising or other supplemental content and specify the criteria that cause it to be combined with the media content.

Selection and presentation of context-relevant supplemental content and advertising

In the present work, we report compilation and analysis of 245 drugs, including small and macromolecules approved by the U.S. FDA from 2015 until June 2020. Nearly 29% of the drugs were approved for the treatment of various types of cancers. Other major therapeutic areas of focus were infectious diseases (14%); neurological conditions (12%); and genetic, metabolic, and cardiovascular disorders (7-8% each). Itemization of the approved drugs according to the year of approval, sponsor, target, chemical class, major drug-metabolizing enzyme(s), route of administration/elimination, and drug-drug interaction liability (perpetrator or/and victim) is presented and discussed. An effort has been made to analyze the pharmacophores to identify the structural (e.g., aromatic, heterocycle, and aliphatic), elemental (e.g., boron, sulfur, fluorine, phosphorus, and deuterium), and functional group (e.g., nitro drugs) diversity among the approved drugs. Further, descriptor-based chemical space analysis of FDA approved drugs and several strategies utilized for optimizing metabolism leading to their discoveries have been emphasized. Finally, an analysis of drug-likeness for the approved drugs is presented.

U.S. FDA Approved Drugs from 2015-June 2020: A Perspective

Citrus medica (Citron) is an underutilized fruit plant having various bioactive components in all parts of the plant. The major bioactive compounds present are iso-limonene, citral, limonene, phenolics, flavonones, vitamin C, pectin, linalool, decanal, and nonanal, accounting for several health benefits. Pectin and heteropolysachharides also play a major role as dietary fibers. The potential impact of citron and its bioactive components to prevent or reverse destructive deregulated processes responsible for certain diseases has attracted different researchers’ attention. The fruit has numerous nutraceutical benefits, proven by pharmacological studies; for example, anti-catarrhal, capillary protector, anti-hypertensive, diuretic, antibacterial, antifungal, anthelmintic, antimicrobial, analgesic, strong antioxidant, anticancerous, antidiabetic, estrogenic, antiulcer, cardioprotective, and antihyperglycemic. The present review explores new insights into the benefits of citron in various body parts. Throughout the world, citron has been used in making carbonated drinks, alcoholic beverages, syrup, candied peels, jams, marmalade, cordials, and many other value added products, which suggests it is an appropriate raw material to develop healthy processed food. In the present review, the fruit taxonomical classification, beneficial phytochemicals, antioxidant activities, and health benefits are discussed.

Citrus medica: nutritional, phytochemical composition and health benefits - a review.

Docking-based 3D-QSAR study of HIV-1 integrase inhibitors.

Toxicity is a common drawback of newly designed chemotherapeutic agents. With the exception of pharmacophore-induced toxicity (lack of selectivity at higher concentrations of a drug), the toxicity due to chemotherapeutic agents is based on the toxicophore moiety present in the drug. To date, methodologies implemented to determine toxicophores may be broadly classified into biological, bioanalytical and computational approaches. The biological approach involves analysis of bioactivated metabolites, whereas the computational approach involves a QSAR-based method, mapping techniques, an inverse docking technique and a few toxicophore identification/estimation tools. Being one of the major steps in drug discovery process, toxicophore identification has proven to be an essential screening step in drug design and development. The paper is first of its kind, attempting to cover and compare different methodologies employed in predicting and determining toxicophores with an emphasis on their scope and limitations. Such information may prove vital in the appropriate selection of methodology and can be used as screening technology by researchers to discover the toxicophoric potentials of their designed and synthesized moieties. Additionally, it can be utilized in the manipulation of molecules containing toxicophores in such a manner that their toxicities might be eliminated or removed.

Toxicophore exploration as a screening technology for drug design and discovery: techniques, scope and limitations

The objective of this study is to identify novel HIV-1 integrase (IN) inhibitors Here, shape-based screening and QSAR have been successfully implemented to identify the novel inhibitors for HIV-1 IN, and in silico validation is performed by docking studies The 2D QSAR model of benzodithiazine derivatives was built using genetic function approximation (GFA) method with good internal (cross-validated r2 = 0852) and external prediction () Best docking pose of highly active molecule of the benzodithiazine derivatives was used as a template for shape-based screening of ZINC database Toxicity prediction was also performed using Deductive Estimation of Risk from Existing Knowledge (DEREK) program to filter non-toxic molecules Inhibitory activities of screened non-toxic molecules were predicted using derived QSAR models Active, non-toxic screened molecules were also docked into the active site of HIV-1 IN using AutoDock and dock program Some molecules docked similarly as highly active molecule of the benzodithiazine derivatives These molecules also followed the same docking interactions in both the programs Finally, four benzodithiazine derivatives were identified as novel HIV-1 integrase inhibitors based on QSAR predictions and docking interactions ADME properties of these molecules were also computed using Discovery Studio

https://onlinelibrary.wiley.com/doi/pdf/10.1111/j.1747-0285.2012.01326.x

Identification of novel HIV-1 integrase inhibitors using shape-based screening, QSAR, and docking approach.

The docking studies and comparative molecular field analysis (CoMFA) were performed on highly active molecules of curcumine derivatives against 3′ processing activity of HIV-1 integrase (IN) enzyme. The optimum CoMFA model was selected with statistically significant cross-validated r2 value of 0.815 and non-cross validated r
 2 value of 0.99. The common pharmacophore of highly active molecules was used for screening of HIV-1 IN inhibitors. The high contribution of polar interactions in pharmacophore mapping is well supported by docking and CoMFA results. The results of docking, CoMFA, and pharmacophore mapping give structural insights as well as important binding features of curcumine derivatives as HIV-1 IN inhibitors which can provide guidance for the rational design of novel HIV-1 IN inhibitors.

Pawan Gupta

Papers

Citrus medica: nutritional, phytochemical composition and health benefits - a review.

Docking-based 3D-QSAR study of HIV-1 integrase inhibitors.

Toxicophore exploration as a screening technology for drug design and discovery: techniques, scope and limitations

Identification of novel HIV-1 integrase inhibitors using shape-based screening, QSAR, and docking approach.

Comparative docking and CoMFA analysis of curcumine derivatives as HIV-1 integrase inhibitors