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Ligand binding site prediction from protein structure plays an important role in various complex rational drug design efforts. Its applications include drug side effects prediction, docking prioritization in inverse virtual screening and elucidation of protein function in genome wide structural studies. Currently available tools have limitations that disqualify them from many possible use cases. In...
Various problems in biomedicine can be formulated as a ranking problem, where a set of candidate components is ranked relatively based on a set of known components. The most popular problem in biomedicine is identification of disease-associated cellular components, where cellular components can be genes, proteins, microRNAs or other molecules. Besides that, a number of problems in pharmacology is...
Protein Docking is an important task in computational chemistry and computational biology and yet is very computationally expensive. This paper presents a study on implementing molecular docking program in the Hadoop-based system. The molecular docking platform based on Hadoop provides the preprocessing of ligand datasets and the analysis function of the docking results, it also implements the parallel...
Pharmaceutical industries are interested in Cysteine-stabilized peptides because they offer an array bioactive properties while being highly stable under a range of physiological conditions. However, it is widely appreciated that only a small fraction of this type of peptides have been experimentally discovered while a large number remain unidentified. However, identification of these cysteine-stabilized...
In a drug development process, appropriate drug-binding selectivity is critical for a success drug. However the selectivity in a data source, showing the intensity of efforts, may be limited to prior knowledge of the expertise or be biased towards the hypothesis testing. With the increasing of drug screening data, it is challenging to coordinate the efforts and execute data governance at a large scale...
Network pharmacology which is based on bioinformatics and system biology knowledge has been employed into the field of Traditional Chinese Medicine (TCM) researches in recent years. In order to summarize current available bioinformatics databases, especially those can be used for TCM formulae study; a systematic search of Chinese literature by May 2017 was conducted. Results showed that research methods...
We propose a novel, semantic-reasoning-based approach to look for potentially adverse drug-drug interactions (DDIs) by using a knowledge-base of biomedical public ontologies and datasets in a semantic graph representation. This approach makes it possible to find previously unknown relations between different biological entities like drugs, proteins and biological processes, and perform inferences...
Recent studies show that drug-disease associations provide important information for drug discovery and drug repositioning. Wet experimental identification of drug-disease associations is time-consuming and labor-intensive. Therefore, the development of computational methods that predict drug-disease associations is an urgent task. In this paper, we propose a novel computational method named NTSIM,...
In the study, it was aimed to investigate the bactericidal activity of antimicrobial photodynamic therapy (aPDT) performed on in-vitro conditions with novel cationic-porphyrin derivatives (CPDs) on multidrug resistant E. coli. In aPDT experiments performed with different concentrations of photosensitizer and different energy densities, 99.9999% or more decrease in bacterial survival was detected....
Warfarin is a popular pharmaceutical anticoagulant that targets the vitamin K epoxide reductase complex sub-unit 1, encoded by the gene VKORC1, but warfarin can be dangerous since it can cause bleeding. A first step in finding better anticoagulants that target the same enzyme is structure based lead molecule optimization using computational tools. This is possible because the tertiary structure of...
A Bayesian optimization technique enables a short search time for a complex prediction model that includes many hyperparameters while maintaining the accuracy of the prediction model. Here, we apply a Bayesian optimization technique to the drug-target interaction (DTI) prediction problem as a method for computational drug discovery. We target neighborhood regularized logistic matrix factorization...
The current drug development pipelines are characterised by long processes with high attrition rates and elevated costs. More than 80% of new compounds fail in the later stages of testing due to severe side-effects caused by unknown biomolecular targets of the compounds. In this work, we present a measure that can predict shared targets for drugs in DrugBank through large scale analysis of the biomedical...
This article is devoted to the description of the prospects of using nanocomposite materials based on hydroxyapatite (HA) in medicine. The basic directions of manufacturing and application of HA-based nanocomposites in stomatology and osteoplastic are described.
High-order Drug-Drug Interactions (DDI) are common particularly for elderly people. It is highly non-trivial to detect such interactions via in vivo/in vitro experiments. In this paper, we present SVM-based classification methods to predict whether a high-order directional drug-drug interaction (HoDDDI) instance is associated with adverse drug reactions (ADRs) and induced side effects. Specifically,...
The problem of the discovery and marketing of new drugs can be vastly accelerated through High Performance Computing (HPC), molecular modeling techniques, and more specifically by means of the techniques commonly named as computational drug discovery (CDD) and in silico high throughput screening. These techniques usually assume a unique interaction site (active site) between potential drugs and a...
Drug polypharmacology or “drug promiscuity” refers to the ability of a drug to bind multiple proteins. Such studies have huge impact to the pharmaceutical industry, but in the same time require large investments on wet-lab experiments. The respective in-silico experiments have a significantly smaller cost and minimize the expenses for the subsequent lab experiments. However, the process of finding...
Geometric semantic genetic programming is a hot topic in evolutionary computation and recently it has been used with success on several problems from Biology and Medicine. Given the young age of geometric semantic genetic programming, in the last few years theoretical research, aimed at improving the method, and applicative research proceeded rapidly and in parallel. As a result, the current state...
Complex networks exist widely in nature and human society, from the Internet to chemical reactions, biological food chain, and then to human society, interpersonal relationships, cooperation between people, science and technology citation and so show a complex network topology characteristics. Network pharmacology is based on the theory of system biology, network analysis of biological systems, select...
Cancer is still one of the challenging diseases to develop new therapies due to the late diagnosis and its complex progression nature. There is an urgent need for new therapy regimes for cancer patients having late stage diagnosis or recurrence. New computational approaches can help to identify more effective drug combinations as new treatment options for cancer. For this purpose, we developed a classification...
Diagnostics and therapeutic interventions in complex systemic diseases like cancer can be mapped to fault identification and control problem. A complete linear framework has been developed in this manuscript to comprehend different classes of faults and controllability of the output for a class of homeostatic inputs in the Boolean network framework. The problems undertaken in this manuscript are non-trivial...
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