In pharmaceutical sciences, a crucial step of the drug discovery process is the identification of drug-target interactions. However, only a small portion of the drug-target interactions have been experimentally validated, as the experimental validation is laborious and costly. To improve the drug discovery efficiency, there is a great need for the development of accurate computational approaches that can predict potential drug-target interactions to direct the experimental verification. In this paper, we propose a novel drug-target interaction prediction algorithm, namely neighborhood regularized logistic matrix factorization (NRLMF). Specifically, the proposed NRLMF method focuses on modeling the probability that a drug would interact with a target by logistic matrix factorization, where the properties of drugs and targets are represented by drug-specific and target-specific latent vectors, respectively. Moreover, NRLMF assigns higher importance levels to positive observations (i.e., the observed
TY - JOUR. T1 - DeepPurpose. T2 - A Deep Learning Library for Drug-Target Interaction Prediction. AU - Huang, Kexin. AU - Fu, Tianfan. AU - Glass, Lucas M. AU - Zitnik, Marinka. AU - Xiao, Cao. AU - Sun, Jimeng. N1 - © The Author(s) 2020. Published by Oxford University Press.. PY - 2021/4/1. Y1 - 2021/4/1. N2 - SUMMARY: Accurate prediction of drug-target interactions (DTI) is crucial for drug discovery. Recently, deep learning (DL) models for show promising performance for DTI prediction. However, these models can be difficult to use for both computer scientists entering the biomedical field and bioinformaticians with limited DL experience. We present DeepPurpose, a comprehensive and easy-to-use DL library for DTI prediction. DeepPurpose supports training of customized DTI prediction models by implementing 15 compound and protein encoders and over 50 neural architectures, along with providing many other useful features. We demonstrate state-of-the-art performance of DeepPurpose on several ...
Now I need to say something about performance. The above code is naive and performs many unnecessary summations. For example, hashing a long chain should only take time linear in its length. But using the above code indiscriminately could give you exponential time. A good implementation might take a divide and conquer approach: slice the molecule in half through a bunch of bonds, compute partial hashes for each half and then sew the halves together in time exponential in the number of bonds you sliced through. For the types of molecules Ive seen in real pharmaceutical databases (say) this is actually pretty cheap if youre smart about the slicing. The hashes in the above code could probably be computed many thousands of times faster. As it is, youll probably need to compile the above with optimisation ...
Where the industry failed to provide standardized emr - med-o-card succeeds.Modern technology permit storage of any type of medical format on a credit card sized 16/32gb memory where coding and analysis is done on the card using cardinstalled medical and pharmaceutical databases and analysis ...
Methyl, Ethyl, Propyl, Butyl: Futile But Not for Water, as the Correlation of Structure and Thermodynamic Signature Shows in a Congeneric Series of Thermolysin Inhibitors ...
D07A - Corticosteroids, plain. The Anatomical Therapeutic Chemical (ATC) classification system from Drugs-about.com includes all drugs classified in groups at five different levels.
The purpose of the World Health Organization (WHO) Anatomical Therapeutic Chemical (ATC) classification system is to be used as a tool for drug utilization research in order to improve quality of drug use. Drugs are divided into different groups according to the organ or system on which they act and their chemical, pharmacological and therapeutical properties.. Return to footnote 4 referrer. ...
The purpose of the World Health Organization (WHO) Anatomical Therapeutic Chemical (ATC) classification system is to be used as a tool for drug utilization research in order to improve quality of drug use. Drugs are divided into different groups according to the organ or system on which they act and their chemical, pharmacological and therapeutical properties.. Return to footnote 4 referrer. ...
D10af52. The Anatomical Therapeutic Chemical (ATC) classification system from sdrugs.com includes all drugs classified in groups at five different levels.
J04ab02. The Anatomical Therapeutic Chemical (ATC) classification system from sdrugs.com includes all drugs classified in groups at five different levels.
A10 - Drugs used in diabetes. The Anatomical Therapeutic Chemical (ATC) classification system from Drugs-about.com includes all drugs classified in groups at five different levels.
4969-02-2|Metixene|DrugBank|Trest|Atosil|metixene|Contalyl|Tremaril|Tremonil|Methixart|Methixene|Metisene [DCIT]|Methixen [German]|Metix...
In one of the most surprising twists since The Matrixs philosophical knee-jerk reaction, the director of said film, Larry Wachowski, has publically
This information should not be interpreted without the help of a healthcare provider. If you believe you are experiencing an interaction, contact a healthcare provider immediately. The absence of an interaction does not necessarily mean no interactions exist ...
A hormone and neurotransmitter used to treat allergic reactions, to restore cardiac rhythm, and to control mucosal congestion, glaucoma, and asthma ...
InChI=1S/C32H37NO7/c1-4-25(21-11-7-5-8-12-21)26(22-13-9-6-10-14-22)23-15-17-24(18-16-23)39-20-19-33(2,3)31-29(36)27(34)28(35)30(40-31)32(37)38/h5-18,27-31,34-36H,4,19-20H2,1-3H3/p+1/b26-25-/t27-,28-,29+,30-,31+/m0/ ...
ListMoto.com - (Anatomical_Therapeutic_Chemical_Classification_System) The ANATOMICAL THERAPEUTIC CHEMICAL (ATC) CLASSIFICATION SYSTEM is used for the classification of active ingredients of drugs according to the organ or system on which they act and their therapeutic , pharmacological and chemical properties. It is controlled by the World Health Organization Collaborating Centre for DRUG Drug Statistics Methodology (WHOCC), and was first published in 1976
The Anatomical Therapeutic Chemical (ATC) Classification System is used for the classification of drugs. It is controlled by the WHO Collaborating Centre for Drug Statistics Methodology (WHOCC), and was first published in 1976.[1] The classification system divides drugs into different groups according to the organ or system on which they act and/or their therapeutic and chemical characteristics. Each bottom-level ATC code stands for a pharmaceutically used substance in a single indication (or use). This means that one drug can have more than one code: acetylsalicylic acid (aspirin), for example, has A01AD05 as a drug for local oral treatment, B01AC06 as a platelet inhibitor, and N02BA01 as an analgesic and antipyretic. On the other hand, several different brands share the same code if they have the same active substance and indications. ...
The Anatomical Therapeutic Chemical (ATC) Classification System is used for the classification of drugs. It is controlled by the WHO Collaborating Centre for Drug Statistics Methodology (WHOCC), and was first published in 1976.[1] The classification system divides drugs into different groups according to the organ or system on which they act and/or their therapeutic and chemical characteristics. Each bottom-level ATC code stands for a pharmaceutically used substance in a single indication (or use). This means that one drug can have more than one code: acetylsalicylic acid (aspirin), for example, has A01AD05 as a drug for local oral treatment, B01AC06 as a platelet inhibitor, and N02BA01 as an analgesic and antipyretic. On the other hand, several different brands share the same code if they have the same active substance and indications. ...
The ATC system is based on the earlier Anatomical Classification System, which is intended as a tool for the pharmaceutical industry to classify pharmaceutical products (as opposed to their active ingredients).[2] This system, confusingly also called ATC, was initiated in 1971 by the European Pharmaceutical Market Research Association (EphMRA) and is being maintained by the EphMRA and the Pharmaceutical Business Intelligence and Research Group (PBIRG). Its codes are organised into four levels.[3] The WHOs system, having five levels, is an extension and modification of the EphMRAs. It was first published in 1976.[1] ...
|p|The SAGE Encyclopedia of Pharmacology and Society explores the social and policy sides of the pharmaceutical industry and its pervasive influence in society.
The National Center for Biomedical Ontology was founded as one of the National Centers for Biomedical Computing, supported by the NHGRI, the NHLBI, and the NIH Common Fund under grant U54-HG004028 ...
Among them a single CTL and two Th epitopes had been totally overlapping with other epitopes with the very same style devoid of amino acid differences and, hence, had been excluded in the association rule mining to prevent redundancy, Epitopes of different types that entirely overlap with one another without amino acid differences had been also integrated to keep in mind multi functional areas, The final set of epitopes con sisted of 44 epitopes representing 4 genes, namely, Gag, Pol, Env and Nef, and incorporated 32 CTL, 10 Th and 2 Ab epitopes, Identification of linked epitopes To determine regularly co taking place epitopes of various kinds, we utilised association rule mining, a data mining technique that identifies and describes relationships amid objects inside a information set, Whilst associa tion rule mining is most typically utilized in advertising ana lyses, this kind of as marketplace basket evaluation, this approach has become effectively utilized to many biolo gical complications, ...
Why Is Frequent Pattern or Association Mining an Essential Task in Data Mining? ... fm, cm, am, fcm, fam, cam, fcam. f:4. c:1. b:1. p:1. b:1. c:3. a:3. b:1. m:2 ... – A free PowerPoint PPT presentation (displayed as a Flash slide show) on PowerShow.com - id: 127fd4-MDU3O
Data/links for these compounds: TCMDC-123563, CHEMBL546966, CHEMBL page: 637010 Cc1ccc(cc1)n2c(cc(c2C)C(=O)CN3C(=O)C(NC3=O)Cc4ccccc4)C TCMDC-125698, CHEMBL587989, CHEMBL: 627784 Cc1cc(c(n1c2ccc(cc2)Cl)C)C=C3C(=O)N(C(=Nc4ccccc4)S3)C5CCCC5 TCMDC-125697, CHEMBL581336, CHEMBL: 640978 CCOC(=O)c1ccc(cc1)n2c(cc(c2C)C=C3C(=O)N(C(=Nc4ccccc4)S3)C5CCCC5)C TCMDC-125659, CHEMBL528140, CHEMBL: 626220 Cc1ccnc(c1)n2c(cc(c2C)C=C3C(=O)N(C(=Nc4ccccc4)S3)Cc5ccco5)C TCMDC-124103, CHEMBL588465, CHEMBL: 643107 Cc1cc(cc(c1)n2c(cc(c2C)C=C3C(=O)NC(=Nc4ccc(cc4)Cl)S3)C)C TCMDC-124456, CHEMBL548395, CHEMBL: 640006 CCn1c(cc(c1C)C=C2C(=O)NC(=Nc3ccccc3)S2)C ...
aux_for_url: 0 base_id_url: https://www.ebi.ac.uk/chembldb/compound/inspect/ base_id_url_available: 1 description: A database of bioactive drug-like small molecules and bioactivities abstracted from the scientific literature. name: chembl name_label: ChEMBL name_long: ChEMBL src_compound_id: - CHEMBL1483 src_id: 1 src_url: https://www.ebi.ac.uk/chembl/ - aux_for_url: 0 base_id_url: http://www.drugbank.ca/drugs/ base_id_url_available: 1 description: A database that combines drug (i.e. chemical, pharmacological and pharmaceutical) data with drug target (i.e. sequence, structure, and pathway) information. name: drugbank name_label: DrugBank name_long: DrugBank src_compound_id: - DB00518 src_id: 2 src_url: http://drugbank.ca/ - aux_for_url: 0 base_id_url: http://pubchem.ncbi.nlm.nih.gov/substance/ base_id_url_available: 1 description: A subset of the PubChem DB: from the original depositor drugs of the future (Prous). name: pubchem_dotf name_label: PubChem: Drugs of the Future name_long: ...
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ATC code N Nervous system is a section of the Anatomical Therapeutic Chemical Classification System, a system of alphanumeric codes developed by the WHO for the classification of drugs and other medical products.[1]. Codes for veterinary use (ATCvet codes) can be created by placing the letter Q in front of the human ATC code: QN...[2] ATCvet codes without corresponding human ATC codes are cited with the leading Q in the following list.. ...
ATC code G02 A section of the Anatomical Therapeutic Chemical Classification System. G Genito-urinary system and sex hormones Additional recommended knowledge
ATC code P01 A section of the Anatomical Therapeutic Chemical Classification System. P Antiparasitic products, insecticides and repellents Additional
ATC code N05 A section of the Anatomical Therapeutic Chemical Classification System. N Nervous system Additional recommended knowledge Daily Visual Balance
Ang Sistema ng klasipikasyon sa Anatomika, Terapeutika at Kemikal o Anatomical Therapeutic Chemical (ATC) ay ginagamit para sa klasipikasyon ng mga aktibong sangkap ng mga droga batay sa pagtalab ng droga sa organo o sistema at kung ano ang mga taglay na kemikal, terapeutika, at parmakolohikal. Kasalukuyang hinahawakan ito ng World Health Organization Collaborating Centre for Drug Statistics Methodology (WHOCC), at unang nailathala noong 1976.[1] Hinahati ng sistemang pangkodigo sa medikasyon ang mga droga sa ibat-ibang pangkat batay sa pagtalab nito sa mga organo o sistema at/o batay sa pagkakakilanlang terapeutiko at kemikal. Nagrerepresenta ang bawat kodigong ATC para sa mga bagay na ginagamit sa medikasyon, o kombinasyon ng mga sabtans, o sa isang pangisahang indikasyon (o ng paggamit). Ibig sabihin nito na ang isang droga ay maaaring magkaroon ng mahigit sa isang kodigo. Halimbawa na lamang nito ay ang acetylsalicylic acid (aspirin), na mayroong A01AD05 bilang droga para sa lokal na ...
DrugBank, Alberta, Canada - DrugBank Version 4.3 The DrugBank database is a unique bioinformatics and cheminformatics resource that combines detailed drug (i.e. chemical, pharmacological and pharmaceutical) data with comprehensive drug target (i.e. seque.
Previous work and publications Before being involved in the SITCON project, my work was dedicated to SAGE data mining in the BM2A team leaded by Olivier Gandrillon at the CGMC and in the Turing team leaded by Jean François Boulicaut at the LIRIS. My Phd Thesis is available here. International Biology/Bioinformatics Journals [1] J. Leyritz, S. Schicklin, S. Blachon , C. Keime , R.G. Pensa, C. Robardet , J. Besson , J-F. Boulicaut and , O. Gandrillon , SQUAT, a web tool to mine SAGE data. BMC Bioinformatics , 2008, 9:378. [2] J. Klema, S. Blachon , A. Soulet, B. Cremilleux and O. Gandrillon, Constraint-Based Knowledge Discovery from SAGE Data. In Silico Biology. 8, 0014, (2008). [3] S. Blachon, R.G. Pensa, J. Besson, C. Robardet, J-F. Boulicaut and O. Gandrillon, Clustering formal concepts to discover biological ly relevant knowledge from gene expression data, In Silico Biology, (2007). [4] C. Becquet , S. Blachon, B. Jeudy, J-F. Boulicaut and O. Gandrillon, Strong association rule mining for ...
[ChEMBL Compound Description] ID:CHEMBL1371493, InChI_Key:VWTINHYPRWEBQY-UHFFFAOYSA-N, Tradenames:BITREX, Synonyms:LIDOCAINE BENZYL BENZOATE | DENATONIUM BENZOATE | BITREX
2G0G: Structure-Based Drug Design of a Novel Family of PPARgamma Partial Agonists: Virtual Screening, X-ray Crystallography, and in Vitro/in Vivo Biological Activities
The following is written in Japanese.... ケンブルチーム(ChEMBL Team)は 欧州バイオインフォマティクス研究所( EMBL-EBI )にあり 創薬研究に有用な化合物やターゲット情報を提供するデータベースを開発していま ...
So, we have an initial interface for the ChEMBL SAR data, and we are looking for some interested people to do some testing, find issues with different OS, browsers, java versions, etc. The kinase SARfari testing we did was very helpful, and many, many thanks to those of you that helped, but rest assured, we will mail you for feedback, bugs, etc. If you wish to take part, please mail us ...
Muplestim is under investigation in clinical trial NCT00002258 (A Phase I, Open Label Trial to Evaluate the Safety, Tolerance and Biological Effects of SDZ ILE-964 (Recombinant Human Interleukin-3, RhIL-3) in ...
Cambridge Society for the Application of Research (CSAR) encourages an appreciation of the application of research through lectures, visits and student awards.
BioAssay record AID 500165 submitted by ChEMBL: Inhibition of PKD1 in rat ventricular myosyte assessed as inhibition of HDAC5 neuclear export.
BioAssay record AID 378105 submitted by ChEMBL: Induction of apoptosis in human p53 mutant A2780S at 1.5 uM after 24 hrs by TdT assay.
Coffee is among the most popular beverages in many cities all over the world, being both at the core of the busiest shops and a long-standing tradition of recreational and social value for many people. Among the many coffee variants, espresso attracts the interest of different stakeholders: from citizens consuming espresso around the city, to local business activities, coffee-machine vendors and international coffee industries. The quality of espresso is one of the most discussed and investigated issues. So far, it has been addressed by means of human experts, electronic noses, and chemical approaches. The current work, instead, proposes a data-driven approach exploiting association rule mining. We analyze a real-world dataset of espresso brewing by professional coffee-making machines, and extract all correlations among external quality-influencing variables and actual metrics determining the quality of the espresso. Thanks to the application of association rule mining, a powerful data-driven exhaustive
Title:Predicting Targeted Polypharmacology for Drug Repositioning and Multi- Target Drug Discovery. VOLUME: 20 ISSUE: 13. Author(s):X. Liu, F. Zhu, X. H. Ma, Z. Shi, S. Y. Yang, Y. Q. Wei and Y. Z. Chen. Affiliation:State Key Laboratory of Biotherapy, Sichuan University, Chengdu 610064, P. R. China.. Keywords:Computer aided drug design, drug discovery, drug repositioning, gene expression, multi-target, network pharmacology, systems pharmacology, virtual screening. Abstract:Prediction of polypharmacology of known drugs and new molecules against selected multiple targets is highly useful for finding new therapeutic applications of existing drugs (drug repositioning) and for discovering multi-target drugs with improved therapeutic efficacies by collective regulations of primary therapeutic targets, compensatory signalling and drug resistance mechanisms. In this review, we describe recent progresses in exploration of in-silico methods for predicting polypharmacology of known drugs and new molecules ...
opt= 3DMet= ATCCode= ATCCode_prefix= ATCCode_suffix= ATC_Supplemental= ATCvet= Abbreviations= Beilstein= CASNo1= CASNo1_Comment= CASNo1_Ref= CASNo2= CASNo2_Comment= CASNo2_Ref= CASNo3= CASNo3_Comment= CASNo3_Ref= CASNo4= CASNo4_Comment= CASNo4_Ref= CASNo5= CASNo5_Comment= CASNo5_Ref= CASNo= CASNoOther= CASNo_Comment= CASNo_Ref= ChEBI1= ChEBI1_Comment= ChEBI1_Ref= ChEBI2= ChEBI2_Comment= ChEBI2_Ref= ChEBI3= ChEBI3_Comment= ChEBI3_Ref= ChEBI4= ChEBI4_Comment= ChEBI4_Ref= ChEBI5= ChEBI5_Comment= ChEBI5_Ref= ChEBI= ChEBIOther= ChEBI_Comment= ChEBI_Ref= ChEMBL1= ChEMBL1_Comment= ChEMBL1_Ref= ChEMBL2= ChEMBL2_Comment= ChEMBL2_Ref= ChEMBL3= ChEMBL3_Comment= ChEMBL3_Ref= ChEMBL4= ChEMBL4_Comment= ChEMBL4_Ref= ChEMBL5= ChEMBL5_Comment= ChEMBL5_Ref= ChEMBL= ChEMBLOther= ChEMBL_Comment= ChEMBL_Ref= ChemSpiderID1= ChemSpiderID1_Comment= ChemSpiderID1_Ref= ChemSpiderID2= ChemSpiderID2_Comment= ChemSpiderID2_Ref= ChemSpiderID3= ChemSpiderID3_Comment= ChemSpiderID3_Ref= ChemSpiderID4= ChemSpiderID4_Comment= ...
Realistic Data for Testing Rule Mining Algorithms: 10.4018/978-1-60566-010-3.ch252: The association rule mining (ARM) problem is a wellestablished topic in the field of knowledge discovery in databases. The problem addressed by ARM is to
The field of knowledge discovery in databases, or Data Mining, has received increasing attention during recent years as large organizations have begun to realize the potential value of the information that is stored implicitly in their databases. One specific data mining task is the mining of Association Rules, particularly from retail data. The task is to determine patterns (or rules) that characterize the shopping behavior of customers from a large database of previous consumer transactions. The rules can then be used to focus marketing efforts such as product placement and sales promotions. Because early algorithms required an unpredictably large number of IO operations, reducing IO cost has been the primary target of the algorithms presented in the literature. One of the most recent proposed algorithms, called PARTITION, uses a new TID-list data representation and a new partitioning technique. The partitioning technique reduces IO cost to a constant amount by processing one database ...
CS 6372 Biological Database Systems and Datamining (3 semester hours) This course emphasizes the concepts of database, data warehouse, data mining and their applications in biological science. Topics include relational data models, data warehouse, OLAP, data pre-processing, association rule mining from data, classification and prediction, clustering, graph mining, time-series data mining, and network analysis. Applications in biological science will be focused on Biological data warehouse design, association rule mining from biological data, classification and prediction from microarray data, clustering analysis of genomic and proteomic data, mining time-series gene expression data, biological network (including protein-protein interaction network, metabolic network) mining. Prerequisite: CS 6325 Introduction to Bioinformatics or BIOL 5376 Applied Bioinformatics (3-0) Y ...
Data mining, the extraction of hidden predictive large amounts of data and picking out the relevant information from large databases, is a powerful new technology with great potential to help...
Numerous medical management & clinical practice guidelines from the US federal government, medical schools, medical academies & associations. Examples: Occupational medicine, acute chemical exposures, heart disease & stroke, endocrine disorders, family practice, clinical preventive services, orthopedics, etc. Examples of medical databases: MEDLINE, HealthSTAR, AISLINE, HISTLINE, HSRPROJ, SDILINE, TOXLINE & CANCERLIT. Clinical trials listing services. Medical reference dictionaries. Large pharmaceutical databases.. ...
Association rule mining is concerned with the discovery of interesting association relationships hidden in databases. Traditional algorithms are only consi
Association rule mining, an important data mining technique, has been widely focused on the extraction of frequent patterns. Nevertheless, in some application domains it is interesting to discover...
Sequential pattern discovery is a well-studied field in data mining. Episodes are sequential patterns that describe events that often occur in the vicinity of each other. Episodes can impose restrictions on the order of the events, which makes them a versatile technique for describing complex patterns in the sequence. Most of the research on episodes deals with special cases such as serial and parallel episodes, while discovering general episodes is surprisingly understudied. This is particularly true when it comes to discovering association rules between them.. In this paper we propose an algorithm that mines association rules between two general episodes. On top of the traditional definitions of frequency and confidence, we introduce two novel confidence measures for the rules. The major challenge in mining these association rules is pattern explosion. To limit the output, we aim to eliminate all redundant rules. We define the class of closed association rules and show that this class contains ...
VIDAL Group is a leading European healthcare informatics and information systems company. With a team of over 200 pharmacists, pharmacologists, physicians, researchers, health informaticists, database architects, and application developers, VIDAL Group provides hospitals, primary care physicians, pharmacists, and patients with online access to up-to-date drug databases and related treatment-based information. The VIDAL Group team utilizes their collective expertise to provide relevant content from regulatory agencies, drug development organisations and peer-reviewed journals. In order to maximize opportunities for combining information produced by VIDAL Group with other healthcare-related information, VIDAL Group publishes its information using a number of standards relating to the clinical use of medications such as the International Classification of Diseases (ICD) and the Anatomical Therapeutic Chemical Classification (ATC). Supporting the information access needs of both patients and ...
ATC code R05 Cough and cold preparations is a therapeutic subgroup of the Anatomical Therapeutic Chemical Classification System, a system of alphanumeric codes developed by the WHO for the classification of drugs and other medical products. Subgroup R05 is part of the anatomical group R Respiratory system.[1]. Codes for veterinary use (ATCvet codes) can be created by placing the letter Q in front of the human ATC code: for example, QR05.[2] ATCvet codes without corresponding human ATC codes are cited with the leading Q in the following list.. National issues of the ATC classification may include additional codes not present in this list, which follows the WHO version. ...
ATC code B03 Antianemic preparations is a therapeutic subgroup of the Anatomical Therapeutic Chemical Classification System, a system of alphanumeric codes developed by the WHO for the classification of drugs and other medical products. Subgroup B03 is part of the anatomical group B Blood and blood forming organs. Codes for veterinary use (ATCvet codes) can be created by placing the letter Q in front of the human ATC code: for example, QB03. ATCvet codes without corresponding human ATC codes are cited with the leading Q in the following list. National issues of the ATC classification may include additional codes not present in this list, which follows the WHO version. B03AA01 Ferrous glycine sulfate B03AA02 Ferrous fumarate B03AA03 Ferrous gluconate B03AA04 Ferrous carbonate B03AA05 Ferrous chloride B03AA06 Ferrous succinate B03AA07 Ferrous sulfate B03AA08 Ferrous tartrate B03AA09 Ferrous aspartate B03AA10 Ferrous ascorbate B03AA11 Ferrous iodine B03AB01 Ferric sodium citrate B03AB02 Saccharated ...
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CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): One of the important problems in data mining is discovering association rules from databases of transactions where each transaction consists of a set of items. The most time consuming operation in this discovery process is the computation of the frequency of the occurrences of interesting subset of items (called candidates) in the database of transactions. To prune the exponentially large space of candidates, most existing algorithms, consider only those candidates that have a user defined minimum support. Even with the pruning, the task of finding all association rules requires a lot of computation power and time. Parallel computers offer a potential solution to the computation requirement of this task, provided efficient and scalable parallel algorithms can be designed. In this paper, we present two new parallel algorithms for mining association rules. The Intelligent Data Distribution algorithm efficiently uses aggregate
The Super-antibiotics That Could Save Us.. The alarming increase of pathogenic bacteria that are resistant to multiple antibiotics is now recognised as a major health issue fuelling demand for new drugs. Bacterial resistance is often caused by molecular changes at the bacterial surface, which alter the nature of specific drug-target interactions. The University College of London team identified a novel mechanism by which drug-target interactions in resistant bacteria can be enhanced. They examined the surface forces generated by four antibiotics; vancomycin, ristomycin, chloroeremomycin and oritavancin against drug-susceptible and drug-resistant targets on a cantilever and demonstrated significant differences in mechanical response when drug-resistant targets are challenged with different antibiotics although no significant differences were observed when using susceptible targets. Remarkably, the binding affinity for oritavancin against drug-resistant targets (70 nM) was found to be 11,000 times ...
The alarming increase of pathogenic bacteria that are resistant to multiple antibiotics is now recognized as a major health issue fuelling demand for new drugs. Bacterial resistance is often caused by molecular changes at the bacterial surface, which alter the nature of specific drug-target interactions. Here, we identify a novel mechanism by which drug-target interactions in resistant bacteria can be enhanced. We examined the surface forces generated by four antibiotics; vancomycin, ristomycin, chloroeremomycin and oritavancin against drug-susceptible and drug-resistant targets on a cantilever and demonstrated significant differences in mechanical response when drug-resistant targets are challenged with different antibiotics although no significant differences were observed when using susceptible targets ...
Antibiotic mode-of-action classification is based upon drug-target interaction and whether the resultant inhibition of cellular function is lethal to bacteria. Here we show that the three major classes of bactericidal antibiotics, regardless of drug-target interaction, stimulate the production of hi …
Mining association rules on categorical data has been discussed widely. It is a relatively difficult problem in the discovery of association rules from num
2G0G: Structure-Based Drug Design of a Novel Family of PPARgamma Partial Agonists: Virtual Screening, X-ray Crystallography, and in Vitro/in Vivo Biological Activities
CO-ADD, The Community for Open Antimicrobial Drug Discovery (http://www.co-add.org), is a global open-access screening initiative launched in February 2015 to uncover significant and rich chemical diversity held outside of corporate screening collections. CO-ADD provides unencumbered free antimicrobial screening for any interested academic researcher. CO-ADD has been recognised as a novel approach in the fight against superbugs by the Wellcome Trust, who have provided funding through their Strategic Awards initiative. Open Source Malaria (OSM) is aimed at finding new medicines for malaria using open source drug discovery, where all data and ideas are freely shared, there are no barriers to participation, and no restriction by patents. The initial set of deposited data from the CO-ADD project consists of OSM compounds screened in CO-ADD assays (DOI = 10.6019/CHEMBL3832881 ...
The FMP is devoted to research in molecular pharmacology, a field which deals with the interaction of small molecules with their cellular targets, and the effects of these interactions on cells and organisms as a whole. Our Molecular Physiology and Cell Biology section studies cell biology, physiology and pathology. Our Chemical Biology section develops molecular tools for probing cellular processes. Finally, our Structural Biology section explores drug-target interactions at the molecular level.
The FMP is devoted to research in molecular pharmacology, a field which deals with the interaction of small molecules with their cellular targets, and the effects of these interactions on cells and organisms as a whole. Our Molecular Physiology and Cell Biology section studies cell biology, physiology and pathology. Our Chemical Biology section develops molecular tools for probing cellular processes. Finally, our Structural Biology section explores drug-target interactions at the molecular level.