Modern drug design: the implication of using artificial neuronal networks and multiple molecular dynamic simulations View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2018-01

AUTHORS

Oleksandr Yakovenko, Steven J. M. Jones

ABSTRACT

We report the implementation of molecular modeling approaches developed as a part of the 2016 Grand Challenge 2, the blinded competition of computer aided drug design technologies held by the D3R Drug Design Data Resource ( https://drugdesigndata.org/ ). The challenge was focused on the ligands of the farnesoid X receptor (FXR), a highly flexible nuclear receptor of the cholesterol derivative chenodeoxycholic acid. FXR is considered an important therapeutic target for metabolic, inflammatory, bowel and obesity related diseases (Expert Opin Drug Metab Toxicol 4:523-532, 2015), but in the context of this competition it is also interesting due to the significant ligand-induced conformational changes displayed by the protein. To deal with these conformational changes we employed multiple simulations of molecular dynamics (MD). Our MD-based protocols were top-ranked in estimating the free energy of binding of the ligands and FXR protein. Our approach was ranked second in the prediction of the binding poses where we also combined MD with molecular docking and artificial neural networks. Our approach showed mediocre results for high-throughput scoring of interactions. More... »

PAGES

299-311

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s10822-017-0085-7

DOI

http://dx.doi.org/10.1007/s10822-017-0085-7

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1092656805

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/29134430


Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
Incoming Citations Browse incoming citations for this publication using opencitations.net

JSON-LD is the canonical representation for SciGraph data.

TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

[
  {
    "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
    "about": [
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0601", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Biochemistry and Cell Biology", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/06", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Biological Sciences", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Binding Sites", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Computer-Aided Design", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Drug Design", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Humans", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Ligands", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Machine Learning", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Molecular Docking Simulation", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Molecular Dynamics Simulation", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Neural Networks (Computer)", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Protein Binding", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Protein Conformation", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Receptors, Cytoplasmic and Nuclear", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "name": [
            "Ifowonco Inc, Vancouver, Canada"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Yakovenko", 
        "givenName": "Oleksandr", 
        "id": "sg:person.012046504107.74", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012046504107.74"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "BC Cancer Agency", 
          "id": "https://www.grid.ac/institutes/grid.248762.d", 
          "name": [
            "Ifowonco Inc, Vancouver, Canada", 
            "Genome Sciences Centre, BC Cancer Agency, Vancouver, Canada"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Jones", 
        "givenName": "Steven J. M.", 
        "id": "sg:person.011076371162.80", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011076371162.80"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "https://doi.org/10.1073/pnas.0409005102", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1000310295"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1529/biophysj.106.084301", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1003477559"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.bmcl.2010.12.123", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1009622396"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/nature14539", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1010020120", 
          "https://doi.org/10.1038/nature14539"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/jcc.20291", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1013759612"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/jcc.20291", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1013759612"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/0021-9991(77)90121-8", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1014025009"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1529/biophysj.103.031682", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1014093420"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/jcc.23398", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1015526841"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/jcc.21334", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1018347686"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/jcc.21334", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1018347686"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1063/1.4964776", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1018390311"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/jcc.540130812", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1019355894"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1073/pnas.1303186110", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1023385282"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00894-011-1095-3", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1024365866", 
          "https://doi.org/10.1007/s00894-011-1095-3"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/jcc.20892", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1024750196"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/ncomms7155", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1025153949", 
          "https://doi.org/10.1038/ncomms7155"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0006-3495(97)78756-3", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1027176605"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/prot.22711", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1028712683"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/prot.22711", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1028712683"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1021/jp506633n", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1030418326"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/jcc.21643", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1031428283"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/jcc.20035", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1033501081"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1517/17425255.2014.999664", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1036496759"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/srep11539", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1039447077", 
          "https://doi.org/10.1038/srep11539"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.bmcl.2009.03.008", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1043916415"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1155/2013/313419", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1047021550"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1021/acs.jctc.5b00405", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1048346470"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1103/physrevlett.78.2690", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1050584535"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1103/physrevlett.78.2690", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1050584535"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1021/ct100494z", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1052306663"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1021/ct100494z", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1052306663"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1021/acs.jmedchem.6b00632", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1055100192"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1021/ci200528d", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1055402828"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1021/ct3008099", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1055424518"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1021/ct700106b", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1055425741"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1021/ct700106b", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1055425741"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1021/ct8002354", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1055425961"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1021/ct8002354", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1055425961"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1021/jm7012198", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1055955233"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1021/jm7012198", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1055955233"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1063/1.328693", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1057931467"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1063/1.447334", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1058025354"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1063/1.448118", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1058026138"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1063/1.470117", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1058048099"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1103/physreva.31.1695", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1060473124"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1103/physreva.31.1695", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1060473124"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/5.726791", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061179979"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1126/science.220.4598.671", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1062526985"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1051/m2an/196903r100351", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1083713037"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1021/acs.jctc.7b00275", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1085765965"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2018-01", 
    "datePublishedReg": "2018-01-01", 
    "description": "We report the implementation of molecular modeling approaches developed as a part of the 2016 Grand Challenge 2, the blinded competition of computer aided drug design technologies held by the D3R Drug Design Data Resource ( https://drugdesigndata.org/ ). The challenge was focused on the ligands of the farnesoid X receptor (FXR), a highly flexible nuclear receptor of the cholesterol derivative chenodeoxycholic acid. FXR is considered an important therapeutic target for metabolic, inflammatory, bowel and obesity related diseases (Expert Opin Drug Metab Toxicol 4:523-532, 2015), but in the context of this competition it is also interesting due to the significant ligand-induced conformational changes displayed by the protein. To deal with these conformational changes we employed multiple simulations of molecular dynamics (MD). Our MD-based protocols were top-ranked in estimating the free energy of binding of the ligands and FXR protein. Our approach was ranked second in the prediction of the binding poses where we also combined MD with molecular docking and artificial neural networks. Our approach showed mediocre results for high-throughput scoring of interactions.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1007/s10822-017-0085-7", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": true, 
    "isPartOf": [
      {
        "id": "sg:journal.1105375", 
        "issn": [
          "0928-2866", 
          "1573-9023"
        ], 
        "name": "Journal of Computer-Aided Molecular Design", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "1", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "32"
      }
    ], 
    "name": "Modern drug design: the implication of using artificial neuronal networks and multiple molecular dynamic simulations", 
    "pagination": "299-311", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "cec2f3b5356280e78b29eee23adf0e5bbc5bb08dda75cdd231a4858e482ce520"
        ]
      }, 
      {
        "name": "pubmed_id", 
        "type": "PropertyValue", 
        "value": [
          "29134430"
        ]
      }, 
      {
        "name": "nlm_unique_id", 
        "type": "PropertyValue", 
        "value": [
          "8710425"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/s10822-017-0085-7"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1092656805"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/s10822-017-0085-7", 
      "https://app.dimensions.ai/details/publication/pub.1092656805"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-11T12:52", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-uberresearch-data-dimensions-target-20181106-alternative/cleanup/v134/2549eaecd7973599484d7c17b260dba0a4ecb94b/merge/v9/a6c9fde33151104705d4d7ff012ea9563521a3ce/jats-lookup/v90/0000000364_0000000364/records_72834_00000000.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://link.springer.com/10.1007%2Fs10822-017-0085-7"
  }
]
 

Download the RDF metadata as:  json-ld nt turtle xml License info

HOW TO GET THIS DATA PROGRAMMATICALLY:

JSON-LD is a popular format for linked data which is fully compatible with JSON.

curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/s10822-017-0085-7'

N-Triples is a line-based linked data format ideal for batch operations.

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/s10822-017-0085-7'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s10822-017-0085-7'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s10822-017-0085-7'


 

This table displays all metadata directly associated to this object as RDF triples.

257 TRIPLES      21 PREDICATES      83 URIs      33 LITERALS      21 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/s10822-017-0085-7 schema:about N0029a01f29f84516ba62ebf808326dd1
2 N16d5257bdd854adebba2d3e119eba9ab
3 N227bce1e0c0f4b47bf3b26fa81e9096f
4 N266233fdc9944b3081fb13bdfece62ba
5 N3d9c76568b494ad0a6bb18b587644baf
6 N6e86c8b09d1444388016ef3838b17cb0
7 N8664d1399a0a49919b3007dc33cb19d3
8 N8a50098c5f81413189ffa164548ee4b5
9 N97887412e05543ab9b6966eaca5f81f5
10 Nb62e6ed78c514dec9f47487eb5a0e054
11 Nc206e43177864bac88fbec8dab4b8413
12 Nedcba03ff81b44fd8564fa0431fe2932
13 anzsrc-for:06
14 anzsrc-for:0601
15 schema:author Nf1bb5d3e8d5a49f795484527024493fb
16 schema:citation sg:pub.10.1007/s00894-011-1095-3
17 sg:pub.10.1038/nature14539
18 sg:pub.10.1038/ncomms7155
19 sg:pub.10.1038/srep11539
20 https://doi.org/10.1002/jcc.20035
21 https://doi.org/10.1002/jcc.20291
22 https://doi.org/10.1002/jcc.20892
23 https://doi.org/10.1002/jcc.21334
24 https://doi.org/10.1002/jcc.21643
25 https://doi.org/10.1002/jcc.23398
26 https://doi.org/10.1002/jcc.540130812
27 https://doi.org/10.1002/prot.22711
28 https://doi.org/10.1016/0021-9991(77)90121-8
29 https://doi.org/10.1016/j.bmcl.2009.03.008
30 https://doi.org/10.1016/j.bmcl.2010.12.123
31 https://doi.org/10.1016/s0006-3495(97)78756-3
32 https://doi.org/10.1021/acs.jctc.5b00405
33 https://doi.org/10.1021/acs.jctc.7b00275
34 https://doi.org/10.1021/acs.jmedchem.6b00632
35 https://doi.org/10.1021/ci200528d
36 https://doi.org/10.1021/ct100494z
37 https://doi.org/10.1021/ct3008099
38 https://doi.org/10.1021/ct700106b
39 https://doi.org/10.1021/ct8002354
40 https://doi.org/10.1021/jm7012198
41 https://doi.org/10.1021/jp506633n
42 https://doi.org/10.1051/m2an/196903r100351
43 https://doi.org/10.1063/1.328693
44 https://doi.org/10.1063/1.447334
45 https://doi.org/10.1063/1.448118
46 https://doi.org/10.1063/1.470117
47 https://doi.org/10.1063/1.4964776
48 https://doi.org/10.1073/pnas.0409005102
49 https://doi.org/10.1073/pnas.1303186110
50 https://doi.org/10.1103/physreva.31.1695
51 https://doi.org/10.1103/physrevlett.78.2690
52 https://doi.org/10.1109/5.726791
53 https://doi.org/10.1126/science.220.4598.671
54 https://doi.org/10.1155/2013/313419
55 https://doi.org/10.1517/17425255.2014.999664
56 https://doi.org/10.1529/biophysj.103.031682
57 https://doi.org/10.1529/biophysj.106.084301
58 schema:datePublished 2018-01
59 schema:datePublishedReg 2018-01-01
60 schema:description We report the implementation of molecular modeling approaches developed as a part of the 2016 Grand Challenge 2, the blinded competition of computer aided drug design technologies held by the D3R Drug Design Data Resource ( https://drugdesigndata.org/ ). The challenge was focused on the ligands of the farnesoid X receptor (FXR), a highly flexible nuclear receptor of the cholesterol derivative chenodeoxycholic acid. FXR is considered an important therapeutic target for metabolic, inflammatory, bowel and obesity related diseases (Expert Opin Drug Metab Toxicol 4:523-532, 2015), but in the context of this competition it is also interesting due to the significant ligand-induced conformational changes displayed by the protein. To deal with these conformational changes we employed multiple simulations of molecular dynamics (MD). Our MD-based protocols were top-ranked in estimating the free energy of binding of the ligands and FXR protein. Our approach was ranked second in the prediction of the binding poses where we also combined MD with molecular docking and artificial neural networks. Our approach showed mediocre results for high-throughput scoring of interactions.
61 schema:genre research_article
62 schema:inLanguage en
63 schema:isAccessibleForFree true
64 schema:isPartOf N2d96ed44b9ea41f58bcb5564e3ff31d6
65 N2e0ac6f46f824ca19b13644c4bd361e6
66 sg:journal.1105375
67 schema:name Modern drug design: the implication of using artificial neuronal networks and multiple molecular dynamic simulations
68 schema:pagination 299-311
69 schema:productId N179b54f1524d4824b88883f7eacb9ced
70 N83ed21f716fa4cb9b686f3cfc1d97ee3
71 Nb25fcefb336d467192d4c323f03c3ac4
72 Nb9758a2aaece43b9ba8821bfb3267537
73 Nd4cc0d60a21640c6814fa175465b31ec
74 schema:sameAs https://app.dimensions.ai/details/publication/pub.1092656805
75 https://doi.org/10.1007/s10822-017-0085-7
76 schema:sdDatePublished 2019-04-11T12:52
77 schema:sdLicense https://scigraph.springernature.com/explorer/license/
78 schema:sdPublisher N2896ab383c0d474184ec17621e94dc98
79 schema:url https://link.springer.com/10.1007%2Fs10822-017-0085-7
80 sgo:license sg:explorer/license/
81 sgo:sdDataset articles
82 rdf:type schema:ScholarlyArticle
83 N0029a01f29f84516ba62ebf808326dd1 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
84 schema:name Humans
85 rdf:type schema:DefinedTerm
86 N16d5257bdd854adebba2d3e119eba9ab schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
87 schema:name Binding Sites
88 rdf:type schema:DefinedTerm
89 N179b54f1524d4824b88883f7eacb9ced schema:name readcube_id
90 schema:value cec2f3b5356280e78b29eee23adf0e5bbc5bb08dda75cdd231a4858e482ce520
91 rdf:type schema:PropertyValue
92 N227bce1e0c0f4b47bf3b26fa81e9096f schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
93 schema:name Computer-Aided Design
94 rdf:type schema:DefinedTerm
95 N266233fdc9944b3081fb13bdfece62ba schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
96 schema:name Molecular Docking Simulation
97 rdf:type schema:DefinedTerm
98 N2896ab383c0d474184ec17621e94dc98 schema:name Springer Nature - SN SciGraph project
99 rdf:type schema:Organization
100 N2d96ed44b9ea41f58bcb5564e3ff31d6 schema:issueNumber 1
101 rdf:type schema:PublicationIssue
102 N2e0ac6f46f824ca19b13644c4bd361e6 schema:volumeNumber 32
103 rdf:type schema:PublicationVolume
104 N3d9c76568b494ad0a6bb18b587644baf schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
105 schema:name Protein Conformation
106 rdf:type schema:DefinedTerm
107 N6e86c8b09d1444388016ef3838b17cb0 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
108 schema:name Drug Design
109 rdf:type schema:DefinedTerm
110 N83ed21f716fa4cb9b686f3cfc1d97ee3 schema:name doi
111 schema:value 10.1007/s10822-017-0085-7
112 rdf:type schema:PropertyValue
113 N8664d1399a0a49919b3007dc33cb19d3 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
114 schema:name Neural Networks (Computer)
115 rdf:type schema:DefinedTerm
116 N8a50098c5f81413189ffa164548ee4b5 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
117 schema:name Molecular Dynamics Simulation
118 rdf:type schema:DefinedTerm
119 N97887412e05543ab9b6966eaca5f81f5 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
120 schema:name Receptors, Cytoplasmic and Nuclear
121 rdf:type schema:DefinedTerm
122 Nac585006a6d449e09a3a6af6b6e0aecd rdf:first sg:person.011076371162.80
123 rdf:rest rdf:nil
124 Nb25fcefb336d467192d4c323f03c3ac4 schema:name nlm_unique_id
125 schema:value 8710425
126 rdf:type schema:PropertyValue
127 Nb2d8062ca19042dc98df71edddfd0507 schema:name Ifowonco Inc, Vancouver, Canada
128 rdf:type schema:Organization
129 Nb62e6ed78c514dec9f47487eb5a0e054 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
130 schema:name Ligands
131 rdf:type schema:DefinedTerm
132 Nb9758a2aaece43b9ba8821bfb3267537 schema:name pubmed_id
133 schema:value 29134430
134 rdf:type schema:PropertyValue
135 Nc206e43177864bac88fbec8dab4b8413 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
136 schema:name Machine Learning
137 rdf:type schema:DefinedTerm
138 Nd4cc0d60a21640c6814fa175465b31ec schema:name dimensions_id
139 schema:value pub.1092656805
140 rdf:type schema:PropertyValue
141 Nedcba03ff81b44fd8564fa0431fe2932 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
142 schema:name Protein Binding
143 rdf:type schema:DefinedTerm
144 Nf1bb5d3e8d5a49f795484527024493fb rdf:first sg:person.012046504107.74
145 rdf:rest Nac585006a6d449e09a3a6af6b6e0aecd
146 anzsrc-for:06 schema:inDefinedTermSet anzsrc-for:
147 schema:name Biological Sciences
148 rdf:type schema:DefinedTerm
149 anzsrc-for:0601 schema:inDefinedTermSet anzsrc-for:
150 schema:name Biochemistry and Cell Biology
151 rdf:type schema:DefinedTerm
152 sg:journal.1105375 schema:issn 0928-2866
153 1573-9023
154 schema:name Journal of Computer-Aided Molecular Design
155 rdf:type schema:Periodical
156 sg:person.011076371162.80 schema:affiliation https://www.grid.ac/institutes/grid.248762.d
157 schema:familyName Jones
158 schema:givenName Steven J. M.
159 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011076371162.80
160 rdf:type schema:Person
161 sg:person.012046504107.74 schema:affiliation Nb2d8062ca19042dc98df71edddfd0507
162 schema:familyName Yakovenko
163 schema:givenName Oleksandr
164 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012046504107.74
165 rdf:type schema:Person
166 sg:pub.10.1007/s00894-011-1095-3 schema:sameAs https://app.dimensions.ai/details/publication/pub.1024365866
167 https://doi.org/10.1007/s00894-011-1095-3
168 rdf:type schema:CreativeWork
169 sg:pub.10.1038/nature14539 schema:sameAs https://app.dimensions.ai/details/publication/pub.1010020120
170 https://doi.org/10.1038/nature14539
171 rdf:type schema:CreativeWork
172 sg:pub.10.1038/ncomms7155 schema:sameAs https://app.dimensions.ai/details/publication/pub.1025153949
173 https://doi.org/10.1038/ncomms7155
174 rdf:type schema:CreativeWork
175 sg:pub.10.1038/srep11539 schema:sameAs https://app.dimensions.ai/details/publication/pub.1039447077
176 https://doi.org/10.1038/srep11539
177 rdf:type schema:CreativeWork
178 https://doi.org/10.1002/jcc.20035 schema:sameAs https://app.dimensions.ai/details/publication/pub.1033501081
179 rdf:type schema:CreativeWork
180 https://doi.org/10.1002/jcc.20291 schema:sameAs https://app.dimensions.ai/details/publication/pub.1013759612
181 rdf:type schema:CreativeWork
182 https://doi.org/10.1002/jcc.20892 schema:sameAs https://app.dimensions.ai/details/publication/pub.1024750196
183 rdf:type schema:CreativeWork
184 https://doi.org/10.1002/jcc.21334 schema:sameAs https://app.dimensions.ai/details/publication/pub.1018347686
185 rdf:type schema:CreativeWork
186 https://doi.org/10.1002/jcc.21643 schema:sameAs https://app.dimensions.ai/details/publication/pub.1031428283
187 rdf:type schema:CreativeWork
188 https://doi.org/10.1002/jcc.23398 schema:sameAs https://app.dimensions.ai/details/publication/pub.1015526841
189 rdf:type schema:CreativeWork
190 https://doi.org/10.1002/jcc.540130812 schema:sameAs https://app.dimensions.ai/details/publication/pub.1019355894
191 rdf:type schema:CreativeWork
192 https://doi.org/10.1002/prot.22711 schema:sameAs https://app.dimensions.ai/details/publication/pub.1028712683
193 rdf:type schema:CreativeWork
194 https://doi.org/10.1016/0021-9991(77)90121-8 schema:sameAs https://app.dimensions.ai/details/publication/pub.1014025009
195 rdf:type schema:CreativeWork
196 https://doi.org/10.1016/j.bmcl.2009.03.008 schema:sameAs https://app.dimensions.ai/details/publication/pub.1043916415
197 rdf:type schema:CreativeWork
198 https://doi.org/10.1016/j.bmcl.2010.12.123 schema:sameAs https://app.dimensions.ai/details/publication/pub.1009622396
199 rdf:type schema:CreativeWork
200 https://doi.org/10.1016/s0006-3495(97)78756-3 schema:sameAs https://app.dimensions.ai/details/publication/pub.1027176605
201 rdf:type schema:CreativeWork
202 https://doi.org/10.1021/acs.jctc.5b00405 schema:sameAs https://app.dimensions.ai/details/publication/pub.1048346470
203 rdf:type schema:CreativeWork
204 https://doi.org/10.1021/acs.jctc.7b00275 schema:sameAs https://app.dimensions.ai/details/publication/pub.1085765965
205 rdf:type schema:CreativeWork
206 https://doi.org/10.1021/acs.jmedchem.6b00632 schema:sameAs https://app.dimensions.ai/details/publication/pub.1055100192
207 rdf:type schema:CreativeWork
208 https://doi.org/10.1021/ci200528d schema:sameAs https://app.dimensions.ai/details/publication/pub.1055402828
209 rdf:type schema:CreativeWork
210 https://doi.org/10.1021/ct100494z schema:sameAs https://app.dimensions.ai/details/publication/pub.1052306663
211 rdf:type schema:CreativeWork
212 https://doi.org/10.1021/ct3008099 schema:sameAs https://app.dimensions.ai/details/publication/pub.1055424518
213 rdf:type schema:CreativeWork
214 https://doi.org/10.1021/ct700106b schema:sameAs https://app.dimensions.ai/details/publication/pub.1055425741
215 rdf:type schema:CreativeWork
216 https://doi.org/10.1021/ct8002354 schema:sameAs https://app.dimensions.ai/details/publication/pub.1055425961
217 rdf:type schema:CreativeWork
218 https://doi.org/10.1021/jm7012198 schema:sameAs https://app.dimensions.ai/details/publication/pub.1055955233
219 rdf:type schema:CreativeWork
220 https://doi.org/10.1021/jp506633n schema:sameAs https://app.dimensions.ai/details/publication/pub.1030418326
221 rdf:type schema:CreativeWork
222 https://doi.org/10.1051/m2an/196903r100351 schema:sameAs https://app.dimensions.ai/details/publication/pub.1083713037
223 rdf:type schema:CreativeWork
224 https://doi.org/10.1063/1.328693 schema:sameAs https://app.dimensions.ai/details/publication/pub.1057931467
225 rdf:type schema:CreativeWork
226 https://doi.org/10.1063/1.447334 schema:sameAs https://app.dimensions.ai/details/publication/pub.1058025354
227 rdf:type schema:CreativeWork
228 https://doi.org/10.1063/1.448118 schema:sameAs https://app.dimensions.ai/details/publication/pub.1058026138
229 rdf:type schema:CreativeWork
230 https://doi.org/10.1063/1.470117 schema:sameAs https://app.dimensions.ai/details/publication/pub.1058048099
231 rdf:type schema:CreativeWork
232 https://doi.org/10.1063/1.4964776 schema:sameAs https://app.dimensions.ai/details/publication/pub.1018390311
233 rdf:type schema:CreativeWork
234 https://doi.org/10.1073/pnas.0409005102 schema:sameAs https://app.dimensions.ai/details/publication/pub.1000310295
235 rdf:type schema:CreativeWork
236 https://doi.org/10.1073/pnas.1303186110 schema:sameAs https://app.dimensions.ai/details/publication/pub.1023385282
237 rdf:type schema:CreativeWork
238 https://doi.org/10.1103/physreva.31.1695 schema:sameAs https://app.dimensions.ai/details/publication/pub.1060473124
239 rdf:type schema:CreativeWork
240 https://doi.org/10.1103/physrevlett.78.2690 schema:sameAs https://app.dimensions.ai/details/publication/pub.1050584535
241 rdf:type schema:CreativeWork
242 https://doi.org/10.1109/5.726791 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061179979
243 rdf:type schema:CreativeWork
244 https://doi.org/10.1126/science.220.4598.671 schema:sameAs https://app.dimensions.ai/details/publication/pub.1062526985
245 rdf:type schema:CreativeWork
246 https://doi.org/10.1155/2013/313419 schema:sameAs https://app.dimensions.ai/details/publication/pub.1047021550
247 rdf:type schema:CreativeWork
248 https://doi.org/10.1517/17425255.2014.999664 schema:sameAs https://app.dimensions.ai/details/publication/pub.1036496759
249 rdf:type schema:CreativeWork
250 https://doi.org/10.1529/biophysj.103.031682 schema:sameAs https://app.dimensions.ai/details/publication/pub.1014093420
251 rdf:type schema:CreativeWork
252 https://doi.org/10.1529/biophysj.106.084301 schema:sameAs https://app.dimensions.ai/details/publication/pub.1003477559
253 rdf:type schema:CreativeWork
254 https://www.grid.ac/institutes/grid.248762.d schema:alternateName BC Cancer Agency
255 schema:name Genome Sciences Centre, BC Cancer Agency, Vancouver, Canada
256 Ifowonco Inc, Vancouver, Canada
257 rdf:type schema:Organization
 




Preview window. Press ESC to close (or click here)


...