Compound optimization monitor (COMO) method for computational evaluation of progress in medicinal chemistry projects
Aim: Development of a new, practically applicable computational method to monitor progress in lead optimization. Computational approaches that aid in compound optimization are discussed and the Compound Optimization Monitor (COMO) method is introduced and put into scientific context. Methodology &am...
Ausführliche Beschreibung
Autor*in: |
Dimitar Yonchev [verfasserIn] Martin Vogt [verfasserIn] Jürgen Bajorath [verfasserIn] |
---|
Format: |
E-Artikel |
---|---|
Sprache: |
Englisch |
Erschienen: |
2019 |
---|
Schlagwörter: |
---|
Übergeordnetes Werk: |
In: Future Drug Discovery - Future Science Ltd, 2021, 1(2019), 2 |
---|---|
Übergeordnetes Werk: |
volume:1 ; year:2019 ; number:2 |
Links: |
---|
DOI / URN: |
10.4155/fdd-2019-0016 |
---|
Katalog-ID: |
DOAJ016189612 |
---|
LEADER | 01000caa a22002652 4500 | ||
---|---|---|---|
001 | DOAJ016189612 | ||
003 | DE-627 | ||
005 | 20230310081407.0 | ||
007 | cr uuu---uuuuu | ||
008 | 230226s2019 xx |||||o 00| ||eng c | ||
024 | 7 | |a 10.4155/fdd-2019-0016 |2 doi | |
035 | |a (DE-627)DOAJ016189612 | ||
035 | |a (DE-599)DOAJ2e8b41921ec3469ea33fb8300c005cd9 | ||
040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
041 | |a eng | ||
050 | 0 | |a RS1-441 | |
100 | 0 | |a Dimitar Yonchev |e verfasserin |4 aut | |
245 | 1 | 0 | |a Compound optimization monitor (COMO) method for computational evaluation of progress in medicinal chemistry projects |
264 | 1 | |c 2019 | |
336 | |a Text |b txt |2 rdacontent | ||
337 | |a Computermedien |b c |2 rdamedia | ||
338 | |a Online-Ressource |b cr |2 rdacarrier | ||
520 | |a Aim: Development of a new, practically applicable computational method to monitor progress in lead optimization. Computational approaches that aid in compound optimization are discussed and the Compound Optimization Monitor (COMO) method is introduced and put into scientific context. Methodology & calculations: The methodological concept and the COMO scoring scheme are described in detail. Results & discussions: Calculation parameters are evaluated, and profiling results reported for an ensemble of analog series. Future perspective: The dual role of virtual analogs as diagnostic tools for progress evaluation and as potential candidates for lead optimization is discussed. In light of this dual role, interfacing COMO with machine learning for compound activity prediction and prioritization of candidates is highlighted as a future research objective. | ||
650 | 4 | |a chemical saturation | |
650 | 4 | |a compound optimization | |
650 | 4 | |a computational analysis | |
650 | 4 | |a decision support | |
650 | 4 | |a drug discovery | |
650 | 4 | |a property evaluation | |
653 | 0 | |a Pharmacy and materia medica | |
700 | 0 | |a Martin Vogt |e verfasserin |4 aut | |
700 | 0 | |a Jürgen Bajorath |e verfasserin |4 aut | |
773 | 0 | 8 | |i In |t Future Drug Discovery |d Future Science Ltd, 2021 |g 1(2019), 2 |w (DE-627)1760793973 |x 26313316 |7 nnns |
773 | 1 | 8 | |g volume:1 |g year:2019 |g number:2 |
856 | 4 | 0 | |u https://doi.org/10.4155/fdd-2019-0016 |z kostenfrei |
856 | 4 | 0 | |u https://doaj.org/article/2e8b41921ec3469ea33fb8300c005cd9 |z kostenfrei |
856 | 4 | 0 | |u https://www.future-science.com/doi/10.4155/fdd-2019-0016 |z kostenfrei |
856 | 4 | 2 | |u https://doaj.org/toc/2631-3316 |y Journal toc |z kostenfrei |
912 | |a GBV_USEFLAG_A | ||
912 | |a SYSFLAG_A | ||
912 | |a GBV_DOAJ | ||
912 | |a GBV_ILN_20 | ||
912 | |a GBV_ILN_22 | ||
912 | |a GBV_ILN_23 | ||
912 | |a GBV_ILN_24 | ||
912 | |a GBV_ILN_31 | ||
912 | |a GBV_ILN_39 | ||
912 | |a GBV_ILN_40 | ||
912 | |a GBV_ILN_60 | ||
912 | |a GBV_ILN_62 | ||
912 | |a GBV_ILN_63 | ||
912 | |a GBV_ILN_65 | ||
912 | |a GBV_ILN_69 | ||
912 | |a GBV_ILN_70 | ||
912 | |a GBV_ILN_73 | ||
912 | |a GBV_ILN_74 | ||
912 | |a GBV_ILN_95 | ||
912 | |a GBV_ILN_105 | ||
912 | |a GBV_ILN_110 | ||
912 | |a GBV_ILN_151 | ||
912 | |a GBV_ILN_161 | ||
912 | |a GBV_ILN_170 | ||
912 | |a GBV_ILN_206 | ||
912 | |a GBV_ILN_213 | ||
912 | |a GBV_ILN_230 | ||
912 | |a GBV_ILN_285 | ||
912 | |a GBV_ILN_293 | ||
912 | |a GBV_ILN_602 | ||
912 | |a GBV_ILN_2014 | ||
912 | |a GBV_ILN_4012 | ||
912 | |a GBV_ILN_4037 | ||
912 | |a GBV_ILN_4112 | ||
912 | |a GBV_ILN_4125 | ||
912 | |a GBV_ILN_4126 | ||
912 | |a GBV_ILN_4249 | ||
912 | |a GBV_ILN_4305 | ||
912 | |a GBV_ILN_4306 | ||
912 | |a GBV_ILN_4307 | ||
912 | |a GBV_ILN_4313 | ||
912 | |a GBV_ILN_4322 | ||
912 | |a GBV_ILN_4323 | ||
912 | |a GBV_ILN_4324 | ||
912 | |a GBV_ILN_4325 | ||
912 | |a GBV_ILN_4338 | ||
912 | |a GBV_ILN_4367 | ||
912 | |a GBV_ILN_4700 | ||
951 | |a AR | ||
952 | |d 1 |j 2019 |e 2 |
author_variant |
d y dy m v mv j b jb |
---|---|
matchkey_str |
article:26313316:2019----::opudpiiainoiocmmtofropttoaeautooporsi |
hierarchy_sort_str |
2019 |
callnumber-subject-code |
RS |
publishDate |
2019 |
allfields |
10.4155/fdd-2019-0016 doi (DE-627)DOAJ016189612 (DE-599)DOAJ2e8b41921ec3469ea33fb8300c005cd9 DE-627 ger DE-627 rakwb eng RS1-441 Dimitar Yonchev verfasserin aut Compound optimization monitor (COMO) method for computational evaluation of progress in medicinal chemistry projects 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Aim: Development of a new, practically applicable computational method to monitor progress in lead optimization. Computational approaches that aid in compound optimization are discussed and the Compound Optimization Monitor (COMO) method is introduced and put into scientific context. Methodology & calculations: The methodological concept and the COMO scoring scheme are described in detail. Results & discussions: Calculation parameters are evaluated, and profiling results reported for an ensemble of analog series. Future perspective: The dual role of virtual analogs as diagnostic tools for progress evaluation and as potential candidates for lead optimization is discussed. In light of this dual role, interfacing COMO with machine learning for compound activity prediction and prioritization of candidates is highlighted as a future research objective. chemical saturation compound optimization computational analysis decision support drug discovery property evaluation Pharmacy and materia medica Martin Vogt verfasserin aut Jürgen Bajorath verfasserin aut In Future Drug Discovery Future Science Ltd, 2021 1(2019), 2 (DE-627)1760793973 26313316 nnns volume:1 year:2019 number:2 https://doi.org/10.4155/fdd-2019-0016 kostenfrei https://doaj.org/article/2e8b41921ec3469ea33fb8300c005cd9 kostenfrei https://www.future-science.com/doi/10.4155/fdd-2019-0016 kostenfrei https://doaj.org/toc/2631-3316 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 1 2019 2 |
spelling |
10.4155/fdd-2019-0016 doi (DE-627)DOAJ016189612 (DE-599)DOAJ2e8b41921ec3469ea33fb8300c005cd9 DE-627 ger DE-627 rakwb eng RS1-441 Dimitar Yonchev verfasserin aut Compound optimization monitor (COMO) method for computational evaluation of progress in medicinal chemistry projects 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Aim: Development of a new, practically applicable computational method to monitor progress in lead optimization. Computational approaches that aid in compound optimization are discussed and the Compound Optimization Monitor (COMO) method is introduced and put into scientific context. Methodology & calculations: The methodological concept and the COMO scoring scheme are described in detail. Results & discussions: Calculation parameters are evaluated, and profiling results reported for an ensemble of analog series. Future perspective: The dual role of virtual analogs as diagnostic tools for progress evaluation and as potential candidates for lead optimization is discussed. In light of this dual role, interfacing COMO with machine learning for compound activity prediction and prioritization of candidates is highlighted as a future research objective. chemical saturation compound optimization computational analysis decision support drug discovery property evaluation Pharmacy and materia medica Martin Vogt verfasserin aut Jürgen Bajorath verfasserin aut In Future Drug Discovery Future Science Ltd, 2021 1(2019), 2 (DE-627)1760793973 26313316 nnns volume:1 year:2019 number:2 https://doi.org/10.4155/fdd-2019-0016 kostenfrei https://doaj.org/article/2e8b41921ec3469ea33fb8300c005cd9 kostenfrei https://www.future-science.com/doi/10.4155/fdd-2019-0016 kostenfrei https://doaj.org/toc/2631-3316 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 1 2019 2 |
allfields_unstemmed |
10.4155/fdd-2019-0016 doi (DE-627)DOAJ016189612 (DE-599)DOAJ2e8b41921ec3469ea33fb8300c005cd9 DE-627 ger DE-627 rakwb eng RS1-441 Dimitar Yonchev verfasserin aut Compound optimization monitor (COMO) method for computational evaluation of progress in medicinal chemistry projects 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Aim: Development of a new, practically applicable computational method to monitor progress in lead optimization. Computational approaches that aid in compound optimization are discussed and the Compound Optimization Monitor (COMO) method is introduced and put into scientific context. Methodology & calculations: The methodological concept and the COMO scoring scheme are described in detail. Results & discussions: Calculation parameters are evaluated, and profiling results reported for an ensemble of analog series. Future perspective: The dual role of virtual analogs as diagnostic tools for progress evaluation and as potential candidates for lead optimization is discussed. In light of this dual role, interfacing COMO with machine learning for compound activity prediction and prioritization of candidates is highlighted as a future research objective. chemical saturation compound optimization computational analysis decision support drug discovery property evaluation Pharmacy and materia medica Martin Vogt verfasserin aut Jürgen Bajorath verfasserin aut In Future Drug Discovery Future Science Ltd, 2021 1(2019), 2 (DE-627)1760793973 26313316 nnns volume:1 year:2019 number:2 https://doi.org/10.4155/fdd-2019-0016 kostenfrei https://doaj.org/article/2e8b41921ec3469ea33fb8300c005cd9 kostenfrei https://www.future-science.com/doi/10.4155/fdd-2019-0016 kostenfrei https://doaj.org/toc/2631-3316 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 1 2019 2 |
allfieldsGer |
10.4155/fdd-2019-0016 doi (DE-627)DOAJ016189612 (DE-599)DOAJ2e8b41921ec3469ea33fb8300c005cd9 DE-627 ger DE-627 rakwb eng RS1-441 Dimitar Yonchev verfasserin aut Compound optimization monitor (COMO) method for computational evaluation of progress in medicinal chemistry projects 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Aim: Development of a new, practically applicable computational method to monitor progress in lead optimization. Computational approaches that aid in compound optimization are discussed and the Compound Optimization Monitor (COMO) method is introduced and put into scientific context. Methodology & calculations: The methodological concept and the COMO scoring scheme are described in detail. Results & discussions: Calculation parameters are evaluated, and profiling results reported for an ensemble of analog series. Future perspective: The dual role of virtual analogs as diagnostic tools for progress evaluation and as potential candidates for lead optimization is discussed. In light of this dual role, interfacing COMO with machine learning for compound activity prediction and prioritization of candidates is highlighted as a future research objective. chemical saturation compound optimization computational analysis decision support drug discovery property evaluation Pharmacy and materia medica Martin Vogt verfasserin aut Jürgen Bajorath verfasserin aut In Future Drug Discovery Future Science Ltd, 2021 1(2019), 2 (DE-627)1760793973 26313316 nnns volume:1 year:2019 number:2 https://doi.org/10.4155/fdd-2019-0016 kostenfrei https://doaj.org/article/2e8b41921ec3469ea33fb8300c005cd9 kostenfrei https://www.future-science.com/doi/10.4155/fdd-2019-0016 kostenfrei https://doaj.org/toc/2631-3316 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 1 2019 2 |
allfieldsSound |
10.4155/fdd-2019-0016 doi (DE-627)DOAJ016189612 (DE-599)DOAJ2e8b41921ec3469ea33fb8300c005cd9 DE-627 ger DE-627 rakwb eng RS1-441 Dimitar Yonchev verfasserin aut Compound optimization monitor (COMO) method for computational evaluation of progress in medicinal chemistry projects 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Aim: Development of a new, practically applicable computational method to monitor progress in lead optimization. Computational approaches that aid in compound optimization are discussed and the Compound Optimization Monitor (COMO) method is introduced and put into scientific context. Methodology & calculations: The methodological concept and the COMO scoring scheme are described in detail. Results & discussions: Calculation parameters are evaluated, and profiling results reported for an ensemble of analog series. Future perspective: The dual role of virtual analogs as diagnostic tools for progress evaluation and as potential candidates for lead optimization is discussed. In light of this dual role, interfacing COMO with machine learning for compound activity prediction and prioritization of candidates is highlighted as a future research objective. chemical saturation compound optimization computational analysis decision support drug discovery property evaluation Pharmacy and materia medica Martin Vogt verfasserin aut Jürgen Bajorath verfasserin aut In Future Drug Discovery Future Science Ltd, 2021 1(2019), 2 (DE-627)1760793973 26313316 nnns volume:1 year:2019 number:2 https://doi.org/10.4155/fdd-2019-0016 kostenfrei https://doaj.org/article/2e8b41921ec3469ea33fb8300c005cd9 kostenfrei https://www.future-science.com/doi/10.4155/fdd-2019-0016 kostenfrei https://doaj.org/toc/2631-3316 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 1 2019 2 |
language |
English |
source |
In Future Drug Discovery 1(2019), 2 volume:1 year:2019 number:2 |
sourceStr |
In Future Drug Discovery 1(2019), 2 volume:1 year:2019 number:2 |
format_phy_str_mv |
Article |
institution |
findex.gbv.de |
topic_facet |
chemical saturation compound optimization computational analysis decision support drug discovery property evaluation Pharmacy and materia medica |
isfreeaccess_bool |
true |
container_title |
Future Drug Discovery |
authorswithroles_txt_mv |
Dimitar Yonchev @@aut@@ Martin Vogt @@aut@@ Jürgen Bajorath @@aut@@ |
publishDateDaySort_date |
2019-01-01T00:00:00Z |
hierarchy_top_id |
1760793973 |
id |
DOAJ016189612 |
language_de |
englisch |
fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">DOAJ016189612</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230310081407.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230226s2019 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.4155/fdd-2019-0016</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ016189612</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJ2e8b41921ec3469ea33fb8300c005cd9</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">RS1-441</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Dimitar Yonchev</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Compound optimization monitor (COMO) method for computational evaluation of progress in medicinal chemistry projects</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2019</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Aim: Development of a new, practically applicable computational method to monitor progress in lead optimization. Computational approaches that aid in compound optimization are discussed and the Compound Optimization Monitor (COMO) method is introduced and put into scientific context. Methodology & calculations: The methodological concept and the COMO scoring scheme are described in detail. Results & discussions: Calculation parameters are evaluated, and profiling results reported for an ensemble of analog series. Future perspective: The dual role of virtual analogs as diagnostic tools for progress evaluation and as potential candidates for lead optimization is discussed. In light of this dual role, interfacing COMO with machine learning for compound activity prediction and prioritization of candidates is highlighted as a future research objective.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">chemical saturation</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">compound optimization</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">computational analysis</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">decision support</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">drug discovery</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">property evaluation</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Pharmacy and materia medica</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Martin Vogt</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Jürgen Bajorath</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="t">Future Drug Discovery</subfield><subfield code="d">Future Science Ltd, 2021</subfield><subfield code="g">1(2019), 2</subfield><subfield code="w">(DE-627)1760793973</subfield><subfield code="x">26313316</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:1</subfield><subfield code="g">year:2019</subfield><subfield code="g">number:2</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.4155/fdd-2019-0016</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doaj.org/article/2e8b41921ec3469ea33fb8300c005cd9</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://www.future-science.com/doi/10.4155/fdd-2019-0016</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/2631-3316</subfield><subfield code="y">Journal toc</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_DOAJ</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_31</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_74</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_170</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_206</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">1</subfield><subfield code="j">2019</subfield><subfield code="e">2</subfield></datafield></record></collection>
|
callnumber-first |
R - Medicine |
author |
Dimitar Yonchev |
spellingShingle |
Dimitar Yonchev misc RS1-441 misc chemical saturation misc compound optimization misc computational analysis misc decision support misc drug discovery misc property evaluation misc Pharmacy and materia medica Compound optimization monitor (COMO) method for computational evaluation of progress in medicinal chemistry projects |
authorStr |
Dimitar Yonchev |
ppnlink_with_tag_str_mv |
@@773@@(DE-627)1760793973 |
format |
electronic Article |
delete_txt_mv |
keep |
author_role |
aut aut aut |
collection |
DOAJ |
remote_str |
true |
callnumber-label |
RS1-441 |
illustrated |
Not Illustrated |
issn |
26313316 |
topic_title |
RS1-441 Compound optimization monitor (COMO) method for computational evaluation of progress in medicinal chemistry projects chemical saturation compound optimization computational analysis decision support drug discovery property evaluation |
topic |
misc RS1-441 misc chemical saturation misc compound optimization misc computational analysis misc decision support misc drug discovery misc property evaluation misc Pharmacy and materia medica |
topic_unstemmed |
misc RS1-441 misc chemical saturation misc compound optimization misc computational analysis misc decision support misc drug discovery misc property evaluation misc Pharmacy and materia medica |
topic_browse |
misc RS1-441 misc chemical saturation misc compound optimization misc computational analysis misc decision support misc drug discovery misc property evaluation misc Pharmacy and materia medica |
format_facet |
Elektronische Aufsätze Aufsätze Elektronische Ressource |
format_main_str_mv |
Text Zeitschrift/Artikel |
carriertype_str_mv |
cr |
hierarchy_parent_title |
Future Drug Discovery |
hierarchy_parent_id |
1760793973 |
hierarchy_top_title |
Future Drug Discovery |
isfreeaccess_txt |
true |
familylinks_str_mv |
(DE-627)1760793973 |
title |
Compound optimization monitor (COMO) method for computational evaluation of progress in medicinal chemistry projects |
ctrlnum |
(DE-627)DOAJ016189612 (DE-599)DOAJ2e8b41921ec3469ea33fb8300c005cd9 |
title_full |
Compound optimization monitor (COMO) method for computational evaluation of progress in medicinal chemistry projects |
author_sort |
Dimitar Yonchev |
journal |
Future Drug Discovery |
journalStr |
Future Drug Discovery |
callnumber-first-code |
R |
lang_code |
eng |
isOA_bool |
true |
recordtype |
marc |
publishDateSort |
2019 |
contenttype_str_mv |
txt |
author_browse |
Dimitar Yonchev Martin Vogt Jürgen Bajorath |
container_volume |
1 |
class |
RS1-441 |
format_se |
Elektronische Aufsätze |
author-letter |
Dimitar Yonchev |
doi_str_mv |
10.4155/fdd-2019-0016 |
author2-role |
verfasserin |
title_sort |
compound optimization monitor (como) method for computational evaluation of progress in medicinal chemistry projects |
callnumber |
RS1-441 |
title_auth |
Compound optimization monitor (COMO) method for computational evaluation of progress in medicinal chemistry projects |
abstract |
Aim: Development of a new, practically applicable computational method to monitor progress in lead optimization. Computational approaches that aid in compound optimization are discussed and the Compound Optimization Monitor (COMO) method is introduced and put into scientific context. Methodology & calculations: The methodological concept and the COMO scoring scheme are described in detail. Results & discussions: Calculation parameters are evaluated, and profiling results reported for an ensemble of analog series. Future perspective: The dual role of virtual analogs as diagnostic tools for progress evaluation and as potential candidates for lead optimization is discussed. In light of this dual role, interfacing COMO with machine learning for compound activity prediction and prioritization of candidates is highlighted as a future research objective. |
abstractGer |
Aim: Development of a new, practically applicable computational method to monitor progress in lead optimization. Computational approaches that aid in compound optimization are discussed and the Compound Optimization Monitor (COMO) method is introduced and put into scientific context. Methodology & calculations: The methodological concept and the COMO scoring scheme are described in detail. Results & discussions: Calculation parameters are evaluated, and profiling results reported for an ensemble of analog series. Future perspective: The dual role of virtual analogs as diagnostic tools for progress evaluation and as potential candidates for lead optimization is discussed. In light of this dual role, interfacing COMO with machine learning for compound activity prediction and prioritization of candidates is highlighted as a future research objective. |
abstract_unstemmed |
Aim: Development of a new, practically applicable computational method to monitor progress in lead optimization. Computational approaches that aid in compound optimization are discussed and the Compound Optimization Monitor (COMO) method is introduced and put into scientific context. Methodology & calculations: The methodological concept and the COMO scoring scheme are described in detail. Results & discussions: Calculation parameters are evaluated, and profiling results reported for an ensemble of analog series. Future perspective: The dual role of virtual analogs as diagnostic tools for progress evaluation and as potential candidates for lead optimization is discussed. In light of this dual role, interfacing COMO with machine learning for compound activity prediction and prioritization of candidates is highlighted as a future research objective. |
collection_details |
GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 |
container_issue |
2 |
title_short |
Compound optimization monitor (COMO) method for computational evaluation of progress in medicinal chemistry projects |
url |
https://doi.org/10.4155/fdd-2019-0016 https://doaj.org/article/2e8b41921ec3469ea33fb8300c005cd9 https://www.future-science.com/doi/10.4155/fdd-2019-0016 https://doaj.org/toc/2631-3316 |
remote_bool |
true |
author2 |
Martin Vogt Jürgen Bajorath |
author2Str |
Martin Vogt Jürgen Bajorath |
ppnlink |
1760793973 |
callnumber-subject |
RS - Pharmacy |
mediatype_str_mv |
c |
isOA_txt |
true |
hochschulschrift_bool |
false |
doi_str |
10.4155/fdd-2019-0016 |
callnumber-a |
RS1-441 |
up_date |
2024-07-03T19:31:29.692Z |
_version_ |
1803587514209927168 |
fullrecord_marcxml |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">DOAJ016189612</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230310081407.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230226s2019 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.4155/fdd-2019-0016</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ016189612</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJ2e8b41921ec3469ea33fb8300c005cd9</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">RS1-441</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Dimitar Yonchev</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Compound optimization monitor (COMO) method for computational evaluation of progress in medicinal chemistry projects</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2019</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Aim: Development of a new, practically applicable computational method to monitor progress in lead optimization. Computational approaches that aid in compound optimization are discussed and the Compound Optimization Monitor (COMO) method is introduced and put into scientific context. Methodology & calculations: The methodological concept and the COMO scoring scheme are described in detail. Results & discussions: Calculation parameters are evaluated, and profiling results reported for an ensemble of analog series. Future perspective: The dual role of virtual analogs as diagnostic tools for progress evaluation and as potential candidates for lead optimization is discussed. In light of this dual role, interfacing COMO with machine learning for compound activity prediction and prioritization of candidates is highlighted as a future research objective.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">chemical saturation</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">compound optimization</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">computational analysis</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">decision support</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">drug discovery</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">property evaluation</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Pharmacy and materia medica</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Martin Vogt</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Jürgen Bajorath</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="t">Future Drug Discovery</subfield><subfield code="d">Future Science Ltd, 2021</subfield><subfield code="g">1(2019), 2</subfield><subfield code="w">(DE-627)1760793973</subfield><subfield code="x">26313316</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:1</subfield><subfield code="g">year:2019</subfield><subfield code="g">number:2</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.4155/fdd-2019-0016</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doaj.org/article/2e8b41921ec3469ea33fb8300c005cd9</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://www.future-science.com/doi/10.4155/fdd-2019-0016</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/2631-3316</subfield><subfield code="y">Journal toc</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_DOAJ</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_31</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_74</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_170</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_206</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">1</subfield><subfield code="j">2019</subfield><subfield code="e">2</subfield></datafield></record></collection>
|
score |
7.4009523 |