DeepPurpose: a deep learning library for drug–target interaction prediction
Abstract 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 bi...
Saved in:
| Published in | Bioinformatics (Oxford, England) Vol. 36; no. 22-23; pp. 5545 - 5547 |
|---|---|
| Main Authors | , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
England
Oxford University Press
01.12.2020
Oxford Publishing Limited (England) |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1367-4803 1367-4811 1367-4811 |
| DOI | 10.1093/bioinformatics/btaa1005 |
Cover
Loading…
| Abstract | Abstract
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 benchmark datasets.
Availability and implementation
https://github.com/kexinhuang12345/DeepPurpose.
Supplementary information
Supplementary data are available at Bioinformatics online. |
|---|---|
| AbstractList | 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 benchmark datasets.SUMMARYAccurate 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 benchmark datasets.https://github.com/kexinhuang12345/DeepPurpose.AVAILABILITY AND IMPLEMENTATIONhttps://github.com/kexinhuang12345/DeepPurpose.Supplementary data are available at Bioinformatics online.SUPPLEMENTARY INFORMATIONSupplementary data are available at Bioinformatics online. 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 benchmark datasets. https://github.com/kexinhuang12345/DeepPurpose. Supplementary data are available at Bioinformatics online. Abstract 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 benchmark datasets. Availability and implementation https://github.com/kexinhuang12345/DeepPurpose. Supplementary information Supplementary data are available at Bioinformatics online. 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 benchmark datasets. Availability and implementation https://github.com/kexinhuang12345/DeepPurpose. Supplementary information Supplementary data are available at Bioinformatics online. |
| Author | Sun, Jimeng Fu, Tianfan Glass, Lucas M Huang, Kexin Xiao, Cao Zitnik, Marinka |
| AuthorAffiliation | btaa1005-aff4 University of Illinois at Urbana-Champaign , Urbana, IL 61801, USA btaa1005-aff3 IQVIA , Cambridge, MA 02139, USA btaa1005-aff1 Harvard University , Boston, MA 02115, USA btaa1005-aff2 Georgia Institute of Technology , Atlanta, GA 30332, USA |
| AuthorAffiliation_xml | – name: btaa1005-aff2 Georgia Institute of Technology , Atlanta, GA 30332, USA – name: btaa1005-aff4 University of Illinois at Urbana-Champaign , Urbana, IL 61801, USA – name: btaa1005-aff1 Harvard University , Boston, MA 02115, USA – name: btaa1005-aff3 IQVIA , Cambridge, MA 02139, USA |
| Author_xml | – sequence: 1 givenname: Kexin orcidid: 0000-0001-6693-8390 surname: Huang fullname: Huang, Kexin – sequence: 2 givenname: Tianfan surname: Fu fullname: Fu, Tianfan – sequence: 3 givenname: Lucas M surname: Glass fullname: Glass, Lucas M – sequence: 4 givenname: Marinka surname: Zitnik fullname: Zitnik, Marinka – sequence: 5 givenname: Cao surname: Xiao fullname: Xiao, Cao – sequence: 6 givenname: Jimeng surname: Sun fullname: Sun, Jimeng email: jimeng@illinois.edu |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/33275143$$D View this record in MEDLINE/PubMed |
| BookMark | eNqNkc1O3TAQhS1ExV95BRqpm25usePYTpCKVNFfCUQX7dqaOONbo1w72E6l7ngH3rBPUtPLRYVNu_JY_s6Z45l9su2DR0JeMPqa0Y4f9y44b0NcQXYmHfcZgFEqtsge41Itmpax7Yea8l2yn9IVLQQVcofscl4rwRq-Ry7eIU5f5jiFhCcVVEO5ViNC9M4vq9H1EeLPqnSqhjgvf93cZohLzJXzGSOY7IKvpoiD-1M-J88sjAkP788D8u3D-69nnxbnlx8_n709XxghmrwQnbKttVLSFjrTSdkxZRQTUqGtpW3oQCm3lKsWaiONamQPirOhEYoBw4EfkNO17zT3KxwM-hxh1FN0qxJXB3D68Yt33_Uy_NAtZbKRqhi8ujeI4XrGlPXKJYPjCB7DnHRdIMmE4rSgL5-gV2GOvnxPc1YXQ97xulBHfyd6iLKZdAHerAETQ0oRrTYuw93QSkA3akb13Wb1483qzWaLXj3Rb1r8W1mvlWGe_lv0G6q8w4Q |
| CitedBy_id | crossref_primary_10_1093_bib_bbae121 crossref_primary_10_1016_j_eswa_2024_123730 crossref_primary_10_3390_molecules27165114 crossref_primary_10_1016_j_csbj_2023_01_021 crossref_primary_10_1515_sagmb_2024_0004 crossref_primary_10_3389_fgene_2025_1471037 crossref_primary_10_1016_j_isci_2023_107646 crossref_primary_10_1080_23311916_2021_1958666 crossref_primary_10_1016_j_aichem_2024_100070 crossref_primary_10_1016_j_cjprs_2022_10_004 crossref_primary_10_1016_j_str_2024_02_004 crossref_primary_10_1021_acsomega_2c06653 crossref_primary_10_1093_bib_bbae480 crossref_primary_10_1016_j_compbiolchem_2024_108172 crossref_primary_10_3390_ijms25073747 crossref_primary_10_1021_acs_jcim_4c00584 crossref_primary_10_1038_s41467_025_57828_0 crossref_primary_10_1016_j_patter_2022_100445 crossref_primary_10_1080_07391102_2024_2316767 crossref_primary_10_3389_fphar_2024_1459954 crossref_primary_10_1016_j_jiph_2023_04_019 crossref_primary_10_1007_s11030_021_10225_3 crossref_primary_10_1142_S0219720024500161 crossref_primary_10_1016_j_ejmech_2023_115500 crossref_primary_10_1016_j_compbiomed_2023_107283 crossref_primary_10_1016_j_eswa_2023_121274 crossref_primary_10_1021_acsomega_2c06781 crossref_primary_10_1093_bib_bbad261 crossref_primary_10_1021_acs_jctc_2c01314 crossref_primary_10_13005_bbra_3318 crossref_primary_10_1038_s41467_023_37572_z crossref_primary_10_3390_biomedicines11072025 crossref_primary_10_1080_07391102_2023_2250479 crossref_primary_10_1016_j_compbiomed_2024_108435 crossref_primary_10_3934_mbe_2022151 crossref_primary_10_1093_bib_bbad386 crossref_primary_10_1007_s10822_024_00571_3 crossref_primary_10_1002_tcr_202400182 crossref_primary_10_1038_s41598_025_86840_z crossref_primary_10_48175_IJARSCT_22854 crossref_primary_10_1093_bib_bbac326 crossref_primary_10_1007_s11030_021_10217_3 crossref_primary_10_1016_j_neucom_2023_127203 crossref_primary_10_1038_s41467_023_43597_1 crossref_primary_10_1186_s12859_023_05181_8 crossref_primary_10_1007_s00894_023_05492_w crossref_primary_10_1080_07391102_2023_2293277 crossref_primary_10_1093_bib_bbaf078 crossref_primary_10_1073_pnas_2220778120 crossref_primary_10_1016_j_xcrp_2023_101520 crossref_primary_10_1080_1062936X_2024_2306336 crossref_primary_10_1016_j_biopha_2022_113350 crossref_primary_10_1002_adts_202400378 crossref_primary_10_3390_genes15010054 crossref_primary_10_1093_bioinformatics_btac035 crossref_primary_10_3389_fphar_2022_854965 crossref_primary_10_3389_fphar_2022_892559 crossref_primary_10_7554_eLife_97330 crossref_primary_10_1093_bioinformatics_btac676 crossref_primary_10_1371_journal_pone_0289890 crossref_primary_10_1038_s41598_022_17573_6 crossref_primary_10_1016_j_bbrc_2023_07_018 crossref_primary_10_1016_j_compbiomed_2023_107226 crossref_primary_10_1016_j_isci_2022_105314 crossref_primary_10_1093_bib_bbad327 crossref_primary_10_1007_s00894_022_05373_8 crossref_primary_10_1016_j_bmc_2022_117003 crossref_primary_10_1109_TKDE_2021_3052150 crossref_primary_10_1016_j_bmc_2025_118095 crossref_primary_10_1038_s41598_022_08787_9 crossref_primary_10_1371_journal_pone_0276609 crossref_primary_10_1016_j_scitotenv_2021_151103 crossref_primary_10_1080_07391102_2024_2435634 crossref_primary_10_1186_s13321_022_00666_9 crossref_primary_10_18632_aging_206190 crossref_primary_10_1002_qub2_32 crossref_primary_10_1021_acs_jcim_4c00171 crossref_primary_10_3390_ijms25147753 crossref_primary_10_1007_s44163_024_00192_7 crossref_primary_10_3389_fphar_2022_1098800 crossref_primary_10_1093_bib_bbac199 crossref_primary_10_3389_frai_2024_1372161 crossref_primary_10_1007_s00521_024_10814_x crossref_primary_10_1016_j_compbiomed_2024_108339 crossref_primary_10_1021_acs_jcim_3c00866 crossref_primary_10_1097_PRS_0000000000010350 crossref_primary_10_1109_TCBB_2021_3121275 crossref_primary_10_3390_biomedicines13020262 crossref_primary_10_1039_D1SC06946B crossref_primary_10_1109_TCBB_2022_3205282 crossref_primary_10_1109_TNNLS_2021_3111745 crossref_primary_10_1371_journal_pcbi_1011597 crossref_primary_10_1038_s41598_022_16493_9 crossref_primary_10_1093_bib_bbac226 crossref_primary_10_1080_07391102_2022_2042389 crossref_primary_10_1109_TCBB_2023_3237863 crossref_primary_10_3389_fphar_2022_1007556 crossref_primary_10_1016_j_heliyon_2023_e18622 crossref_primary_10_3390_ijms22189983 crossref_primary_10_1007_s12672_025_01978_6 crossref_primary_10_1038_s42003_024_06797_z crossref_primary_10_1038_s42256_024_00876_w crossref_primary_10_1038_s41467_021_27137_3 crossref_primary_10_1186_s12859_021_04464_2 crossref_primary_10_3390_biom11050643 crossref_primary_10_3390_molecules27134098 crossref_primary_10_1021_acs_jcim_4c00422 crossref_primary_10_1093_bib_bbac263 crossref_primary_10_3389_fphar_2024_1331062 crossref_primary_10_1093_bioinformatics_btac731 crossref_primary_10_1155_2022_2461055 crossref_primary_10_3390_ijerph18031117 crossref_primary_10_1021_acs_jcim_4c01907 crossref_primary_10_1109_JBHI_2021_3121798 crossref_primary_10_3390_molecules27103112 crossref_primary_10_1002_jcc_27388 crossref_primary_10_3390_ijms25168917 crossref_primary_10_15302_J_QB_022_0320 crossref_primary_10_1016_j_aichem_2023_100039 crossref_primary_10_3390_ijms24076573 crossref_primary_10_1016_j_ailsci_2024_100100 crossref_primary_10_3390_biology12040519 crossref_primary_10_1039_D2CP05332B crossref_primary_10_3389_fgene_2021_638330 crossref_primary_10_1007_s00530_024_01325_9 crossref_primary_10_3390_ijms242116013 crossref_primary_10_1109_TBDATA_2020_3048644 crossref_primary_10_1021_acsomega_3c00030 crossref_primary_10_1016_j_compbiomed_2022_105214 crossref_primary_10_1016_j_crphar_2021_100042 crossref_primary_10_1155_2024_6621199 crossref_primary_10_3389_fgene_2021_738274 crossref_primary_10_3389_fmolb_2023_1227371 crossref_primary_10_1016_j_compbiomed_2023_107372 crossref_primary_10_1016_j_scitotenv_2022_153639 crossref_primary_10_3390_molecules30020274 crossref_primary_10_1093_bib_bbae340 crossref_primary_10_1093_bioinformatics_btae416 crossref_primary_10_1007_s00284_023_03282_4 crossref_primary_10_1016_j_eng_2023_01_014 crossref_primary_10_3389_fchem_2024_1380266 crossref_primary_10_1016_j_drudis_2022_02_023 crossref_primary_10_1038_s42256_021_00438_4 crossref_primary_10_1093_bioinformatics_btae135 crossref_primary_10_1021_acs_jcim_4c02264 crossref_primary_10_3390_foods12183386 crossref_primary_10_7554_eLife_97330_3 crossref_primary_10_2174_1574893616666211119093100 crossref_primary_10_1021_acsami_2c08891 crossref_primary_10_1016_j_ejmech_2025_117269 crossref_primary_10_1109_JBHI_2024_3402529 crossref_primary_10_3390_molecules27092980 crossref_primary_10_1093_biomet_asae010 crossref_primary_10_1093_bib_bbaf024 crossref_primary_10_2174_0118715206270568231129054853 crossref_primary_10_1093_bioinformatics_btad615 crossref_primary_10_1186_s12864_022_08648_9 crossref_primary_10_3390_biom12121754 crossref_primary_10_1016_j_ymeth_2022_02_007 crossref_primary_10_2174_0115734099282231240214095025 crossref_primary_10_3390_ph18030329 crossref_primary_10_1016_j_envint_2024_109032 crossref_primary_10_1093_bib_bbad368 crossref_primary_10_1002_cpz1_302 crossref_primary_10_1186_s12859_024_05698_6 crossref_primary_10_1093_bioinformatics_btac485 crossref_primary_10_1021_acs_jcim_4c00772 crossref_primary_10_1093_bib_bbad120 crossref_primary_10_1002_wcms_1597 crossref_primary_10_7554_eLife_68832 crossref_primary_10_1254_fpj_22072 crossref_primary_10_1093_bib_bbac427 crossref_primary_10_1021_acs_jcim_2c01008 crossref_primary_10_1007_s11063_021_10617_4 crossref_primary_10_1109_TETC_2023_3238963 crossref_primary_10_2174_1573409918666220816112206 crossref_primary_10_1007_s11030_024_10975_w |
| Cites_doi | 10.1093/nar/gkl999 10.1038/nm.4306 10.1093/bib/bbu010 10.1038/nrd.2016.230 10.1371/journal.pcbi.1007129 10.1021/acs.jcim.9b00237 10.1186/s13321-017-0209-z 10.1021/acschembio.6b00346 10.1111/tmi.13383 10.1016/j.inffus.2018.09.012 10.1038/nbt.1990 10.1093/nar/gky1033 10.1093/bioinformatics/bty593 |
| ContentType | Journal Article |
| Copyright | The Author(s) 2020. Published by Oxford University Press. 2020 The Author(s) 2020. Published by Oxford University Press. |
| Copyright_xml | – notice: The Author(s) 2020. Published by Oxford University Press. 2020 – notice: The Author(s) 2020. Published by Oxford University Press. |
| DBID | TOX AAYXX CITATION CGR CUY CVF ECM EIF NPM 7QF 7QO 7QQ 7SC 7SE 7SP 7SR 7TA 7TB 7TM 7TO 7U5 8BQ 8FD F28 FR3 H8D H8G H94 JG9 JQ2 K9. KR7 L7M L~C L~D P64 7X8 5PM |
| DOI | 10.1093/bioinformatics/btaa1005 |
| DatabaseName | Oxford Journals Open Access Collection (Oxford University Press) CrossRef Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed Aluminium Industry Abstracts Biotechnology Research Abstracts Ceramic Abstracts Computer and Information Systems Abstracts Corrosion Abstracts Electronics & Communications Abstracts Engineered Materials Abstracts Materials Business File Mechanical & Transportation Engineering Abstracts Nucleic Acids Abstracts Oncogenes and Growth Factors Abstracts Solid State and Superconductivity Abstracts METADEX Technology Research Database ANTE: Abstracts in New Technology & Engineering Engineering Research Database Aerospace Database Copper Technical Reference Library AIDS and Cancer Research Abstracts Materials Research Database ProQuest Computer Science Collection ProQuest Health & Medical Complete (Alumni) Civil Engineering Abstracts Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional Biotechnology and BioEngineering Abstracts MEDLINE - Academic PubMed Central (Full Participant titles) |
| DatabaseTitle | CrossRef MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) Materials Research Database Oncogenes and Growth Factors Abstracts Technology Research Database Computer and Information Systems Abstracts – Academic Mechanical & Transportation Engineering Abstracts Nucleic Acids Abstracts ProQuest Computer Science Collection Computer and Information Systems Abstracts ProQuest Health & Medical Complete (Alumni) Materials Business File Aerospace Database Copper Technical Reference Library Engineered Materials Abstracts Biotechnology Research Abstracts AIDS and Cancer Research Abstracts Advanced Technologies Database with Aerospace ANTE: Abstracts in New Technology & Engineering Civil Engineering Abstracts Aluminium Industry Abstracts Electronics & Communications Abstracts Ceramic Abstracts METADEX Biotechnology and BioEngineering Abstracts Computer and Information Systems Abstracts Professional Solid State and Superconductivity Abstracts Engineering Research Database Corrosion Abstracts MEDLINE - Academic |
| DatabaseTitleList | MEDLINE - Academic MEDLINE Materials Research Database |
| Database_xml | – sequence: 1 dbid: NPM name: PubMed url: https://proxy.k.utb.cz/login?url=http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 2 dbid: EIF name: MEDLINE url: https://proxy.k.utb.cz/login?url=https://www.webofscience.com/wos/medline/basic-search sourceTypes: Index Database – sequence: 3 dbid: TOX name: Oxford Journals Open Access Collection (Oxford University Press) url: https://academic.oup.com/journals/ sourceTypes: Publisher |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Biology |
| EISSN | 1367-4811 |
| EndPage | 5547 |
| ExternalDocumentID | PMC8016467 33275143 10_1093_bioinformatics_btaa1005 10.1093/bioinformatics/btaa1005 |
| Genre | Research Support, U.S. Gov't, Non-P.H.S Journal Article Research Support, N.I.H., Extramural Report |
| GrantInformation_xml | – fundername: NHLBI NIH HHS grantid: R56 HL138415 – fundername: NINDS NIH HHS grantid: R01 NS107291 – fundername: ; – fundername: ; grantid: SCH-2014438; IIS-1418511; CCF-1533768; IIS-1838042 – fundername: ; grantid: R01 1R01NS107291-01; R56HL138415 – fundername: ; grantid: IIS-2030459; IIS-2033384 |
| GroupedDBID | --- -E4 -~X .-4 .2P .DC .GJ .I3 0R~ 1TH 23N 2WC 4.4 48X 53G 5GY 5WA 70D AAIJN AAIMJ AAJKP AAJQQ AAKPC AAMDB AAMVS AAOGV AAPQZ AAPXW AAUQX AAVAP AAVLN ABEFU ABEJV ABEUO ABGNP ABIXL ABNGD ABNKS ABPQP ABPTD ABQLI ABQTQ ABWST ABXVV ABZBJ ACGFS ACIWK ACPRK ACUFI ACUKT ACUXJ ACYTK ADBBV ADEYI ADEZT ADFTL ADGKP ADGZP ADHKW ADHZD ADMLS ADOCK ADPDF ADRDM ADRTK ADVEK ADYVW ADZTZ ADZXQ AECKG AEGPL AEJOX AEKKA AEKSI AELWJ AEMDU AENEX AENZO AEPUE AETBJ AEWNT AFFNX AFFZL AFGWE AFIYH AFOFC AFRAH AGINJ AGKEF AGQXC AGSYK AHMBA AHXPO AI. AIJHB AJEEA AJEUX AKHUL AKWXX ALMA_UNASSIGNED_HOLDINGS ALTZX ALUQC AMNDL APIBT APWMN AQDSO ARIXL ASPBG ATTQO AVWKF AXUDD AYOIW AZFZN AZVOD BAWUL BAYMD BHONS BQDIO BQUQU BSWAC BTQHN C1A C45 CAG CDBKE COF CS3 CZ4 DAKXR DIK DILTD DU5 D~K EBD EBS EE~ EJD ELUNK EMOBN F5P F9B FEDTE FHSFR FLIZI FLUFQ FOEOM FQBLK GAUVT GJXCC GROUPED_DOAJ GX1 H13 H5~ HAR HVGLF HW0 HZ~ IOX J21 JXSIZ KAQDR KOP KQ8 KSI KSN M-Z M49 MK~ ML0 N9A NGC NLBLG NMDNZ NOMLY NTWIH NU- NVLIB O0~ O9- OAWHX ODMLO OJQWA OK1 OVD OVEED O~Y P2P PAFKI PB- PEELM PQQKQ Q1. Q5Y R44 RD5 RIG RNI RNS ROL RPM RUSNO RW1 RXO RZF RZO SV3 TEORI TJP TLC TOX TR2 VH1 W8F WOQ X7H YAYTL YKOAZ YXANX ZGI ZKX ~91 ~KM AAYXX CITATION ADRIX AFXEN BCRHZ CGR CUY CVF ECM EIF NPM ROX 7QF 7QO 7QQ 7SC 7SE 7SP 7SR 7TA 7TB 7TM 7TO 7U5 8BQ 8FD F28 FR3 H8D H8G H94 JG9 JQ2 K9. KR7 L7M L~C L~D P64 7X8 5PM |
| ID | FETCH-LOGICAL-c554t-597f8ff6608a9c966917c71567ef26f40d003f0378a2c6c746ba731d4571a1ed3 |
| IEDL.DBID | TOX |
| ISSN | 1367-4803 1367-4811 |
| IngestDate | Thu Aug 21 18:16:40 EDT 2025 Fri Jul 11 10:06:14 EDT 2025 Mon Jun 30 10:41:31 EDT 2025 Wed Feb 19 02:28:39 EST 2025 Tue Jul 01 02:33:54 EDT 2025 Thu Apr 24 23:03:25 EDT 2025 Wed Apr 02 07:04:07 EDT 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 22-23 |
| Language | English |
| License | This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. http://creativecommons.org/licenses/by/4.0 The Author(s) 2020. Published by Oxford University Press. |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c554t-597f8ff6608a9c966917c71567ef26f40d003f0378a2c6c746ba731d4571a1ed3 |
| Notes | SourceType-Scholarly Journals-1 content type line 14 ObjectType-Report-1 ObjectType-Article-1 ObjectType-Feature-2 content type line 23 |
| ORCID | 0000-0001-6693-8390 |
| OpenAccessLink | https://dx.doi.org/10.1093/bioinformatics/btaa1005 |
| PMID | 33275143 |
| PQID | 3128013932 |
| PQPubID | 36124 |
| PageCount | 3 |
| ParticipantIDs | pubmedcentral_primary_oai_pubmedcentral_nih_gov_8016467 proquest_miscellaneous_2467615730 proquest_journals_3128013932 pubmed_primary_33275143 crossref_citationtrail_10_1093_bioinformatics_btaa1005 crossref_primary_10_1093_bioinformatics_btaa1005 oup_primary_10_1093_bioinformatics_btaa1005 |
| ProviderPackageCode | CITATION AAYXX |
| PublicationCentury | 2000 |
| PublicationDate | 2020-12-01 |
| PublicationDateYYYYMMDD | 2020-12-01 |
| PublicationDate_xml | – month: 12 year: 2020 text: 2020-12-01 day: 01 |
| PublicationDecade | 2020 |
| PublicationPlace | England |
| PublicationPlace_xml | – name: England – name: Oxford |
| PublicationTitle | Bioinformatics (Oxford, England) |
| PublicationTitleAlternate | Bioinformatics |
| PublicationYear | 2020 |
| Publisher | Oxford University Press Oxford Publishing Limited (England) |
| Publisher_xml | – name: Oxford University Press – name: Oxford Publishing Limited (England) |
| References | Yang (2023062707100304900_btaa1005-B15) 2019; 59 Kim (2023062707100304900_btaa1005-B6) 2019; 47 Rutkowska (2023062707100304900_btaa1005-B12) 2016; 11 Corsello (2023062707100304900_btaa1005-B3) 2017; 23 Santos (2023062707100304900_btaa1005-B13) 2017; 16 Zitnik (2023062707100304900_btaa1005-B16) 2019; 50 Lee (2023062707100304900_btaa1005-B7) 2019; 15 Abid (2023062707100304900_btaa1005-B1) 2019 Pahikkala (2023062707100304900_btaa1005-B11) 2015; 16 Cho (2023062707100304900_btaa1005-B2) 2014 Liu (2023062707100304900_btaa1005-B8) 2007; 35 Öztürk (2023062707100304900_btaa1005-B10) 2018; 34 Nguyen (2023062707100304900_btaa1005-B9) 2020 Davis (2023062707100304900_btaa1005-B4) 2011; 29 He (2023062707100304900_btaa1005-B5) 2017; 9 Velavan (2023062707100304900_btaa1005-B14) 2020; 25 |
| References_xml | – volume: 35 start-page: D198 year: 2007 ident: 2023062707100304900_btaa1005-B8 article-title: BindingDB: a web-accessible database of experimentally determined protein–ligand binding affinities publication-title: Nucleic Acids Res doi: 10.1093/nar/gkl999 – volume: 23 start-page: 405 year: 2017 ident: 2023062707100304900_btaa1005-B3 article-title: The drug repurposing hub: a next-generation drug library and information resource publication-title: Nat. Med doi: 10.1038/nm.4306 – year: 2019 ident: 2023062707100304900_btaa1005-B1 – volume: 16 start-page: 325 year: 2015 ident: 2023062707100304900_btaa1005-B11 article-title: Toward more realistic drug–target interaction predictions publication-title: Brief. Bioinf doi: 10.1093/bib/bbu010 – volume: 16 start-page: 19 year: 2017 ident: 2023062707100304900_btaa1005-B13 article-title: A comprehensive map of molecular drug targets publication-title: Nat. Rev. Drug Discov doi: 10.1038/nrd.2016.230 – volume: 15 start-page: e1007129 year: 2019 ident: 2023062707100304900_btaa1005-B7 article-title: DeepConv-DTI: prediction of drug–target interactions via deep learning with convolution on protein sequences publication-title: PLOS Comput. Biol doi: 10.1371/journal.pcbi.1007129 – start-page: 103 year: 2014 ident: 2023062707100304900_btaa1005-B2 – volume: 59 start-page: 3370 year: 2019 ident: 2023062707100304900_btaa1005-B15 article-title: Analyzing learned molecular representations for property prediction publication-title: J. Chem. Inf. Model doi: 10.1021/acs.jcim.9b00237 – volume: 9 start-page: 24 year: 2017 ident: 2023062707100304900_btaa1005-B5 article-title: SimBoost: a read-across approach for predicting drug–target binding affinities using gradient boosting machines publication-title: J. Cheminf doi: 10.1186/s13321-017-0209-z – year: 2020 ident: 2023062707100304900_btaa1005-B9 – volume: 11 start-page: 2541 year: 2016 ident: 2023062707100304900_btaa1005-B12 article-title: A modular probe strategy for drug localization, target identification and target occupancy measurement on single cell level publication-title: ACS Chem. Biol doi: 10.1021/acschembio.6b00346 – volume: 25 start-page: 278 year: 2020 ident: 2023062707100304900_btaa1005-B14 article-title: The COVID19 epidemic publication-title: Trop. Med. Int. Health doi: 10.1111/tmi.13383 – volume: 50 start-page: 71 year: 2019 ident: 2023062707100304900_btaa1005-B16 article-title: Machine learning for integrating data in biology and medicine: principles, practice, and opportunities publication-title: Inf. Fus doi: 10.1016/j.inffus.2018.09.012 – volume: 29 start-page: 1046 year: 2011 ident: 2023062707100304900_btaa1005-B4 article-title: Comprehensive analysis of kinase inhibitor selectivity publication-title: Nat. Biotechnol doi: 10.1038/nbt.1990 – volume: 47 start-page: D1102 year: 2019 ident: 2023062707100304900_btaa1005-B6 article-title: Pubchem 2019 update: improved access to chemical data publication-title: Nucleic Acids Res doi: 10.1093/nar/gky1033 – volume: 34 start-page: i821 year: 2018 ident: 2023062707100304900_btaa1005-B10 article-title: DeepDTA: deep drug–target binding affinity prediction publication-title: Bioinformatics doi: 10.1093/bioinformatics/bty593 |
| SSID | ssj0005056 |
| Score | 2.6862292 |
| Snippet | Abstract
Summary
Accurate prediction of drug–target interactions (DTI) is crucial for drug discovery. Recently, deep learning (DL) models for show promising... Accurate prediction of drug-target interactions (DTI) is crucial for drug discovery. Recently, deep learning (DL) models for show promising performance for DTI... Accurate prediction of drug–target interactions (DTI) is crucial for drug discovery. Recently, deep learning (DL) models for show promising performance for DTI... |
| SourceID | pubmedcentral proquest pubmed crossref oup |
| SourceType | Open Access Repository Aggregation Database Index Database Enrichment Source Publisher |
| StartPage | 5545 |
| SubjectTerms | Applications Notes Availability Bioinformatics Deep Learning Drug Development Drug Discovery Pharmaceutical Preparations Prediction models Proteins |
| Title | DeepPurpose: a deep learning library for drug–target interaction prediction |
| URI | https://www.ncbi.nlm.nih.gov/pubmed/33275143 https://www.proquest.com/docview/3128013932 https://www.proquest.com/docview/2467615730 https://pubmed.ncbi.nlm.nih.gov/PMC8016467 |
| Volume | 36 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1LS8QwEB5kQfAivq2uEsGblO0m2ST1JuqyCD4OCnsraZquC9Jd9nHw5n_wH_pLnPSx2gVRby1J-phJOt90Jt8AnPKQxq6aJLqpXPqcUevHllmfo-lOQ2M6yuYJsnei98Rv-p3-CtBqL8xyCD9krXg4KklEHXFxK55p3S5oS9EUO7r8x_v-V1ZHkBdsdURkPlcBq1K6fr5OzSDVNrl9w5rLKZPfbFB3A9ZL8EguCm1vworNtmC1KCf5ug23V9aOH1Byo6k9J5okeErKshADUv6vIfhsJJnMBx9v70UaOHGUEZNigwMZT1zkxh3uwFP3-vGy55flEnyDmGDmo2uQqjQVIlA6NOjGoCdmJPpn0qZUpDxIcAWnAZNKUyOM5CLWkrUT3pFt3bYJ24VGNsrsPhCpRWBpohEMWK5CpXSqhAl44siqjEw8EJXMIlNyibuSFi9REdNmUV3YUSVsD4LFwHFBp_H7kDNUyt97NyvlReVqnEYMjbCDuox6cLJoxnXkgiM6s6P5NKJoMRDd4QfPg71C14t7MkalA5YeyNosWHRwHN31lmz4nHN1q5zATR786yUOYY06tz7PmmlCYzaZ2yPEPrP4OJ_tn-5sCKs |
| linkProvider | Oxford University Press |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=DeepPurpose%3A+a+deep+learning+library+for+drug%E2%80%93target+interaction+prediction&rft.jtitle=Bioinformatics+%28Oxford%2C+England%29&rft.au=Huang%2C+Kexin&rft.au=Fu%2C+Tianfan&rft.au=Glass%2C+Lucas+M&rft.au=Zitnik%2C+Marinka&rft.date=2020-12-01&rft.pub=Oxford+University+Press&rft.issn=1367-4803&rft.eissn=1367-4811&rft.volume=36&rft.issue=22-23&rft.spage=5545&rft.epage=5547&rft_id=info:doi/10.1093%2Fbioinformatics%2Fbtaa1005&rft_id=info%3Apmid%2F33275143&rft.externalDocID=PMC8016467 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1367-4803&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1367-4803&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1367-4803&client=summon |