MolAICal: a soft tool for 3D drug design of protein targets by artificial intelligence and classical algorithm
Deep learning is an important branch of artificial intelligence that has been successfully applied into medicine and two-dimensional ligand design. The three-dimensional (3D) ligand generation in the 3D pocket of protein target is an interesting and challenging issue for drug design by deep learning...
Saved in:
| Published in | Briefings in bioinformatics Vol. 22; no. 3 |
|---|---|
| Main Authors | , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Oxford University Press
20.05.2021
|
| Subjects | |
| Online Access | Get full text |
Cover
Loading…
| Abstract | Deep learning is an important branch of artificial intelligence that has been successfully applied into medicine and two-dimensional ligand design. The three-dimensional (3D) ligand generation in the 3D pocket of protein target is an interesting and challenging issue for drug design by deep learning. Here, the MolAICal software is introduced to supply a way for generating 3D drugs in the 3D pocket of protein targets by combining with merits of deep learning model and classical algorithm. The MolAICal software mainly contains two modules for 3D drug design. In the first module of MolAICal, it employs the genetic algorithm, deep learning model trained by FDA-approved drug fragments and Vinardo score fitting on the basis of PDBbind database for drug design. In the second module, it uses deep learning generative model trained by drug-like molecules of ZINC database and molecular docking invoked by Autodock Vina automatically. Besides, the Lipinski’s rule of five, Pan-assay interference compounds (PAINS), synthetic accessibility (SA) and other user-defined rules are introduced for filtering out unwanted ligands in MolAICal. To show the drug design modules of MolAICal, the membrane protein glucagon receptor and non-membrane protein SARS-CoV-2 main protease are chosen as the investigative drug targets. The results show MolAICal can generate the various and novel ligands with good binding scores and appropriate XLOGP values. We believe that MolAICal can use the advantages of deep learning model and classical programming for designing 3D drugs in protein pocket. MolAICal is freely for any nonprofit purpose and accessible at https://molaical.github.io. |
|---|---|
| AbstractList | Deep learning is an important branch of artificial intelligence that has been successfully applied into medicine and two-dimensional ligand design. The three-dimensional (3D) ligand generation in the 3D pocket of protein target is an interesting and challenging issue for drug design by deep learning. Here, the MolAICal software is introduced to supply a way for generating 3D drugs in the 3D pocket of protein targets by combining with merits of deep learning model and classical algorithm. The MolAICal software mainly contains two modules for 3D drug design. In the first module of MolAICal, it employs the genetic algorithm, deep learning model trained by FDA-approved drug fragments and Vinardo score fitting on the basis of PDBbind database for drug design. In the second module, it uses deep learning generative model trained by drug-like molecules of ZINC database and molecular docking invoked by Autodock Vina automatically. Besides, the Lipinski’s rule of five, Pan-assay interference compounds (PAINS), synthetic accessibility (SA) and other user-defined rules are introduced for filtering out unwanted ligands in MolAICal. To show the drug design modules of MolAICal, the membrane protein glucagon receptor and non-membrane protein SARS-CoV-2 main protease are chosen as the investigative drug targets. The results show MolAICal can generate the various and novel ligands with good binding scores and appropriate XLOGP values. We believe that MolAICal can use the advantages of deep learning model and classical programming for designing 3D drugs in protein pocket. MolAICal is freely for any nonprofit purpose and accessible at
https://molaical.github.io
. Deep learning is an important branch of artificial intelligence that has been successfully applied into medicine and two-dimensional ligand design. The three-dimensional (3D) ligand generation in the 3D pocket of protein target is an interesting and challenging issue for drug design by deep learning. Here, the MolAICal software is introduced to supply a way for generating 3D drugs in the 3D pocket of protein targets by combining with merits of deep learning model and classical algorithm. The MolAICal software mainly contains two modules for 3D drug design. In the first module of MolAICal, it employs the genetic algorithm, deep learning model trained by FDA-approved drug fragments and Vinardo score fitting on the basis of PDBbind database for drug design. In the second module, it uses deep learning generative model trained by drug-like molecules of ZINC database and molecular docking invoked by Autodock Vina automatically. Besides, the Lipinski’s rule of five, Pan-assay interference compounds (PAINS), synthetic accessibility (SA) and other user-defined rules are introduced for filtering out unwanted ligands in MolAICal. To show the drug design modules of MolAICal, the membrane protein glucagon receptor and non-membrane protein SARS-CoV-2 main protease are chosen as the investigative drug targets. The results show MolAICal can generate the various and novel ligands with good binding scores and appropriate XLOGP values. We believe that MolAICal can use the advantages of deep learning model and classical programming for designing 3D drugs in protein pocket. MolAICal is freely for any nonprofit purpose and accessible at https://molaical.github.io. Deep learning is an important branch of artificial intelligence that has been successfully applied into medicine and two-dimensional ligand design. The three-dimensional (3D) ligand generation in the 3D pocket of protein target is an interesting and challenging issue for drug design by deep learning. Here, the MolAICal software is introduced to supply a way for generating 3D drugs in the 3D pocket of protein targets by combining with merits of deep learning model and classical algorithm. The MolAICal software mainly contains two modules for 3D drug design. In the first module of MolAICal, it employs the genetic algorithm, deep learning model trained by FDA-approved drug fragments and Vinardo score fitting on the basis of PDBbind database for drug design. In the second module, it uses deep learning generative model trained by drug-like molecules of ZINC database and molecular docking invoked by Autodock Vina automatically. Besides, the Lipinski's rule of five, Pan-assay interference compounds (PAINS), synthetic accessibility (SA) and other user-defined rules are introduced for filtering out unwanted ligands in MolAICal. To show the drug design modules of MolAICal, the membrane protein glucagon receptor and non-membrane protein SARS-CoV-2 main protease are chosen as the investigative drug targets. The results show MolAICal can generate the various and novel ligands with good binding scores and appropriate XLOGP values. We believe that MolAICal can use the advantages of deep learning model and classical programming for designing 3D drugs in protein pocket. MolAICal is freely for any nonprofit purpose and accessible at https://molaical.github.io.Deep learning is an important branch of artificial intelligence that has been successfully applied into medicine and two-dimensional ligand design. The three-dimensional (3D) ligand generation in the 3D pocket of protein target is an interesting and challenging issue for drug design by deep learning. Here, the MolAICal software is introduced to supply a way for generating 3D drugs in the 3D pocket of protein targets by combining with merits of deep learning model and classical algorithm. The MolAICal software mainly contains two modules for 3D drug design. In the first module of MolAICal, it employs the genetic algorithm, deep learning model trained by FDA-approved drug fragments and Vinardo score fitting on the basis of PDBbind database for drug design. In the second module, it uses deep learning generative model trained by drug-like molecules of ZINC database and molecular docking invoked by Autodock Vina automatically. Besides, the Lipinski's rule of five, Pan-assay interference compounds (PAINS), synthetic accessibility (SA) and other user-defined rules are introduced for filtering out unwanted ligands in MolAICal. To show the drug design modules of MolAICal, the membrane protein glucagon receptor and non-membrane protein SARS-CoV-2 main protease are chosen as the investigative drug targets. The results show MolAICal can generate the various and novel ligands with good binding scores and appropriate XLOGP values. We believe that MolAICal can use the advantages of deep learning model and classical programming for designing 3D drugs in protein pocket. MolAICal is freely for any nonprofit purpose and accessible at https://molaical.github.io. |
| Author | Bai, Qifeng Huang, Junzhou Tan, Shuoyan Liu, Huanxiang Xu, Tingyang Yao, Xiaojun |
| AuthorAffiliation | 2 University of Connecticut 3 Rutgers University 1 Lanzhou University |
| AuthorAffiliation_xml | – name: 2 University of Connecticut – name: 3 Rutgers University – name: 1 Lanzhou University |
| Author_xml | – sequence: 1 givenname: Qifeng orcidid: 0000-0001-7296-6187 surname: Bai fullname: Bai, Qifeng organization: Lanzhou University – sequence: 2 givenname: Shuoyan surname: Tan fullname: Tan, Shuoyan organization: Lanzhou University – sequence: 3 givenname: Tingyang surname: Xu fullname: Xu, Tingyang organization: University of Connecticut – sequence: 4 givenname: Huanxiang surname: Liu fullname: Liu, Huanxiang organization: Lanzhou University – sequence: 5 givenname: Junzhou surname: Huang fullname: Huang, Junzhou organization: Rutgers University – sequence: 6 givenname: Xiaojun surname: Yao fullname: Yao, Xiaojun organization: Lanzhou University |
| BookMark | eNptkctqHDEQRUWwiV9Z5Qe0NISOpdazswiYsZMYbLKJ16JaUrdlNNJY0hj89-7B40VCVlVQ596q4p6gg5STR-gzJV8pGdjFGMaLcQSgkn5Ax5Qr1XEi-MGul6oTXLIjdFLrIyE9UZp-REesV0rrgR6jdJfj5c0K4jcMuOap4ZZzxFMumF1hV7Yzdr6GOeE84U3JzYeEG5TZt4rHFwylhSnYABGH1HyMYfbJegzJYRuh1mCXEcQ5l9Ae1mfocIJY_ad9PUX3P67_rH51t79_3qwubzvLtGwdcz3jXDgOykoYwQ-69wqAce0Ed1qPoAjRTjHPeu6EkwNbYOKl7LUQAztF3998N9tx7Z31qRWIZlPCGsqLyRDM35MUHsycn43igvdKLAbne4OSn7a-NrMO1S7_QfJ5W03P2bJJMqIXlL6htuRai5-MDQ1ayDvnEA0lZpeTWXIy-5wWzZd_NO-n_Y9-BSORmAk |
| CitedBy_id | crossref_primary_10_1021_acs_jmedchem_2c00732 crossref_primary_10_3389_fchem_2022_991369 crossref_primary_10_1002_cbdv_202301771 crossref_primary_10_22159_ajpcr_2023_v16i11_48193 crossref_primary_10_1080_07391102_2023_2183043 crossref_primary_10_33084_bjop_v6i3_4915 crossref_primary_10_1016_j_jafr_2023_100783 crossref_primary_10_1021_acs_jpcb_4c03845 crossref_primary_10_1109_TCBB_2024_3492708 crossref_primary_10_1080_07391102_2023_2167116 crossref_primary_10_1111_jcmm_70014 crossref_primary_10_1186_s13321_025_00976_8 crossref_primary_10_1021_acs_jmedchem_4c01044 crossref_primary_10_1177_11779322231167970 crossref_primary_10_1021_acs_jmedchem_1c00927 crossref_primary_10_1111_cbdd_14639 crossref_primary_10_3389_fphar_2022_843043 crossref_primary_10_1007_s42250_023_00684_6 crossref_primary_10_1111_jfbc_14338 crossref_primary_10_1080_07391102_2022_2163702 crossref_primary_10_1016_j_ijbiomac_2024_130730 crossref_primary_10_1093_bib_bbab484 crossref_primary_10_1021_acs_jcim_3c01456 crossref_primary_10_1080_07391102_2022_2148128 crossref_primary_10_1007_s44371_024_00025_7 crossref_primary_10_3233_JAD_240413 crossref_primary_10_1098_rsos_220369 crossref_primary_10_1177_11769351221118556 crossref_primary_10_1016_j_biopha_2024_116459 crossref_primary_10_1080_17460441_2023_2174522 crossref_primary_10_1002_minf_202200188 crossref_primary_10_1039_D2NJ03253H crossref_primary_10_1007_s40203_024_00300_6 crossref_primary_10_1007_s11030_021_10217_3 crossref_primary_10_1038_s42256_022_00494_4 crossref_primary_10_1007_s11030_022_10489_3 crossref_primary_10_1093_bib_bbaa422 crossref_primary_10_3390_biomedinformatics4010030 crossref_primary_10_3390_molecules27072281 crossref_primary_10_1002_adma_202211485 crossref_primary_10_3390_microorganisms13020237 crossref_primary_10_3390_vaccines10101734 crossref_primary_10_1007_s00894_023_05492_w crossref_primary_10_33084_bjop_v6i1_3876 crossref_primary_10_3390_ijms23095054 crossref_primary_10_1002_slct_202403533 crossref_primary_10_3390_sym14010016 crossref_primary_10_1016_j_apmt_2024_102068 crossref_primary_10_1016_j_biopha_2022_113350 crossref_primary_10_1093_bib_bbac520 crossref_primary_10_1007_s11030_023_10659_x crossref_primary_10_1021_acs_jcim_3c01964 crossref_primary_10_3390_ijms24129849 crossref_primary_10_3390_math11061279 crossref_primary_10_1111_cbdd_14302 crossref_primary_10_1007_s00894_022_05434_y crossref_primary_10_1093_bib_bbac518 crossref_primary_10_1111_cns_14260 crossref_primary_10_1002_wcms_1568 crossref_primary_10_1016_j_csbj_2022_05_057 crossref_primary_10_1093_bib_bbab544 crossref_primary_10_1007_s11030_024_10968_9 crossref_primary_10_1038_s41598_023_45185_1 crossref_primary_10_1080_07391102_2022_2070282 crossref_primary_10_3390_foods11244046 crossref_primary_10_1177_11769351221127862 crossref_primary_10_1186_s13321_021_00563_7 crossref_primary_10_3390_nu15153489 crossref_primary_10_1002_cbdv_202301400 crossref_primary_10_1080_07391102_2024_2329302 crossref_primary_10_3390_ph14121277 crossref_primary_10_1080_07391102_2022_2133010 crossref_primary_10_1007_s40203_022_00136_y crossref_primary_10_35118_apjmbb_2024_032_4_03 crossref_primary_10_1016_j_drudis_2024_104024 crossref_primary_10_1021_acs_jcim_0c01079 crossref_primary_10_3389_frai_2024_1372161 crossref_primary_10_3390_molecules26227061 crossref_primary_10_1039_D2CP01453J crossref_primary_10_1093_bib_bbac592 crossref_primary_10_1080_07391102_2022_2112619 crossref_primary_10_1093_bib_bbac628 crossref_primary_10_3389_fphar_2022_955648 crossref_primary_10_3389_fmicb_2021_739684 crossref_primary_10_1186_s13321_022_00598_4 crossref_primary_10_1007_s12668_024_01710_8 crossref_primary_10_1021_acs_jmedchem_3c00627 crossref_primary_10_1142_S0219519422500725 crossref_primary_10_2174_1871526523666230816151614 crossref_primary_10_1080_07391102_2023_2237594 crossref_primary_10_3390_ijms22041676 crossref_primary_10_1007_s42514_021_00086_5 crossref_primary_10_3389_fphar_2024_1331062 crossref_primary_10_1002_arch_22025 crossref_primary_10_1002_slct_202203932 crossref_primary_10_1016_j_sbi_2021_10_001 crossref_primary_10_3390_jpm11090926 crossref_primary_10_1007_s40203_023_00142_8 crossref_primary_10_3390_molecules26040837 crossref_primary_10_1016_j_sbi_2021_102326 crossref_primary_10_3390_ph17070880 crossref_primary_10_1007_s13205_023_03695_9 crossref_primary_10_1002_cbdv_202300804 crossref_primary_10_1016_j_aichem_2023_100038 crossref_primary_10_1002_wcms_1581 crossref_primary_10_3390_ijms23094738 crossref_primary_10_1007_s43440_021_00282_8 crossref_primary_10_3390_magnetochemistry9070171 crossref_primary_10_4155_fmc_2021_0243 crossref_primary_10_1016_j_abb_2024_109998 crossref_primary_10_1016_j_foodchem_2021_131807 crossref_primary_10_1016_j_bbrc_2025_151638 crossref_primary_10_35118_apjmbb_2024_032_4_22 crossref_primary_10_3390_molecules26030737 crossref_primary_10_1186_s12859_024_05904_5 crossref_primary_10_1016_j_csbj_2022_09_002 crossref_primary_10_2174_1574893618666230227105703 crossref_primary_10_1360_SSC_2022_0135 crossref_primary_10_1016_j_ejmech_2024_116227 crossref_primary_10_1177_14604582241307839 crossref_primary_10_1080_07391102_2022_2141883 crossref_primary_10_1021_acs_jcim_4c01451 crossref_primary_10_1007_s43994_024_00132_2 crossref_primary_10_1016_j_iswa_2025_200508 crossref_primary_10_1093_nar_gkad414 crossref_primary_10_1021_acs_jcim_4c00247 crossref_primary_10_1080_07391102_2024_2329293 crossref_primary_10_1080_07391102_2023_2232041 crossref_primary_10_1021_acs_jcim_3c00713 crossref_primary_10_1093_bib_bbaa260 crossref_primary_10_1002_wcms_1641 crossref_primary_10_1093_bib_bbaa416 crossref_primary_10_33435_tcandtc_1196019 crossref_primary_10_3389_fchem_2020_597495 crossref_primary_10_1017_qrd_2022_12 crossref_primary_10_1093_bib_bbac559 crossref_primary_10_3390_ijms23063261 crossref_primary_10_1016_j_bioorg_2023_106894 crossref_primary_10_1360_SSC_2024_0150 crossref_primary_10_1016_j_chom_2024_02_011 crossref_primary_10_1080_13880209_2022_2063341 crossref_primary_10_3390_ph16070971 crossref_primary_10_1021_acsomega_3c05863 crossref_primary_10_1016_j_compbiomed_2024_108376 crossref_primary_10_3389_fgene_2024_1450529 crossref_primary_10_1039_D4CP03521F crossref_primary_10_1007_s11164_023_05035_1 crossref_primary_10_1186_s13321_022_00665_w crossref_primary_10_1080_07391102_2023_2213777 crossref_primary_10_1016_j_ipha_2024_02_008 crossref_primary_10_3934_mbe_2023012 crossref_primary_10_1016_j_arabjc_2024_105656 crossref_primary_10_1016_j_scitotenv_2023_165059 crossref_primary_10_1038_s42003_025_07840_3 crossref_primary_10_1016_j_ipha_2023_11_008 crossref_primary_10_1007_s11356_021_16223_0 crossref_primary_10_1007_s42250_024_00976_5 crossref_primary_10_3934_mbe_2023419 crossref_primary_10_1016_j_csbj_2021_06_017 crossref_primary_10_2147_DDDT_S330947 crossref_primary_10_1186_s13321_023_00702_2 crossref_primary_10_3389_frhem_2024_1305741 crossref_primary_10_1039_D2DD00115B crossref_primary_10_1007_s12033_023_00831_x |
| Cites_doi | 10.1021/ci100275a 10.4155/fmc-2018-0358 10.1039/C6CP01555G 10.1038/scientificamerican0992-144 10.1021/ci100350u 10.1021/acs.jcim.9b00445 10.1021/jm030580l 10.1038/s41586-020-2223-y 10.1016/S0169-409X(00)00129-0 10.1093/bib/bbz156 10.1021/acs.jmedchem.8b00686 10.1021/jm0303195 10.2174/15680266113139990114 10.1038/s41592-019-0403-1 10.1109/5254.708428 10.1002/prot.25892 10.1023/A:1007465528199 10.1007/s0089400060498 10.1093/bioinformatics/btaa645 10.1093/bib/bbz087 10.1371/journal.pone.0155183 10.3389/fmolb.2017.00087 10.1145/175247.175256 10.1021/ci990010n 10.1002/jcc.20289 10.3389/fenvs.2015.00080 10.1038/srep42717 10.1002/jcc.20681 10.1038/srep24817 10.1021/acscentsci.7b00572 10.3389/fchem.2019.00851 10.1021/acscentsci.7b00512 10.1021/ct300418h 10.18632/oncotarget.14073 10.1021/acs.chemrev.8b00728 10.1021/acs.jmedchem.5b00886 10.1002/minf.201600118 10.1038/s41598-019-44773-4 10.1111/cbdd.12095 10.1016/j.drudis.2018.01.039 10.1038/nature17414 10.1021/ci3001277 10.1021/acs.jcim.7b00049 10.1021/acsmedchemlett.7b00462 10.1038/sdata.2014.22 10.1016/j.compbiolchem.2017.05.007 10.1002/jcc.21256 10.1002/jcc.21334 10.1016/j.cell.2018.02.010 10.1021/ci030340e 10.1016/j.ces.2016.02.037 10.1038/nature25978 10.1038/srep40053 10.1186/1758-2946-3-33 10.1126/science.abb3405 |
| ContentType | Journal Article |
| Copyright | The Author(s) 2020. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com. The Author(s) 2020. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com 2020 |
| Copyright_xml | – notice: The Author(s) 2020. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com. – notice: The Author(s) 2020. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com 2020 |
| DBID | AAYXX CITATION 7X8 5PM |
| DOI | 10.1093/bib/bbaa161 |
| DatabaseName | CrossRef MEDLINE - Academic PubMed Central (Full Participant titles) |
| DatabaseTitle | CrossRef MEDLINE - Academic |
| DatabaseTitleList | CrossRef MEDLINE - Academic |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Biology |
| EISSN | 1477-4054 |
| ExternalDocumentID | PMC7454275 10_1093_bib_bbaa161 |
| GrantInformation_xml | – fundername: ; grantid: 21605066; 21775060 – fundername: ; grantid: JR202004 |
| GroupedDBID | --- -E4 .2P .I3 0R~ 23N 2WC 36B 4.4 48X 53G 5GY 5VS 6J9 70D 8VB AAHBH AAIJN AAIMJ AAJKP AAMDB AAMVS AAOGV AAPQZ AAPXW AARHZ AAVAP AAVLN AAYXX ABDBF ABEJV ABEUO ABGNP ABIXL ABNKS ABPQP ABPTD ABQLI ABWST ABXVV ABXZS ABZBJ ACGFO ACGFS ACGOD ACIWK ACPRK ACUFI ACUHS ACUXJ ACYTK ADBBV ADEYI ADFTL ADGKP ADGZP ADHKW ADHZD ADOCK ADPDF ADQBN ADRDM ADRTK ADVEK ADYVW ADZTZ ADZXQ AECKG AEGPL AEGXH AEJOX AEKKA AEKSI AELWJ AEMDU AEMOZ AENEX AENZO AEPUE AETBJ AEWNT AFFZL AFGWE AFIYH AFOFC AFRAH AGINJ AGKEF AGQXC AGSYK AHGBF AHMBA AHQJS AHXPO AIAGR AIJHB AJEEA AJEUX AKHUL AKVCP AKWXX ALMA_UNASSIGNED_HOLDINGS ALTZX ALUQC ALXQX AMNDL ANAKG APIBT APWMN ARIXL AXUDD AYOIW AZVOD BAWUL BAYMD BEYMZ BHONS BQDIO BQUQU BSWAC BTQHN C45 CDBKE CITATION CS3 CZ4 DAKXR DIK DILTD DU5 D~K E3Z EAD EAP EAS EBA EBC EBD EBR EBS EBU EE~ EMB EMK EMOBN EST ESX F5P F9B FHSFR FLIZI FLUFQ FOEOM FQBLK GAUVT GJXCC GX1 H13 H5~ HAR HW0 HZ~ IOX J21 JXSIZ K1G KBUDW KOP KSI KSN M-Z MK~ ML0 N9A NGC NLBLG NMDNZ NOMLY O9- OAWHX ODMLO OJQWA OK1 OVD OVEED P2P PAFKI PEELM PQQKQ Q1. Q5Y QWB RD5 RPM RUSNO RW1 RXO SV3 TEORI TH9 TJP TLC TOX TR2 TUS W8F WOQ X7H YAYTL YKOAZ YXANX ZKX ZL0 ~91 7X8 5PM AAUQX |
| ID | FETCH-LOGICAL-c386t-3d23445d4a7c6abae982e7aa348d54d88ba7008d73e324d5d693d4a0e66285593 |
| ISSN | 1467-5463 1477-4054 |
| IngestDate | Thu Aug 21 14:32:08 EDT 2025 Fri Jul 11 01:58:34 EDT 2025 Thu Apr 24 22:53:35 EDT 2025 Tue Jul 01 03:39:30 EDT 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 3 |
| Language | English |
| License | http://creativecommons.org/licenses/by-nc/4.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-c386t-3d23445d4a7c6abae982e7aa348d54d88ba7008d73e324d5d693d4a0e66285593 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
| ORCID | 0000-0001-7296-6187 |
| OpenAccessLink | https://pubmed.ncbi.nlm.nih.gov/PMC7454275 |
| PMID | 32778891 |
| PQID | 2432856308 |
| PQPubID | 23479 |
| ParticipantIDs | pubmedcentral_primary_oai_pubmedcentral_nih_gov_7454275 proquest_miscellaneous_2432856308 crossref_citationtrail_10_1093_bib_bbaa161 crossref_primary_10_1093_bib_bbaa161 |
| ProviderPackageCode | CITATION AAYXX |
| PublicationCentury | 2000 |
| PublicationDate | 20210520 |
| PublicationDateYYYYMMDD | 2021-05-20 |
| PublicationDate_xml | – month: 05 year: 2021 text: 20210520 day: 20 |
| PublicationDecade | 2020 |
| PublicationTitle | Briefings in bioinformatics |
| PublicationYear | 2021 |
| Publisher | Oxford University Press |
| Publisher_xml | – name: Oxford University Press |
| References | Dahl (2021052110393062300_ref19) 2014 Yang (2021052110393062300_ref36) 2019; 119 Ho (2021052110393062300_ref8) 1995 Fang (2021052110393062300_ref13) 2017; 69 O'Boyle (2021052110393062300_ref46) 2011; 3 Miller (2021052110393062300_ref60) 2012; 8 Sun (2021052110393062300_ref38) 2016; 6 Cheron (2021052110393062300_ref41) 2016; 59 Hinton (2021052110393062300_ref11) 1992; 267 Hearst (2021052110393062300_ref7) 1998; 13 Goodfellow (2021052110393062300_ref29) 2014 Rumelhart (2021052110393062300_ref10) 1994; 37 Phillips (2021052110393062300_ref57) 2005; 26 Arjovsky (2021052110393062300_ref35) 2017 Shen (2021052110393062300_ref26) 2020; 10 Jin (2021052110393062300_ref63) 2020; 582 Makhzani (2021052110393062300_ref32) 2015 Wang (2021052110393062300_ref59) 2017; 4 Miao (2021052110393062300_ref4) 2019; 9 Leong (2021052110393062300_ref23) 2017; 7 Ramakrishnan (2021052110393062300_ref51) 2014; 1 Murcia-Soler (2021052110393062300_ref17) 2004; 44 Friedman (2021052110393062300_ref9) 1997; 29 Kadurin (2021052110393062300_ref33) 2017; 8 Shen (2021052110393062300_ref53) 2020; 21 Winkler (2021052110393062300_ref21) 2017; 36 Douguet (2021052110393062300_ref49) 2018; 9 Zhang (2021052110393062300_ref64) 2020; 368 Zhang (2021052110393062300_ref27) 2019 Shi (2021052110393062300_ref5) 2019 Ballester (2021052110393062300_ref65) 2007; 28 Morris (2021052110393062300_ref66) 2009; 30 Yuan (2021052110393062300_ref39) 2011; 51 Wang (2021052110393062300_ref44) 2004; 47 Chen (2021052110393062300_ref22) 2018; 23 Xia (2021052110393062300_ref16) 2004; 47 Mayr (2021052110393062300_ref20) 2016; 3 Xu (2021052110393062300_ref25) 2019; 11 Shultz (2021052110393062300_ref56) 2019; 62 Wang (2021052110393062300_ref40) 2000; 6 Li (2021052110393062300_ref14) 2017; 57 Quiroga (2021052110393062300_ref52) 2016; 11 Tenorio-Borroto (2021052110393062300_ref18) 2013; 13 Trott (2021052110393062300_ref42) 2010; 31 Hou (2021052110393062300_ref58) 2011; 51 Xue (2021052110393062300_ref30) 2019; 9 Kong (2021052110393062300_ref45) 2020 Renault (2021052110393062300_ref15) 2013; 81 Gomez-Bombarelli (2021052110393062300_ref31) 2018; 4 Hou (2021052110393062300_ref6) 1999; 39 Kermany (2021052110393062300_ref1) 2018; 172 Wang (2021052110393062300_ref43) 2016; 18 Chen (2021052110393062300_ref12) 2017; 159 Kong (2021052110393062300_ref48) 2020 Jazayeri (2021052110393062300_ref61) 2016; 533 Segler (2021052110393062300_ref24) 2018; 4 Kong (2021052110393062300_ref47) 2019; 59 Moen (2021052110393062300_ref2) 2019; 16 Guimaraes (2021052110393062300_ref34) 2017 De Cao (2021052110393062300_ref37) 2018 Bai (2021052110393062300_ref62) 2019; 7 Kingma (2021052110393062300_ref28) 2013 Lipinski (2021052110393062300_ref54) 2001; 46 Segler (2021052110393062300_ref3) 2018; 555 Irwin (2021052110393062300_ref50) 2012; 52 Daina (2021052110393062300_ref55) 2017; 7 |
| References_xml | – volume: 51 start-page: 69 year: 2011 ident: 2021052110393062300_ref58 article-title: Assessing the performance of the MM/PBSA and MM/GBSA methods. 1. The accuracy of binding free energy calculations based on molecular dynamics simulations publication-title: J Chem Inf Model doi: 10.1021/ci100275a – volume: 11 start-page: 567 year: 2019 ident: 2021052110393062300_ref25 article-title: Deep learning for molecular generation publication-title: Future Med Chem doi: 10.4155/fmc-2018-0358 – volume: 18 start-page: 12964 year: 2016 ident: 2021052110393062300_ref43 article-title: Comprehensive evaluation of ten docking programs on a diverse set of protein-ligand complexes: the prediction accuracy of sampling power and scoring power publication-title: Phys Chem Chem Phys doi: 10.1039/C6CP01555G – volume: 267 start-page: 144 year: 1992 ident: 2021052110393062300_ref11 article-title: How neural networks learn from experience publication-title: Sci Am doi: 10.1038/scientificamerican0992-144 – volume: 51 start-page: 1083 year: 2011 ident: 2021052110393062300_ref39 article-title: LigBuilder 2: a practical de novo drug design approach publication-title: J Chem Inf Model doi: 10.1021/ci100350u – volume: 59 start-page: 3556 year: 2019 ident: 2021052110393062300_ref47 article-title: CoDockPP: a multistage approach for global and site-specific protein-protein docking publication-title: J Chem Inf Model doi: 10.1021/acs.jcim.9b00445 – volume: 47 start-page: 2977 year: 2004 ident: 2021052110393062300_ref44 article-title: The PDBbind database: collection of binding affinities for protein-ligand complexes with known three-dimensional structures publication-title: J Med Chem doi: 10.1021/jm030580l – volume: 582 start-page: 289 year: 2020 ident: 2021052110393062300_ref63 article-title: Structure of Mpro from COVID-19 virus and discovery of its inhibitors publication-title: Nature doi: 10.1038/s41586-020-2223-y – volume: 46 start-page: 3 year: 2001 ident: 2021052110393062300_ref54 article-title: Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings publication-title: Adv Drug Deliv Rev doi: 10.1016/S0169-409X(00)00129-0 – year: 2019 ident: 2021052110393062300_ref5 article-title: Deep learning for mining protein data publication-title: Brief Bioinform doi: 10.1093/bib/bbz156 – volume: 62 start-page: 1701 year: 2019 ident: 2021052110393062300_ref56 article-title: Two decades under the influence of the rule of five and the changing properties of approved oral drugs publication-title: J Med Chem doi: 10.1021/acs.jmedchem.8b00686 – volume-title: arXiv preprint year: 2013 ident: 2021052110393062300_ref28 article-title: Auto-encoding variational Bayes – volume: 47 start-page: 4463 year: 2004 ident: 2021052110393062300_ref16 article-title: Classification of kinase inhibitors using a Bayesian model publication-title: J Med Chem doi: 10.1021/jm0303195 – volume: 9 start-page: e1395 year: 2019 ident: 2021052110393062300_ref30 article-title: Advances and challenges in deep generative models for de novo molecule generation publication-title: Wiley Interdisciplinary Reviews: Computational Molecular Science – volume: 13 start-page: 1636 year: 2013 ident: 2021052110393062300_ref18 article-title: Entropy model for multiplex drug-target interaction endpoints of drug immunotoxicity publication-title: Curr Top Med Chem doi: 10.2174/15680266113139990114 – volume: 16 start-page: 1233 year: 2019 ident: 2021052110393062300_ref2 article-title: Deep learning for cellular image analysis publication-title: Nat Methods doi: 10.1038/s41592-019-0403-1 – volume: 13 start-page: 18 year: 1998 ident: 2021052110393062300_ref7 article-title: Support vector machines publication-title: IEEE Intelligent Systems and their applications doi: 10.1109/5254.708428 – volume-title: arXiv preprint year: 2017 ident: 2021052110393062300_ref34 article-title: Objective-reinforced generative adversarial networks (ORGAN) for sequence generation models – year: 2020 ident: 2021052110393062300_ref48 article-title: Template-based modeling and ab-initio docking using CoDock in CAPRI publication-title: Proteins doi: 10.1002/prot.25892 – volume: 29 start-page: 131 year: 1997 ident: 2021052110393062300_ref9 article-title: Bayesian network classifiers publication-title: Machine learning doi: 10.1023/A:1007465528199 – volume: 6 start-page: 498 year: 2000 ident: 2021052110393062300_ref40 article-title: LigBuilder: a multi-purpose program for structure-based drug design publication-title: Molecular modeling annual doi: 10.1007/s0089400060498 – volume-title: arXiv preprint year: 2020 ident: 2021052110393062300_ref45 article-title: COVID-19 docking server: an interactive server for docking small molecules, peptides and antibodies against potential targets of COVID-19 doi: 10.1093/bioinformatics/btaa645 – year: 2019 ident: 2021052110393062300_ref27 article-title: Deep learning for drug-drug interaction extraction from the literature: a review publication-title: Brief Bioinform doi: 10.1093/bib/bbz087 – volume: 11 start-page: e0155183 year: 2016 ident: 2021052110393062300_ref52 article-title: Vinardo: a scoring function based on Autodock Vina improves scoring, docking, and virtual screening publication-title: PLoS One doi: 10.1371/journal.pone.0155183 – volume: 21 start-page: 282 year: 2020 ident: 2021052110393062300_ref53 article-title: Comprehensive assessment of nine docking programs on type II kinase inhibitors: prediction accuracy of sampling power, scoring power and screening power publication-title: Brief Bioinform – volume: 4 start-page: 87 year: 2017 ident: 2021052110393062300_ref59 article-title: Recent developments and applications of the MMPBSA method publication-title: Front Mol Biosci doi: 10.3389/fmolb.2017.00087 – volume: 37 start-page: 87 year: 1994 ident: 2021052110393062300_ref10 article-title: The basic ideas in neural networks publication-title: Communications of the ACM doi: 10.1145/175247.175256 – volume: 39 start-page: 775 year: 1999 ident: 2021052110393062300_ref6 article-title: Applications of genetic algorithms on the structure− activity relationship analysis of some cinnamamides publication-title: J Chem Inf Comput Sci doi: 10.1021/ci990010n – volume: 26 start-page: 1781 year: 2005 ident: 2021052110393062300_ref57 article-title: Scalable molecular dynamics with NAMD publication-title: J Comput Chem doi: 10.1002/jcc.20289 – volume: 3 start-page: 80 year: 2016 ident: 2021052110393062300_ref20 article-title: DeepTox: toxicity prediction using deep learning publication-title: Front Environ Sci doi: 10.3389/fenvs.2015.00080 – volume: 10 start-page: e1429 year: 2020 ident: 2021052110393062300_ref26 article-title: From machine learning to deep learning: advances in scoring functions for protein–ligand docking publication-title: Wiley Interdisciplinary Reviews: Computational Molecular Science – volume: 7 start-page: 42717 year: 2017 ident: 2021052110393062300_ref55 article-title: SwissADME: a free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules publication-title: Sci Rep doi: 10.1038/srep42717 – volume: 28 start-page: 1711 year: 2007 ident: 2021052110393062300_ref65 article-title: Ultrafast shape recognition to search compound databases for similar molecular shapes publication-title: J Comput Chem doi: 10.1002/jcc.20681 – volume: 6 start-page: 24817 year: 2016 ident: 2021052110393062300_ref38 article-title: Constructing and validating high-performance MIEC-SVM models in virtual screening for kinases: a better way for actives discovery publication-title: Sci Rep doi: 10.1038/srep24817 – volume: 4 start-page: 268 year: 2018 ident: 2021052110393062300_ref31 article-title: Automatic chemical design using a data-driven continuous representation of molecules publication-title: ACS Cent Sci doi: 10.1021/acscentsci.7b00572 – volume-title: arXiv preprint year: 2017 ident: 2021052110393062300_ref35 article-title: Wasserstein Gan – volume: 7 start-page: 851 year: 2019 ident: 2021052110393062300_ref62 article-title: Conformation transition of intracellular part of glucagon receptor in complex with agonist glucagon by conventional and accelerated molecular dynamics simulations publication-title: Front Chem doi: 10.3389/fchem.2019.00851 – volume: 4 start-page: 120 year: 2018 ident: 2021052110393062300_ref24 article-title: Generating focused molecule libraries for drug discovery with recurrent neural networks publication-title: ACS Cent Sci doi: 10.1021/acscentsci.7b00512 – volume: 8 start-page: 3314 year: 2012 ident: 2021052110393062300_ref60 article-title: MMPBSA.Py: an efficient program for end-state free energy calculations publication-title: Journal of Chemical Theory and Computation doi: 10.1021/ct300418h – start-page: 2672 volume-title: Advances in Neural Information Processing Systems year: 2014 ident: 2021052110393062300_ref29 article-title: Generative adversarial nets – year: 2018 ident: 2021052110393062300_ref37 article-title: An implicit generative model for small molecular graphs publication-title: arXiv preprint – volume: 8 start-page: 10883 year: 2017 ident: 2021052110393062300_ref33 article-title: The cornucopia of meaningful leads: applying deep adversarial autoencoders for new molecule development in oncology publication-title: Oncotarget doi: 10.18632/oncotarget.14073 – volume: 119 start-page: 10520 year: 2019 ident: 2021052110393062300_ref36 article-title: Concepts of artificial intelligence for computer-assisted drug discovery publication-title: Chem Rev doi: 10.1021/acs.chemrev.8b00728 – volume: 59 start-page: 4171 year: 2016 ident: 2021052110393062300_ref41 article-title: OpenGrowth: an automated and rational algorithm for finding new protein ligands publication-title: J Med Chem doi: 10.1021/acs.jmedchem.5b00886 – volume: 36 start-page: 1600118 year: 2017 ident: 2021052110393062300_ref21 article-title: Performance of deep and shallow neural networks, the universal approximation theorem, activity cliffs, and QSAR publication-title: Molecular Informatics doi: 10.1002/minf.201600118 – volume: 9 start-page: 8802 year: 2019 ident: 2021052110393062300_ref4 article-title: Improved classification of blood-brain-barrier drugs using deep learning publication-title: Sci Rep doi: 10.1038/s41598-019-44773-4 – volume: 81 start-page: 442 year: 2013 ident: 2021052110393062300_ref15 article-title: Virtual screening of CB(2) receptor agonists from bayesian network and high-throughput docking: structural insights into agonist-modulated GPCR features publication-title: Chem Biol Drug Des doi: 10.1111/cbdd.12095 – volume: 23 start-page: 1241 year: 2018 ident: 2021052110393062300_ref22 article-title: The rise of deep learning in drug discovery publication-title: Drug Discov Today doi: 10.1016/j.drudis.2018.01.039 – volume: 533 start-page: 274 year: 2016 ident: 2021052110393062300_ref61 article-title: Extra-helical binding site of a glucagon receptor antagonist publication-title: Nature doi: 10.1038/nature17414 – volume: 52 start-page: 1757 year: 2012 ident: 2021052110393062300_ref50 article-title: ZINC: a free tool to discover chemistry for biology publication-title: J Chem Inf Model doi: 10.1021/ci3001277 – start-page: 278 volume-title: Proceedings of 3rd International Conference on Document Analysis and Recognition year: 1995 ident: 2021052110393062300_ref8 – volume: 57 start-page: 1007 year: 2017 ident: 2021052110393062300_ref14 article-title: Structural and sequence similarity makes a significant impact on machine-learning-based scoring functions for protein-ligand interactions publication-title: J Chem Inf Model doi: 10.1021/acs.jcim.7b00049 – volume: 9 start-page: 204 year: 2018 ident: 2021052110393062300_ref49 article-title: Data sets representative of the structures and experimental properties of FDA-approved drugs publication-title: ACS Med Chem Lett doi: 10.1021/acsmedchemlett.7b00462 – volume: 1 start-page: 140022 year: 2014 ident: 2021052110393062300_ref51 article-title: Quantum chemistry structures and properties of 134 kilo molecules publication-title: Sci Data doi: 10.1038/sdata.2014.22 – volume: 69 start-page: 110 year: 2017 ident: 2021052110393062300_ref13 article-title: Improving virtual screening predictive accuracy of human kallikrein 5 inhibitors using machine learning models publication-title: Comput Biol Chem doi: 10.1016/j.compbiolchem.2017.05.007 – volume: 30 start-page: 2785 year: 2009 ident: 2021052110393062300_ref66 article-title: AutoDock4 and AutoDockTools4: automated docking with selective receptor flexibility publication-title: J Comput Chem doi: 10.1002/jcc.21256 – volume: 31 start-page: 455 year: 2010 ident: 2021052110393062300_ref42 article-title: AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading publication-title: J Comput Chem doi: 10.1002/jcc.21334 – volume: 172 start-page: 1122 year: 2018 ident: 2021052110393062300_ref1 article-title: Identifying medical diagnoses and treatable diseases by image-based deep learning publication-title: Cell doi: 10.1016/j.cell.2018.02.010 – volume: 44 start-page: 1031 year: 2004 ident: 2021052110393062300_ref17 article-title: Artificial neural networks and linear discriminant analysis: a valuable combination in the selection of new antibacterial compounds publication-title: J Chem Inf Comput Sci doi: 10.1021/ci030340e – volume-title: arXiv preprint year: 2014 ident: 2021052110393062300_ref19 article-title: Multi-task neural networks for QSAR predictions – volume: 159 start-page: 31 year: 2017 ident: 2021052110393062300_ref12 article-title: Developing an in silico pipeline for faster drug candidate discovery: virtual high throughput screening with the signature molecular descriptor using support vector machine models publication-title: Chem Eng Sci doi: 10.1016/j.ces.2016.02.037 – volume: 555 start-page: 604 year: 2018 ident: 2021052110393062300_ref3 article-title: Planning chemical syntheses with deep neural networks and symbolic AI publication-title: Nature doi: 10.1038/nature25978 – volume-title: arXiv preprint year: 2015 ident: 2021052110393062300_ref32 article-title: Adversarial autoencoders – volume: 7 start-page: 40053 year: 2017 ident: 2021052110393062300_ref23 article-title: Prediction of N-methyl-D-aspartate receptor GluN1-ligand binding affinity by a novel SVM-pose/SVM-score combinatorial ensemble docking scheme publication-title: Sci Rep doi: 10.1038/srep40053 – volume: 3 start-page: 33 year: 2011 ident: 2021052110393062300_ref46 article-title: Open babel: an open chemical toolbox publication-title: J Chem doi: 10.1186/1758-2946-3-33 – volume: 368 start-page: 409 year: 2020 ident: 2021052110393062300_ref64 article-title: Crystal structure of SARS-CoV-2 main protease provides a basis for design of improved alpha-ketoamide inhibitors publication-title: Science doi: 10.1126/science.abb3405 |
| SSID | ssj0020781 |
| Score | 2.6514754 |
| Snippet | Deep learning is an important branch of artificial intelligence that has been successfully applied into medicine and two-dimensional ligand design. The... |
| SourceID | pubmedcentral proquest crossref |
| SourceType | Open Access Repository Aggregation Database Enrichment Source Index Database |
| SubjectTerms | Problem Solving Protocol |
| Title | MolAICal: a soft tool for 3D drug design of protein targets by artificial intelligence and classical algorithm |
| URI | https://www.proquest.com/docview/2432856308 https://pubmed.ncbi.nlm.nih.gov/PMC7454275 |
| Volume | 22 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV1Li9swEBbtlkIvpU-avlBhTw3e3UjyQ71t90FasrsUEsjNSLaUGFK7JDY0_fUdPeLYkMO2FxEmYhCaL5MZaeYTQsehjrKR1DyIM50FTI_gJyW1CGQuhRahENyeQ97cRuMZ-z4P5_sWAttdUsuT7M_BvpL_sSrIwK6mS_YfLNsqBQF8BvvCCBaG8V42vqlW598uxMp1LG_Ao0IoWdmOxCG9HObrZjHMbYmGLWw2lAymrNEWf29M4Gl0egqJosvNaZvdTFztuARWi2pd1MufvRtgyLG1ffMTVMqi8gysdad6_qt76vpHoZX_f7RHBO7EddlU2z0y540DTrkAYTt3UljxuBHl72In90cUZGRu18lZx6uyOIZE1bFFn6gDMu-KCelAjh708I79ShbSjFKIkeNy7zNp396l17PJJJ1ezacP0SMCKYTxgdO7eZuMG5Ij23nml-F7N0H9KSg_9ar70co-BekX0HYikukz9NSnEvjc4eI5eqDKF-ixe1x0-xKVO3R8wQIbbGCDDQxWwvQSG2xghw1caeyxgT02sNziPTZwFxsYsIFbbOAWG6_Q7PpqejEO_OsaQUaTqA5oTihjYc5EnEVCCsUTomIhKEvykOVJIkUMAWIeUwVBdx7mEacw-UxFpus25PQ1OiqrUr1BGHJmesalUjrijOdKaqUZJ4JA-k0U5wP0ebeLaeap580LKKvUlUDQFLY89Vs-QMft5F-OceXwtE87c6TgEc01lyhV1WxSwigsEFaUDFDcs1Orz3Cq978pi6XlVo9ZyEgcvr2H9nfoyR7s79FRvW7UB4hQa_nRQu0vhnCa_w |
| 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=MolAICal%3A+a+soft+tool+for+3D+drug+design+of+protein+targets+by+artificial+intelligence+and+classical+algorithm&rft.jtitle=Briefings+in+bioinformatics&rft.au=Bai%2C+Qifeng&rft.au=Tan%2C+Shuoyan&rft.au=Xu%2C+Tingyang&rft.au=Liu%2C+Huanxiang&rft.date=2021-05-20&rft.issn=1477-4054&rft.eissn=1477-4054&rft.volume=22&rft.issue=3&rft_id=info:doi/10.1093%2Fbib%2Fbbaa161&rft.externalDBID=NO_FULL_TEXT |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1467-5463&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1467-5463&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1467-5463&client=summon |