QSPRpred: a Flexible Open-Source Quantitative Structure-Property Relationship Modelling Tool
Building reliable and robust quantitative structure–property relationship (QSPR) models is a challenging task. First, the experimental data needs to be obtained, analyzed and curated. Second, the number of available methods is continuously growing and evaluating different algorithms and methodologie...
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| Published in | Journal of cheminformatics Vol. 16; no. 1; pp. 128 - 16 |
|---|---|
| Main Authors | , , , , , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Cham
Springer International Publishing
14.11.2024
BioMed Central Ltd Springer Nature B.V BMC |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1758-2946 1758-2946 |
| DOI | 10.1186/s13321-024-00908-y |
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| Abstract | Building reliable and robust quantitative structure–property relationship (QSPR) models is a challenging task. First, the experimental data needs to be obtained, analyzed and curated. Second, the number of available methods is continuously growing and evaluating different algorithms and methodologies can be arduous. Finally, the last hurdle that researchers face is to ensure the reproducibility of their models and facilitate their transferability into practice. In this work, we introduce QSPRpred, a toolkit for analysis of bioactivity data sets and QSPR modelling, which attempts to address the aforementioned challenges. QSPRpred’s modular Python API enables users to intuitively describe different parts of a modelling workflow using a plethora of pre-implemented components, but also integrates customized implementations in a “plug-and-play” manner. QSPRpred data sets and models are directly serializable, which means they can be readily reproduced and put into operation after training as the models are saved with all required data pre-processing steps to make predictions on new compounds directly from SMILES strings. The general-purpose character of QSPRpred is also demonstrated by inclusion of support for multi-task and proteochemometric modelling. The package is extensively documented and comes with a large collection of tutorials to help new users. In this paper, we describe all of QSPRpred’s functionalities and also conduct a small benchmarking case study to illustrate how different components can be leveraged to compare a diverse set of models. QSPRpred is fully open-source and available at
https://github.com/CDDLeiden/QSPRpred
.
Scientific Contribution
QSPRpred aims to provide a complex, but comprehensive Python API to conduct all tasks encountered in QSPR modelling from data preparation and analysis to model creation and model deployment. In contrast to similar packages, QSPRpred offers a wider and more exhaustive range of capabilities and integrations with many popular packages that also go beyond QSPR modelling. A significant contribution of QSPRpred is also in its automated and highly standardized serialization scheme, which significantly improves reproducibility and transferability of models. |
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| AbstractList | Building reliable and robust quantitative structure-property relationship (QSPR) models is a challenging task. First, the experimental data needs to be obtained, analyzed and curated. Second, the number of available methods is continuously growing and evaluating different algorithms and methodologies can be arduous. Finally, the last hurdle that researchers face is to ensure the reproducibility of their models and facilitate their transferability into practice. In this work, we introduce QSPRpred, a toolkit for analysis of bioactivity data sets and QSPR modelling, which attempts to address the aforementioned challenges. QSPRpred's modular Python API enables users to intuitively describe different parts of a modelling workflow using a plethora of pre-implemented components, but also integrates customized implementations in a "plug-and-play" manner. QSPRpred data sets and models are directly serializable, which means they can be readily reproduced and put into operation after training as the models are saved with all required data pre-processing steps to make predictions on new compounds directly from SMILES strings. The general-purpose character of QSPRpred is also demonstrated by inclusion of support for multi-task and proteochemometric modelling. The package is extensively documented and comes with a large collection of tutorials to help new users. In this paper, we describe all of QSPRpred's functionalities and also conduct a small benchmarking case study to illustrate how different components can be leveraged to compare a diverse set of models. QSPRpred is fully open-source and available at https://github.com/CDDLeiden/QSPRpred .Scientific ContributionQSPRpred aims to provide a complex, but comprehensive Python API to conduct all tasks encountered in QSPR modelling from data preparation and analysis to model creation and model deployment. In contrast to similar packages, QSPRpred offers a wider and more exhaustive range of capabilities and integrations with many popular packages that also go beyond QSPR modelling. A significant contribution of QSPRpred is also in its automated and highly standardized serialization scheme, which significantly improves reproducibility and transferability of models. Abstract Building reliable and robust quantitative structure–property relationship (QSPR) models is a challenging task. First, the experimental data needs to be obtained, analyzed and curated. Second, the number of available methods is continuously growing and evaluating different algorithms and methodologies can be arduous. Finally, the last hurdle that researchers face is to ensure the reproducibility of their models and facilitate their transferability into practice. In this work, we introduce QSPRpred, a toolkit for analysis of bioactivity data sets and QSPR modelling, which attempts to address the aforementioned challenges. QSPRpred’s modular Python API enables users to intuitively describe different parts of a modelling workflow using a plethora of pre-implemented components, but also integrates customized implementations in a “plug-and-play” manner. QSPRpred data sets and models are directly serializable, which means they can be readily reproduced and put into operation after training as the models are saved with all required data pre-processing steps to make predictions on new compounds directly from SMILES strings. The general-purpose character of QSPRpred is also demonstrated by inclusion of support for multi-task and proteochemometric modelling. The package is extensively documented and comes with a large collection of tutorials to help new users. In this paper, we describe all of QSPRpred’s functionalities and also conduct a small benchmarking case study to illustrate how different components can be leveraged to compare a diverse set of models. QSPRpred is fully open-source and available at https://github.com/CDDLeiden/QSPRpred . Scientific Contribution QSPRpred aims to provide a complex, but comprehensive Python API to conduct all tasks encountered in QSPR modelling from data preparation and analysis to model creation and model deployment. In contrast to similar packages, QSPRpred offers a wider and more exhaustive range of capabilities and integrations with many popular packages that also go beyond QSPR modelling. A significant contribution of QSPRpred is also in its automated and highly standardized serialization scheme, which significantly improves reproducibility and transferability of models. Building reliable and robust quantitative structure-property relationship (QSPR) models is a challenging task. First, the experimental data needs to be obtained, analyzed and curated. Second, the number of available methods is continuously growing and evaluating different algorithms and methodologies can be arduous. Finally, the last hurdle that researchers face is to ensure the reproducibility of their models and facilitate their transferability into practice. In this work, we introduce QSPRpred, a toolkit for analysis of bioactivity data sets and QSPR modelling, which attempts to address the aforementioned challenges. QSPRpred's modular Python API enables users to intuitively describe different parts of a modelling workflow using a plethora of pre-implemented components, but also integrates customized implementations in a "plug-and-play" manner. QSPRpred data sets and models are directly serializable, which means they can be readily reproduced and put into operation after training as the models are saved with all required data pre-processing steps to make predictions on new compounds directly from SMILES strings. The general-purpose character of QSPRpred is also demonstrated by inclusion of support for multi-task and proteochemometric modelling. The package is extensively documented and comes with a large collection of tutorials to help new users. In this paper, we describe all of QSPRpred's functionalities and also conduct a small benchmarking case study to illustrate how different components can be leveraged to compare a diverse set of models. QSPRpred is fully open-source and available at https://github.com/CDDLeiden/QSPRpred .Scientific ContributionQSPRpred aims to provide a complex, but comprehensive Python API to conduct all tasks encountered in QSPR modelling from data preparation and analysis to model creation and model deployment. In contrast to similar packages, QSPRpred offers a wider and more exhaustive range of capabilities and integrations with many popular packages that also go beyond QSPR modelling. A significant contribution of QSPRpred is also in its automated and highly standardized serialization scheme, which significantly improves reproducibility and transferability of models.Building reliable and robust quantitative structure-property relationship (QSPR) models is a challenging task. First, the experimental data needs to be obtained, analyzed and curated. Second, the number of available methods is continuously growing and evaluating different algorithms and methodologies can be arduous. Finally, the last hurdle that researchers face is to ensure the reproducibility of their models and facilitate their transferability into practice. In this work, we introduce QSPRpred, a toolkit for analysis of bioactivity data sets and QSPR modelling, which attempts to address the aforementioned challenges. QSPRpred's modular Python API enables users to intuitively describe different parts of a modelling workflow using a plethora of pre-implemented components, but also integrates customized implementations in a "plug-and-play" manner. QSPRpred data sets and models are directly serializable, which means they can be readily reproduced and put into operation after training as the models are saved with all required data pre-processing steps to make predictions on new compounds directly from SMILES strings. The general-purpose character of QSPRpred is also demonstrated by inclusion of support for multi-task and proteochemometric modelling. The package is extensively documented and comes with a large collection of tutorials to help new users. In this paper, we describe all of QSPRpred's functionalities and also conduct a small benchmarking case study to illustrate how different components can be leveraged to compare a diverse set of models. QSPRpred is fully open-source and available at https://github.com/CDDLeiden/QSPRpred .Scientific ContributionQSPRpred aims to provide a complex, but comprehensive Python API to conduct all tasks encountered in QSPR modelling from data preparation and analysis to model creation and model deployment. In contrast to similar packages, QSPRpred offers a wider and more exhaustive range of capabilities and integrations with many popular packages that also go beyond QSPR modelling. A significant contribution of QSPRpred is also in its automated and highly standardized serialization scheme, which significantly improves reproducibility and transferability of models. Building reliable and robust quantitative structure–property relationship (QSPR) models is a challenging task. First, the experimental data needs to be obtained, analyzed and curated. Second, the number of available methods is continuously growing and evaluating different algorithms and methodologies can be arduous. Finally, the last hurdle that researchers face is to ensure the reproducibility of their models and facilitate their transferability into practice. In this work, we introduce QSPRpred, a toolkit for analysis of bioactivity data sets and QSPR modelling, which attempts to address the aforementioned challenges. QSPRpred’s modular Python API enables users to intuitively describe different parts of a modelling workflow using a plethora of pre-implemented components, but also integrates customized implementations in a “plug-and-play” manner. QSPRpred data sets and models are directly serializable, which means they can be readily reproduced and put into operation after training as the models are saved with all required data pre-processing steps to make predictions on new compounds directly from SMILES strings. The general-purpose character of QSPRpred is also demonstrated by inclusion of support for multi-task and proteochemometric modelling. The package is extensively documented and comes with a large collection of tutorials to help new users. In this paper, we describe all of QSPRpred’s functionalities and also conduct a small benchmarking case study to illustrate how different components can be leveraged to compare a diverse set of models. QSPRpred is fully open-source and available at https://github.com/CDDLeiden/QSPRpred . Scientific Contribution QSPRpred aims to provide a complex, but comprehensive Python API to conduct all tasks encountered in QSPR modelling from data preparation and analysis to model creation and model deployment. In contrast to similar packages, QSPRpred offers a wider and more exhaustive range of capabilities and integrations with many popular packages that also go beyond QSPR modelling. A significant contribution of QSPRpred is also in its automated and highly standardized serialization scheme, which significantly improves reproducibility and transferability of models. Building reliable and robust quantitative structure-property relationship (QSPR) models is a challenging task. First, the experimental data needs to be obtained, analyzed and curated. Second, the number of available methods is continuously growing and evaluating different algorithms and methodologies can be arduous. Finally, the last hurdle that researchers face is to ensure the reproducibility of their models and facilitate their transferability into practice. In this work, we introduce QSPRpred, a toolkit for analysis of bioactivity data sets and QSPR modelling, which attempts to address the aforementioned challenges. QSPRpred's modular Python API enables users to intuitively describe different parts of a modelling workflow using a plethora of pre-implemented components, but also integrates customized implementations in a "plug-and-play" manner. QSPRpred data sets and models are directly serializable, which means they can be readily reproduced and put into operation after training as the models are saved with all required data pre-processing steps to make predictions on new compounds directly from SMILES strings. The general-purpose character of QSPRpred is also demonstrated by inclusion of support for multi-task and proteochemometric modelling. The package is extensively documented and comes with a large collection of tutorials to help new users. In this paper, we describe all of QSPRpred's functionalities and also conduct a small benchmarking case study to illustrate how different components can be leveraged to compare a diverse set of models. QSPRpred is fully open-source and available at https://github.com/CDDLeiden/QSPRpred. Building reliable and robust quantitative structure-property relationship (QSPR) models is a challenging task. First, the experimental data needs to be obtained, analyzed and curated. Second, the number of available methods is continuously growing and evaluating different algorithms and methodologies can be arduous. Finally, the last hurdle that researchers face is to ensure the reproducibility of their models and facilitate their transferability into practice. In this work, we introduce QSPRpred, a toolkit for analysis of bioactivity data sets and QSPR modelling, which attempts to address the aforementioned challenges. QSPRpred's modular Python API enables users to intuitively describe different parts of a modelling workflow using a plethora of pre-implemented components, but also integrates customized implementations in a "plug-and-play" manner. QSPRpred data sets and models are directly serializable, which means they can be readily reproduced and put into operation after training as the models are saved with all required data pre-processing steps to make predictions on new compounds directly from SMILES strings. The general-purpose character of QSPRpred is also demonstrated by inclusion of support for multi-task and proteochemometric modelling. The package is extensively documented and comes with a large collection of tutorials to help new users. In this paper, we describe all of QSPRpred's functionalities and also conduct a small benchmarking case study to illustrate how different components can be leveraged to compare a diverse set of models. QSPRpred is fully open-source and available at Scientific Contribution QSPRpred aims to provide a complex, but comprehensive Python API to conduct all tasks encountered in QSPR modelling from data preparation and analysis to model creation and model deployment. In contrast to similar packages, QSPRpred offers a wider and more exhaustive range of capabilities and integrations with many popular packages that also go beyond QSPR modelling. A significant contribution of QSPRpred is also in its automated and highly standardized serialization scheme, which significantly improves reproducibility and transferability of models. Keywords: QSPR modelling, QSAR modelling, Proteochemometrics, Cheminformatics, Machine learning, Software |
| ArticleNumber | 128 |
| Audience | Academic |
| Author | Béquignon, Olivier J. M. van den Broek, Remco L. van Hasselt, J. G. Coen Šícho, Martin González, Marina Gorostiola van der Graaf, Piet. H. Araripe, David Alencar Luukkonen, Sohvi van den Maagdenberg, Helle W. Jespers, Michiel Schoenmaker, Linde Bernatavicius, Andrius van Westen, Gerard J. P. |
| Author_xml | – sequence: 1 givenname: Helle W. orcidid: 0000-0002-9718-7806 surname: van den Maagdenberg fullname: van den Maagdenberg, Helle W. organization: Computational Drug Discovery, Leiden Academic Centre for Drug Research, Leiden University – sequence: 2 givenname: Martin orcidid: 0000-0002-8771-1731 surname: Šícho fullname: Šícho, Martin organization: Computational Drug Discovery, Leiden Academic Centre for Drug Research, Leiden University, CZ-OPENSCREEN: National Infrastructure for Chemical Biology, Department of Informatics and Chemistry, Faculty of Chemical Technology, University of Chemistry and Technology Prague – sequence: 3 givenname: David Alencar orcidid: 0000-0002-5104-1959 surname: Araripe fullname: Araripe, David Alencar organization: Computational Drug Discovery, Leiden Academic Centre for Drug Research, Leiden University, Department of Human Genetics, Leiden University Medical Center – sequence: 4 givenname: Sohvi orcidid: 0000-0001-9387-1427 surname: Luukkonen fullname: Luukkonen, Sohvi organization: Computational Drug Discovery, Leiden Academic Centre for Drug Research, Leiden University, ELLIS Unit Linz and LIT AI Lab, Institute for Machine Learning, Johannes Kepler University – sequence: 5 givenname: Linde orcidid: 0000-0001-9879-1004 surname: Schoenmaker fullname: Schoenmaker, Linde organization: Computational Drug Discovery, Leiden Academic Centre for Drug Research, Leiden University – sequence: 6 givenname: Michiel orcidid: 0009-0003-2083-0159 surname: Jespers fullname: Jespers, Michiel organization: Computational Drug Discovery, Leiden Academic Centre for Drug Research, Leiden University – sequence: 7 givenname: Olivier J. M. orcidid: 0000-0002-7554-9220 surname: Béquignon fullname: Béquignon, Olivier J. M. organization: Computational Drug Discovery, Leiden Academic Centre for Drug Research, Leiden University, Department of Neurosurgery, Brain Tumor Center Amsterdam, Amsterdam University Medical Center, Cancer Center Amsterdam – sequence: 8 givenname: Marina Gorostiola orcidid: 0000-0003-1568-0881 surname: González fullname: González, Marina Gorostiola organization: Computational Drug Discovery, Leiden Academic Centre for Drug Research, Leiden University, Oncode Institute – sequence: 9 givenname: Remco L. orcidid: 0009-0008-5661-1157 surname: van den Broek fullname: van den Broek, Remco L. organization: Computational Drug Discovery, Leiden Academic Centre for Drug Research, Leiden University – sequence: 10 givenname: Andrius orcidid: 0000-0002-0058-3678 surname: Bernatavicius fullname: Bernatavicius, Andrius organization: Computational Drug Discovery, Leiden Academic Centre for Drug Research, Leiden University, Leiden Institute of Advanced Computer Science, Leiden University – sequence: 11 givenname: J. G. Coen orcidid: 0000-0002-1664-7314 surname: van Hasselt fullname: van Hasselt, J. G. Coen organization: Computational Drug Discovery, Leiden Academic Centre for Drug Research, Leiden University – sequence: 12 givenname: Piet. H. orcidid: 0000-0003-1314-3484 surname: van der Graaf fullname: van der Graaf, Piet. H. organization: Computational Drug Discovery, Leiden Academic Centre for Drug Research, Leiden University, Certara UK – sequence: 13 givenname: Gerard J. P. orcidid: 0000-0003-0717-1817 surname: van Westen fullname: van Westen, Gerard J. P. email: gerard@lacdr.leidenuniv.nl organization: Computational Drug Discovery, Leiden Academic Centre for Drug Research, Leiden University |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/39543652$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.1021/acs.jcim.4c00049 10.1002/bip.20296 10.1093/molbev/mst010 10.1186/s13321-018-0258-y 10.1021/acs.jcim.9b00237 10.1186/s13321-023-00730-y 10.1016/j.molliq.2021.117631 10.1039/D0CS00098A 10.1186/s13321-015-0086-2 10.1021/jm00390a003 10.1039/C7SC02664A 10.1093/nar/gkac956 10.1186/s13321-020-0408-x 10.1021/acs.jcim.1c00086 10.1371/journal.pcbi.1011301 10.1186/s13321-022-00672-x 10.1186/s13321-024-00837-w 10.1021/acs.jcim.3c00132 10.1186/s13321-018-0281-z 10.1007/s11095-015-1832-x 10.1093/nar/gky1075 10.1021/ci800038f 10.1080/17460441.2021.1909567 10.1002/minf.201800108 10.1038/s41598-023-45086-3 10.1021/jm4004285 10.1021/acs.jcim.2c01073 10.1002/jcc.21707 10.1021/ci010132r 10.1038/sdata.2016.18 10.1016/j.jbi.2020.103484 10.1186/s13321-021-00550-y 10.1021/acs.jcim.3c00523 10.1186/s13321-023-00689-w 10.1186/s13321-023-00743-7 10.1126/science.359.6377.725 10.3390/molecules26041111 10.1038/s41573-023-00832-0 10.1186/s13321-020-0413-0 10.1021/acs.jcim.9b01204 10.1186/s13321-023-00745-5 10.1021/acs.jcim.9b01053 10.1021/acs.jcim.7b00523 10.3390/molecules25215172 10.1021/acs.jcim.1c00889 10.1038/msb.2011.75 10.1101/2022.12.13.520154 10.1186/s13321-019-0385-0 10.1089/cmb.2008.0173 10.1021/acs.jcim.6b00088 10.1038/s41467-023-41948-6 10.1021/acs.molpharmaceut.3c00812 10.1016/j.drudis.2022.03.017 10.1021/acs.jcim.4c00457 10.1002/minf.202000113 10.1038/s41467-024-51321-w 10.1016/j.ddtec.2020.08.003 10.1039/C4MD00216D 10.48550/arXiv.2207.08815 10.26434/chemrxiv-2023-fzqwd 10.48550/arXiv.2305.02997 10.1145/3292500.3330701 10.1145/2939672.2939785 10.26434/chemrxiv-2022-m8l33-v2 10.1021/acs.jcim.3c00434 10.1007/978-3-540-78246-9_38 10.26434/chemrxiv-2023-3zcfl-v3 10.1109/TPAMI.2021.3054719 10.25080/Majora-92bf1922-00a 10.1021/ja01062a035 10.1021/ci5001168 10.26434/chemrxiv-2023-q11q4-v2 10.48550/arXiv.2211.12858 10.1186/s13321-017-0232-0 |
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| Issue | 1 |
| Keywords | Proteochemometrics Cheminformatics Software QSPR modelling QSAR modelling Machine learning |
| Language | English |
| License | 2024. The Author(s). cc-by |
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| PublicationTitle | Journal of cheminformatics |
| PublicationTitleAbbrev | J Cheminform |
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| Publisher | Springer International Publishing BioMed Central Ltd Springer Nature B.V BMC |
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| References | Wilkinson, Dumontier, Aalbersberg, Appleton, Axton, Baak, Blomberg, Boiten, Silva Santos, Bourne, Bouwman, Brookes, Clark, Crosas, Dillo, Dumon, Edmunds, Evelo, Finkers, Gonzalez-Beltran, Gray, Groth, Goble, Grethe, Heringa, Hoen, Hooft, Kuhn, Kok, Kok, Lusher, Martone, Mons, Packer, Persson, Rocca-Serra, Roos, Schaik, Sansone, Schultes, Sengstag, Slater, Strawn, Swertz, Thompson, Lei, Mulligen, Velterop, Waagmeester, Wittenburg, Wolstencroft, Zhao, Mons (CR84) 2016; 3 Durant, Leland, Henry, Nourse (CR64) 2002; 42 Rácz, Bajusz, Héberger (CR73) 2021; 26 CR38 Clark (CR41) 2019; 11 CR36 CR35 Bequignon, Bongers (CR57) 2023; 15 CR31 CR30 Kursa, Rudnicki (CR72) 2010; 30 Atas Guvenilir, Doğan (CR20) 2023; 15 Tejera, Munteanu, López-Cortés, Cabrera-Andrade, Pérez-Castillo (CR5) 2020; 25 Sicho, Liu, Svozil, Westen (CR99) 2021; 13 Janela, Bajorath (CR24) 2023; 13 Burggraaff, Lenselink, Jespers, Engelen, Bongers, Gorostiola González, Liu, Hoos, Vlijmen, IJzerman, Westen (CR15) 2020; 60 Boldini, Grisoni, Kuhn, Friedrich, Sieber (CR33) 2023; 15 Moriwaki, Tian, Kawashita, Takagi (CR61) 2018; 10 Playe, Stoven (CR19) 2020; 12 Born, Huynh, Stroobants, Cornell, Manica (CR17) 2022; 62 CR48 CR45 CR44 Minnich, McLoughlin, Tse, Deng, Weber, Murad, Madej, Ramsundar, Rush, Calad-Thomson, Brase, Allen (CR47) 2020; 60 Lanini, Santarossa, Sirockin, Lewis, Fechner, Misztela, Lewis, Maziarz, Stanley, Segler, Stiefl, Schneider (CR51) 2023 Sievers, Wilm, Dineen, Gibson, Karplus, Li, Lopez, McWilliam, Remmert, Söding, Thompson, Higgins (CR69) 2011; 7 CR59 CR58 Tropsha, Isayev, Varnek, Schneider, Cherkasov (CR12) 2023; 1 CR56 CR54 CR52 Yap (CR63) 2011; 32 Hoyt, Zdrazil, Guha, Jeliazkova, Martinez-Mayorga, Nittinger (CR42) 2023; 15 Alves, Bobrowski, Melo-Filho, Korn, Auerbach, Schmitt, Muratov, Tropsha (CR4) 2021; 40 León, Chen, Gillet (CR93) 2018; 10 Välitalo, Griffioen, Rizk, Visser, Danhof, Rao, Graaf, Hasselt (CR6) 2016; 33 Wu, Ramsundar, Feinberg, Gomes, Geniesse, Pappu, Leswing, Pande (CR29) 2018; 9 Kim, Chen, Cheng, Gindulyte, He, He, Li, Shoemaker, Thiessen, Yu, Zaslavsky, Zhang, Bolton (CR10) 2023; 51 CR68 Mei, Liao, Zhou, Li (CR67) 2005; 80 Paduszyński, Klebowski, Królikowska (CR7) 2021; 344 CR60 Tilborg, Alenicheva, Grisoni (CR32) 2022; 62 Patel, Chilton, Sartini, Gibson, Barber, Covey-Crump, Przybylak, Cronin, Madden (CR43) 2018; 58 Turon, Hlozek, Woodland, Chibale, Duran-Frigola (CR50) 2022 Mervin, Voronov, Kabeshov, Engkvist (CR53) 2024; 64 Luukkonen, Meijer, Tricarico, Hofmans, Stouten, Westen, Lenselink (CR22) 2023; 63 Jiménez-Luna, Grisoni, Weskamp, Schneider (CR3) 2021; 16 Hong, Xie, Ge, Qian, Fang, Shi, Su, Perkins, Tong (CR62) 2008; 48 Muratov, Bajorath, Sheridan, Tetko, Filimonov, Poroikov, Oprea, Baskin, Varnek, Roitberg, Isayev, Curtalolo, Fourches, Cohen, Aspuru-Guzik, Winkler, Agrafiotis, Cherkasov, Tropsha (CR1) 2020; 49 Pedregosa, Varoquaux, Gramfort, Michel, Thirion, Grisel, Blondel, Prettenhofer, Weiss, Dubourg, Vanderplas, Passos, Cournapeau, Brucher, Perrot, Duchesnay (CR74) 2011; 12 Kanev, Zhang, Kooistra, Bender, Leurs, Bailey, Würdinger, Graaf, Esch, Westerman (CR23) 2023; 19 Cherkasov, Muratov, Fourches, Varnek, Baskin, Cronin, Dearden, Gramatica, Martin, Todeschini, Consonni, Kuz’min, Cramer, Benigni, Yang, Rathman, Terfloth, Gasteiger, Richard, Tropsha (CR11) 2014; 57 CR78 Shuqi, Gao, He, Zhang, Ke (CR49) 2024; 15 CR77 CR76 CR75 Hutson (CR39) 2018; 359 CR71 Lombardo, Bentzien, Berellini, Muegge (CR94) 2024 Gramatica, Sangion (CR34) 2016; 56 CR2 Bongers, IJzerman, Van Westen (CR13) 2019; 32 CR88 CR87 CR86 CR85 Schaduangrat, Lampa, Simeon, Gleeson, Spjuth, Nantasenamat (CR40) 2020; 12 CR83 CR82 CR81 Murrell, Cortes-Ciriano, Westen, Stott, Bender, Malliavin, Glen (CR55) 2015; 7 CR80 Hellberg, Sjoestroem, Skagerberg, Wold (CR65) 1987; 30 Sosnin, Vashurina, Withnall, Karpov, Fedorov, Tetko (CR90) 2019; 38 Gorostiola González, Broek, Braun, Chatzopoulou, Jespers, IJzerman, Heitman, Westen (CR16) 2023; 15 Pillai, Dasgupta, Sudsakorn, Fretland, Mavroudis (CR8) 2022; 27 Yang, Swanson, Jin, Coley, Eiden, Gao, Guzman-Perez, Hopper, Kelley, Mathea, Palmer, Settels, Jaakkola, Jensen, Barzilay (CR27) 2019; 59 CR18 CR14 Zhao, Qin, Gou, Zhang, Yang (CR89) 2020; 108 CR98 CR97 CR96 CR95 CR92 CR91 Landrum, Riniker (CR37) 2024 Deng, Yang, Wang, Ojima, Samaras, Wang (CR28) 2023; 14 Georgiev (CR66) 2009; 16 Ramsundar, Eastman, Walters, Pande (CR46) 2019 Lopez-del Rio, Picart-Armada, Perera-Lluna (CR21) 2021; 61 CR26 CR25 Mendez, Gaulton, Bento, Chambers, De Veij, Félix, Magariños, Mosquera, Mutowo, Nowotka, Gordillo-Marañón, Hunter, Junco, Mugumbate, Rodriguez-Lopez, Atkinson, Bosc, Radoux, Segura-Cabrera, Hersey, Leach (CR9) 2019; 47 Guesné, Hanser, Werner, Boobier, Scott (CR79) 2024; 16 Katoh, Standley (CR70) 2013; 30 CT Hoyt (908_CR42) 2023; 15 908_CR2 J Jiménez-Luna (908_CR3) 2021; 16 908_CR82 SJJ Guesné (908_CR79) 2024; 16 908_CR81 908_CR80 908_CR88 908_CR87 908_CR86 908_CR85 S Hellberg (908_CR65) 1987; 30 908_CR83 P Gramatica (908_CR34) 2016; 56 J Born (908_CR17) 2022; 62 M Gorostiola González (908_CR16) 2023; 15 B Playe (908_CR19) 2020; 12 AG Georgiev (908_CR66) 2009; 16 908_CR71 Z Wu (908_CR29) 2018; 9 908_CR78 K Katoh (908_CR70) 2013; 30 908_CR77 M Hutson (908_CR39) 2018; 359 908_CR76 908_CR75 N Schaduangrat (908_CR40) 2020; 12 908_CR26 908_CR25 A Cherkasov (908_CR11) 2014; 57 OJ Bequignon (908_CR57) 2023; 15 D Mendez (908_CR9) 2019; 47 MD Wilkinson (908_CR84) 2016; 3 T Janela (908_CR24) 2023; 13 H Hong (908_CR62) 2008; 48 M Patel (908_CR43) 2018; 58 BJ Bongers (908_CR13) 2019; 32 L Shuqi (908_CR49) 2024; 15 908_CR18 Z Zhao (908_CR89) 2020; 108 J Deng (908_CR28) 2023; 14 908_CR14 RD Clark (908_CR41) 2019; 11 B Ramsundar (908_CR46) 2019 908_CR92 G Turon (908_CR50) 2022 908_CR91 M Sicho (908_CR99) 2021; 13 N Pillai (908_CR8) 2022; 27 908_CR98 908_CR97 908_CR96 908_CR95 A Lopez-del Rio (908_CR21) 2021; 61 908_CR48 H Atas Guvenilir (908_CR20) 2023; 15 K Yang (908_CR27) 2019; 59 MB Kursa (908_CR72) 2010; 30 EN Muratov (908_CR1) 2020; 49 S Kim (908_CR10) 2023; 51 VM Alves (908_CR4) 2021; 40 908_CR45 JL Durant (908_CR64) 2002; 42 908_CR44 H Mei (908_CR67) 2005; 80 GA Landrum (908_CR37) 2024 D Tilborg (908_CR32) 2022; 62 CW Yap (908_CR63) 2011; 32 908_CR38 F Lombardo (908_CR94) 2024 908_CR36 A León (908_CR93) 2018; 10 F Pedregosa (908_CR74) 2011; 12 908_CR35 DS Murrell (908_CR55) 2015; 7 908_CR31 908_CR30 L Mervin (908_CR53) 2024; 64 908_CR60 L Burggraaff (908_CR15) 2020; 60 H Moriwaki (908_CR61) 2018; 10 A Tropsha (908_CR12) 2023; 1 GK Kanev (908_CR23) 2023; 19 AJ Minnich (908_CR47) 2020; 60 908_CR68 F Sievers (908_CR69) 2011; 7 E Tejera (908_CR5) 2020; 25 PAJ Välitalo (908_CR6) 2016; 33 J Lanini (908_CR51) 2023 S Luukkonen (908_CR22) 2023; 63 908_CR59 D Boldini (908_CR33) 2023; 15 908_CR58 S Sosnin (908_CR90) 2019; 38 K Paduszyński (908_CR7) 2021; 344 A Rácz (908_CR73) 2021; 26 908_CR56 908_CR54 908_CR52 |
| References_xml | – ident: CR45 – ident: CR97 – ident: CR68 – year: 2024 ident: CR37 article-title: Combining IC50 or Ki Values from Different Sources Is a Source of Significant Noise publication-title: J Chem Inform Model doi: 10.1021/acs.jcim.4c00049 – volume: 80 start-page: 775 issue: 6 year: 2005 end-page: 786 ident: CR67 article-title: A new set of amino acid descriptors and its application in peptide QSARs publication-title: Peptide Sci doi: 10.1002/bip.20296 – volume: 30 start-page: 772 issue: 4 year: 2010 end-page: 780 ident: CR72 article-title: Feature Selection with the Boruta Package publication-title: J Stat Softw doi: 10.1093/molbev/mst010 – volume: 10 start-page: 4 issue: 1 year: 2018 ident: CR61 article-title: Mordred: a molecular descriptor calculator publication-title: J Cheminform doi: 10.1186/s13321-018-0258-y – volume: 59 start-page: 3370 issue: 8 year: 2019 end-page: 3388 ident: CR27 article-title: Analyzing learned molecular representations for property prediction publication-title: J Chem Inform Model doi: 10.1021/acs.jcim.9b00237 – volume: 15 start-page: 62 issue: 1 year: 2023 ident: CR42 article-title: Improving reproducibility and reusability in the Journal of Cheminformatics publication-title: J Cheminform doi: 10.1186/s13321-023-00730-y – ident: CR54 – ident: CR80 – volume: 344 year: 2021 ident: CR7 article-title: Predicting melting point of ionic liquids using QSPR approach: Literature review and new models publication-title: J Mol Liq doi: 10.1016/j.molliq.2021.117631 – ident: CR77 – volume: 49 start-page: 3525 issue: 11 year: 2020 end-page: 3564 ident: CR1 article-title: QSAR without borders publication-title: Chem Soc Rev doi: 10.1039/D0CS00098A – ident: CR25 – volume: 7 start-page: 45 issue: 1 year: 2015 ident: CR55 article-title: Chemically Aware Model Builder (camb): an R package for property and bioactivity modelling of small molecules publication-title: J Cheminform doi: 10.1186/s13321-015-0086-2 – volume: 30 start-page: 1126 issue: 7 year: 1987 end-page: 1135 ident: CR65 article-title: Peptide quantitative structure-activity relationships, a multivariate approach publication-title: J Med Chem doi: 10.1021/jm00390a003 – ident: CR71 – volume: 9 start-page: 513 issue: 2 year: 2018 end-page: 530 ident: CR29 article-title: MoleculeNet: a benchmark for molecular machine learning publication-title: Chem Sci doi: 10.1039/C7SC02664A – volume: 51 start-page: 1373 issue: D1 year: 2023 end-page: 1380 ident: CR10 article-title: PubChem 2023 update publication-title: Nucleic Acids Res doi: 10.1093/nar/gkac956 – volume: 12 start-page: 9 issue: 1 year: 2020 ident: CR40 article-title: Towards reproducible computational drug discovery publication-title: J Cheminform doi: 10.1186/s13321-020-0408-x – ident: CR92 – ident: CR88 – volume: 61 start-page: 1657 issue: 4 year: 2021 end-page: 1669 ident: CR21 article-title: Balancing data on deep learning-based proteochemometric activity classification publication-title: J Chem Inform Model doi: 10.1021/acs.jcim.1c00086 – volume: 19 start-page: 1011301 issue: 9 year: 2023 ident: CR23 article-title: Predicting the target landscape of kinase inhibitors using 3D convolutional neural networks publication-title: PLOS Comput Biol doi: 10.1371/journal.pcbi.1011301 – ident: CR60 – ident: CR36 – ident: CR85 – volume: 15 start-page: 3 issue: 1 year: 2023 ident: CR57 article-title: Papyrus: a large-scale curated dataset aimed at bioactivity predictions publication-title: J Cheminform doi: 10.1186/s13321-022-00672-x – volume: 16 start-page: 43 issue: 1 year: 2024 ident: CR79 article-title: Mind your prevalence! publication-title: J Cheminform doi: 10.1186/s13321-024-00837-w – volume: 63 start-page: 3688 issue: 12 year: 2023 end-page: 3696 ident: CR22 article-title: Large-scale modeling of sparse protein kinase activity data publication-title: J Chem Inform Model doi: 10.1021/acs.jcim.3c00132 – ident: CR18 – volume: 10 start-page: 26 issue: 1 year: 2018 ident: CR93 article-title: Effect of missing data on multitask prediction methods publication-title: Journal of Cheminformatics doi: 10.1186/s13321-018-0281-z – ident: CR91 – volume: 33 start-page: 856 issue: 4 year: 2016 end-page: 867 ident: CR6 article-title: Structure-Based Prediction of Anti-infective Drug Concentrations in the Human Lung Epithelial Lining Fluid publication-title: Pharmac Res doi: 10.1007/s11095-015-1832-x – ident: CR30 – volume: 47 start-page: 930 issue: D1 year: 2019 end-page: 940 ident: CR9 article-title: ChEMBL: towards direct deposition of bioassay data publication-title: Nucleic Acids Res doi: 10.1093/nar/gky1075 – volume: 48 start-page: 1337 issue: 7 year: 2008 end-page: 1344 ident: CR62 article-title: Mold2, Molecular Descriptors from 2D Structures for Chemoinformatics and Toxicoinformatics publication-title: J Chem Inform Model doi: 10.1021/ci800038f – volume: 16 start-page: 949 issue: 9 year: 2021 end-page: 959 ident: CR3 article-title: Artificial intelligence in drug discovery: recent advances and future perspectives publication-title: Exp Opin Drug Disc doi: 10.1080/17460441.2021.1909567 – ident: CR86 – volume: 38 start-page: 1800108 issue: 4 year: 2019 ident: CR90 article-title: A survey of multi-task learning methods in chemoinformatics publication-title: Mol Inform doi: 10.1002/minf.201800108 – volume: 13 start-page: 17816 issue: 1 year: 2023 ident: CR24 article-title: Rationalizing general limitations in assessing and comparing methods for compound potency prediction publication-title: Scientific Reports doi: 10.1038/s41598-023-45086-3 – volume: 57 start-page: 4977 issue: 12 year: 2014 end-page: 5010 ident: CR11 article-title: QSAR Modeling: where have you been? Where are you going to? publication-title: J Med Chem doi: 10.1021/jm4004285 – volume: 62 start-page: 5938 issue: 23 year: 2022 end-page: 5951 ident: CR32 article-title: Exposing the limitations of molecular machine learning with activity cliffs publication-title: J Chem Inform Model doi: 10.1021/acs.jcim.2c01073 – volume: 32 start-page: 1466 issue: 7 year: 2011 end-page: 1474 ident: CR63 article-title: PaDEL-descriptor: An open source software to calculate molecular descriptors and fingerprints publication-title: Journal of Computational Chemistry doi: 10.1002/jcc.21707 – ident: CR44 – volume: 42 start-page: 1273 issue: 6 year: 2002 end-page: 1280 ident: CR64 article-title: Reoptimization of MDL keys for use in drug discovery publication-title: J Chem Inform Comput Sci doi: 10.1021/ci010132r – volume: 3 issue: 1 year: 2016 ident: CR84 article-title: The FAIR Guiding Principles for scientific data management and stewardship publication-title: Sci Data doi: 10.1038/sdata.2016.18 – volume: 108 year: 2020 ident: CR89 article-title: Multi-task learning models for predicting active compounds publication-title: J Biomed Inform doi: 10.1016/j.jbi.2020.103484 – ident: CR38 – ident: CR52 – volume: 13 start-page: 73 issue: 1 year: 2021 ident: CR99 article-title: GenUI: interactive and extensible open source software platform for de novo molecular generation and cheminformatics publication-title: J Cheminform doi: 10.1186/s13321-021-00550-y – volume: 12 start-page: 2825 issue: 85 year: 2011 end-page: 2830 ident: CR74 article-title: Scikit-learn: machine learning in Python publication-title: J Mach Learn Res – year: 2023 ident: CR51 article-title: PREFER: a new predictive modeling framework for molecular discovery publication-title: J Chem Inform Model doi: 10.1021/acs.jcim.3c00523 – volume: 15 start-page: 16 issue: 1 year: 2023 ident: CR20 article-title: How to approach machine learning-based prediction of drug/compound-target interactions publication-title: J Cheminform doi: 10.1186/s13321-023-00689-w – ident: CR83 – volume: 15 start-page: 73 issue: 1 year: 2023 ident: CR33 article-title: Practical guidelines for the use of gradient boosting for molecular property prediction publication-title: J Cheminform doi: 10.1186/s13321-023-00743-7 – volume: 359 start-page: 725 issue: 6377 year: 2018 end-page: 726 ident: CR39 article-title: Artificial intelligence faces reproducibility crisis publication-title: Science (New York, N.Y.) doi: 10.1126/science.359.6377.725 – year: 2019 ident: CR46 publication-title: Deep Learning for the Life Sciences: Applying Deep Learning to Genomics, Microscopy, Drug Discovery, and More, 1st – ident: CR87 – volume: 26 start-page: 1111 issue: 4 year: 2021 ident: CR73 article-title: Effect of Dataset Size and Train/Test Split Ratios in QSAR/QSPR Multiclass Classification publication-title: Molecules doi: 10.3390/molecules26041111 – ident: CR35 – volume: 1 start-page: 1 year: 2023 end-page: 15 ident: CR12 article-title: Integrating QSAR modelling and deep learning in drug discovery: the emergence of deep QSAR publication-title: Nat Rev Drug Disc doi: 10.1038/s41573-023-00832-0 – ident: CR58 – volume: 30 start-page: 772 issue: 4 year: 2013 end-page: 780 ident: CR70 article-title: MAFFT multiple sequence alignment software version 7: improvements in performance and usability publication-title: Mol Biol Evol doi: 10.1093/molbev/mst010 – volume: 12 start-page: 11 issue: 1 year: 2020 ident: CR19 article-title: Evaluation of deep and shallow learning methods in chemogenomics for the prediction of drugs specificity publication-title: J Cheminform doi: 10.1186/s13321-020-0413-0 – volume: 60 start-page: 4283 issue: 9 year: 2020 end-page: 4295 ident: CR15 article-title: Successive statistical and structure-based modeling to identify chemically novel kinase inhibitors publication-title: J Chem Inform Model doi: 10.1021/acs.jcim.9b01204 – volume: 15 start-page: 74 issue: 1 year: 2023 ident: CR16 article-title: 3DDPDs: describing protein dynamics for proteochemometric bioactivity prediction. A case for (mutant) G protein-coupled receptors publication-title: J Cheminform doi: 10.1186/s13321-023-00745-5 – volume: 60 start-page: 1955 issue: 4 year: 2020 end-page: 1968 ident: CR47 article-title: AMPL: A Data-Driven Modeling Pipeline for Drug Discovery publication-title: J Chem Inform Model doi: 10.1021/acs.jcim.9b01053 – ident: CR96 – ident: CR75 – volume: 58 start-page: 673 issue: 3 year: 2018 end-page: 682 ident: CR43 article-title: Assessment and Reproducibility of Quantitative Structure-Activity Relationship Models by the Nonexpert publication-title: Journal of Chemical Information and Modeling doi: 10.1021/acs.jcim.7b00523 – volume: 25 start-page: 5172 issue: 21 year: 2020 ident: CR5 article-title: Drugs Repurposing Using QSAR, Docking and Molecular Dynamics for Possible Inhibitors of the SARS-CoV-2 Mpro Protease publication-title: Molecules doi: 10.3390/molecules25215172 – ident: CR78 – ident: CR81 – ident: CR26 – ident: CR95 – volume: 62 start-page: 240 issue: 2 year: 2022 end-page: 257 ident: CR17 article-title: Active Site Sequence Representations of Human Kinases Outperform Full Sequence Representations for Affinity Prediction and Inhibitor Generation: 3D Effects in a 1D Model publication-title: J Chem Inform Model doi: 10.1021/acs.jcim.1c00889 – ident: CR14 – ident: CR2 – volume: 7 start-page: 539 issue: 1 year: 2011 ident: CR69 article-title: Fast, scalable generation of high-quality protein multiple sequence alignments using Clustal Omega publication-title: Mol Syst Biol doi: 10.1038/msb.2011.75 – year: 2022 ident: CR50 article-title: First fully-automated AI/ML virtual screening cascade implemented at a drug discovery centre in Africa publication-title: BioRxiv doi: 10.1101/2022.12.13.520154 – ident: CR82 – volume: 11 start-page: 62 issue: 1 year: 2019 ident: CR41 article-title: A path to next-generation reproducibility in cheminformatics publication-title: J Cheminform. doi: 10.1186/s13321-019-0385-0 – volume: 16 start-page: 703 issue: 5 year: 2009 end-page: 723 ident: CR66 article-title: Interpretable numerical descriptors of amino acid space publication-title: J Comput Biol doi: 10.1089/cmb.2008.0173 – ident: CR56 – ident: CR98 – ident: CR48 – volume: 56 start-page: 1127 issue: 6 year: 2016 end-page: 1131 ident: CR34 article-title: A historical excursus on the statistical validation parameters for QSAR models: a clarification concerning metrics and terminology publication-title: J Chem Inform Model doi: 10.1021/acs.jcim.6b00088 – volume: 14 start-page: 6395 issue: 1 year: 2023 ident: CR28 article-title: A systematic study of key elements underlying molecular property prediction publication-title: Nat Commun doi: 10.1038/s41467-023-41948-6 – ident: CR31 – year: 2024 ident: CR94 article-title: Prediction of Human Clearance Using In Silico Models with Reduced Bias publication-title: Mol Pharm doi: 10.1021/acs.molpharmaceut.3c00812 – volume: 27 start-page: 2209 issue: 8 year: 2022 end-page: 2215 ident: CR8 article-title: Machine Learning guided early drug discovery of small molecules publication-title: Drug Disc Today doi: 10.1016/j.drudis.2022.03.017 – volume: 64 start-page: 5365 issue: 14 year: 2024 end-page: 5374 ident: CR53 article-title: QSARtuna: An Automated QSAR Modeling Platform for Molecular Property Prediction in Drug Design publication-title: J Chem Inform Model doi: 10.1021/acs.jcim.4c00457 – volume: 40 start-page: 2000113 issue: 1 year: 2021 ident: CR4 article-title: QSAR Modeling of SARS-CoV Mpro Inhibitors Identifies Sufugolix, Cenicriviroc, Proglumetacin, and other Drugs as Candidates for Repurposing against SARS-CoV-2 publication-title: Mol Inform doi: 10.1002/minf.202000113 – volume: 15 start-page: 1 year: 2024 ident: CR49 article-title: Data-driven quantum chemical property prediction leveraging 3d conformations with uni-mol+ publication-title: Nat Commun doi: 10.1038/s41467-024-51321-w – volume: 32 start-page: 89 year: 2019 end-page: 98 ident: CR13 article-title: Proteochemometrics: recent developments in bioactivity and selectivity modeling publication-title: Drug Disc Today doi: 10.1016/j.ddtec.2020.08.003 – ident: CR59 – ident: CR76 – year: 2023 ident: 908_CR51 publication-title: J Chem Inform Model doi: 10.1021/acs.jcim.3c00523 – volume-title: Deep Learning for the Life Sciences: Applying Deep Learning to Genomics, Microscopy, Drug Discovery, and More, 1st year: 2019 ident: 908_CR46 – volume: 16 start-page: 43 issue: 1 year: 2024 ident: 908_CR79 publication-title: J Cheminform doi: 10.1186/s13321-024-00837-w – year: 2024 ident: 908_CR94 publication-title: Mol Pharm doi: 10.1021/acs.molpharmaceut.3c00812 – ident: 908_CR52 – volume: 9 start-page: 513 issue: 2 year: 2018 ident: 908_CR29 publication-title: Chem Sci doi: 10.1039/C7SC02664A – volume: 14 start-page: 6395 issue: 1 year: 2023 ident: 908_CR28 publication-title: Nat Commun doi: 10.1038/s41467-023-41948-6 – volume: 59 start-page: 3370 issue: 8 year: 2019 ident: 908_CR27 publication-title: J Chem Inform Model doi: 10.1021/acs.jcim.9b00237 – volume: 15 start-page: 16 issue: 1 year: 2023 ident: 908_CR20 publication-title: J Cheminform doi: 10.1186/s13321-023-00689-w – volume: 19 start-page: 1011301 issue: 9 year: 2023 ident: 908_CR23 publication-title: PLOS Comput Biol doi: 10.1371/journal.pcbi.1011301 – volume: 48 start-page: 1337 issue: 7 year: 2008 ident: 908_CR62 publication-title: J Chem Inform Model doi: 10.1021/ci800038f – volume: 15 start-page: 74 issue: 1 year: 2023 ident: 908_CR16 publication-title: J Cheminform doi: 10.1186/s13321-023-00745-5 – ident: 908_CR14 doi: 10.1039/C4MD00216D – ident: 908_CR35 – volume: 7 start-page: 45 issue: 1 year: 2015 ident: 908_CR55 publication-title: J Cheminform doi: 10.1186/s13321-015-0086-2 – volume: 10 start-page: 4 issue: 1 year: 2018 ident: 908_CR61 publication-title: J Cheminform doi: 10.1186/s13321-018-0258-y – volume: 1 start-page: 1 year: 2023 ident: 908_CR12 publication-title: Nat Rev Drug Disc doi: 10.1038/s41573-023-00832-0 – ident: 908_CR26 doi: 10.48550/arXiv.2207.08815 – volume: 11 start-page: 62 issue: 1 year: 2019 ident: 908_CR41 publication-title: J Cheminform. doi: 10.1186/s13321-019-0385-0 – ident: 908_CR54 doi: 10.26434/chemrxiv-2023-fzqwd – ident: 908_CR38 – ident: 908_CR86 – volume: 38 start-page: 1800108 issue: 4 year: 2019 ident: 908_CR90 publication-title: Mol Inform doi: 10.1002/minf.201800108 – volume: 60 start-page: 4283 issue: 9 year: 2020 ident: 908_CR15 publication-title: J Chem Inform Model doi: 10.1021/acs.jcim.9b01204 – volume: 3 issue: 1 year: 2016 ident: 908_CR84 publication-title: Sci Data doi: 10.1038/sdata.2016.18 – volume: 10 start-page: 26 issue: 1 year: 2018 ident: 908_CR93 publication-title: Journal of Cheminformatics doi: 10.1186/s13321-018-0281-z – volume: 30 start-page: 772 issue: 4 year: 2010 ident: 908_CR72 publication-title: J Stat Softw doi: 10.1093/molbev/mst010 – volume: 15 start-page: 73 issue: 1 year: 2023 ident: 908_CR33 publication-title: J Cheminform doi: 10.1186/s13321-023-00743-7 – volume: 63 start-page: 3688 issue: 12 year: 2023 ident: 908_CR22 publication-title: J Chem Inform Model doi: 10.1021/acs.jcim.3c00132 – volume: 359 start-page: 725 issue: 6377 year: 2018 ident: 908_CR39 publication-title: Science (New York, N.Y.) doi: 10.1126/science.359.6377.725 – ident: 908_CR83 – year: 2024 ident: 908_CR37 publication-title: J Chem Inform Model doi: 10.1021/acs.jcim.4c00049 – volume: 33 start-page: 856 issue: 4 year: 2016 ident: 908_CR6 publication-title: Pharmac Res doi: 10.1007/s11095-015-1832-x – volume: 12 start-page: 2825 issue: 85 year: 2011 ident: 908_CR74 publication-title: J Mach Learn Res – volume: 27 start-page: 2209 issue: 8 year: 2022 ident: 908_CR8 publication-title: Drug Disc Today doi: 10.1016/j.drudis.2022.03.017 – volume: 62 start-page: 5938 issue: 23 year: 2022 ident: 908_CR32 publication-title: J Chem Inform Model doi: 10.1021/acs.jcim.2c01073 – ident: 908_CR97 – volume: 47 start-page: 930 issue: D1 year: 2019 ident: 908_CR9 publication-title: Nucleic Acids Res doi: 10.1093/nar/gky1075 – volume: 80 start-page: 775 issue: 6 year: 2005 ident: 908_CR67 publication-title: Peptide Sci doi: 10.1002/bip.20296 – ident: 908_CR25 doi: 10.48550/arXiv.2305.02997 – volume: 56 start-page: 1127 issue: 6 year: 2016 ident: 908_CR34 publication-title: J Chem Inform Model doi: 10.1021/acs.jcim.6b00088 – ident: 908_CR81 doi: 10.1145/3292500.3330701 – ident: 908_CR91 doi: 10.1145/2939672.2939785 – volume: 26 start-page: 1111 issue: 4 year: 2021 ident: 908_CR73 publication-title: Molecules doi: 10.3390/molecules26041111 – ident: 908_CR88 – volume: 15 start-page: 62 issue: 1 year: 2023 ident: 908_CR42 publication-title: J Cheminform doi: 10.1186/s13321-023-00730-y – ident: 908_CR36 – volume: 60 start-page: 1955 issue: 4 year: 2020 ident: 908_CR47 publication-title: J Chem Inform Model doi: 10.1021/acs.jcim.9b01053 – volume: 30 start-page: 772 issue: 4 year: 2013 ident: 908_CR70 publication-title: Mol Biol Evol doi: 10.1093/molbev/mst010 – volume: 32 start-page: 1466 issue: 7 year: 2011 ident: 908_CR63 publication-title: Journal of Computational Chemistry doi: 10.1002/jcc.21707 – ident: 908_CR75 doi: 10.26434/chemrxiv-2022-m8l33-v2 – ident: 908_CR59 – ident: 908_CR95 doi: 10.1021/acs.jcim.3c00434 – volume: 15 start-page: 1 year: 2024 ident: 908_CR49 publication-title: Nat Commun doi: 10.1038/s41467-024-51321-w – volume: 25 start-page: 5172 issue: 21 year: 2020 ident: 908_CR5 publication-title: Molecules doi: 10.3390/molecules25215172 – volume: 30 start-page: 1126 issue: 7 year: 1987 ident: 908_CR65 publication-title: J Med Chem doi: 10.1021/jm00390a003 – ident: 908_CR71 – volume: 7 start-page: 539 issue: 1 year: 2011 ident: 908_CR69 publication-title: Mol Syst Biol doi: 10.1038/msb.2011.75 – ident: 908_CR45 – ident: 908_CR68 – volume: 13 start-page: 73 issue: 1 year: 2021 ident: 908_CR99 publication-title: J Cheminform doi: 10.1186/s13321-021-00550-y – volume: 62 start-page: 240 issue: 2 year: 2022 ident: 908_CR17 publication-title: J Chem Inform Model doi: 10.1021/acs.jcim.1c00889 – ident: 908_CR44 doi: 10.1007/978-3-540-78246-9_38 – volume: 344 year: 2021 ident: 908_CR7 publication-title: J Mol Liq doi: 10.1016/j.molliq.2021.117631 – ident: 908_CR85 – volume: 16 start-page: 949 issue: 9 year: 2021 ident: 908_CR3 publication-title: Exp Opin Drug Disc doi: 10.1080/17460441.2021.1909567 – ident: 908_CR60 – ident: 908_CR56 – ident: 908_CR80 – volume: 15 start-page: 3 issue: 1 year: 2023 ident: 908_CR57 publication-title: J Cheminform doi: 10.1186/s13321-022-00672-x – volume: 51 start-page: 1373 issue: D1 year: 2023 ident: 908_CR10 publication-title: Nucleic Acids Res doi: 10.1093/nar/gkac956 – volume: 13 start-page: 17816 issue: 1 year: 2023 ident: 908_CR24 publication-title: Scientific Reports doi: 10.1038/s41598-023-45086-3 – volume: 42 start-page: 1273 issue: 6 year: 2002 ident: 908_CR64 publication-title: J Chem Inform Comput Sci doi: 10.1021/ci010132r – ident: 908_CR78 doi: 10.26434/chemrxiv-2023-3zcfl-v3 – ident: 908_CR96 – volume: 16 start-page: 703 issue: 5 year: 2009 ident: 908_CR66 publication-title: J Comput Biol doi: 10.1089/cmb.2008.0173 – volume: 108 year: 2020 ident: 908_CR89 publication-title: J Biomed Inform doi: 10.1016/j.jbi.2020.103484 – volume: 57 start-page: 4977 issue: 12 year: 2014 ident: 908_CR11 publication-title: J Med Chem doi: 10.1021/jm4004285 – ident: 908_CR31 – year: 2022 ident: 908_CR50 publication-title: BioRxiv doi: 10.1101/2022.12.13.520154 – ident: 908_CR92 doi: 10.1109/TPAMI.2021.3054719 – volume: 32 start-page: 89 year: 2019 ident: 908_CR13 publication-title: Drug Disc Today doi: 10.1016/j.ddtec.2020.08.003 – volume: 40 start-page: 2000113 issue: 1 year: 2021 ident: 908_CR4 publication-title: Mol Inform doi: 10.1002/minf.202000113 – volume: 64 start-page: 5365 issue: 14 year: 2024 ident: 908_CR53 publication-title: J Chem Inform Model doi: 10.1021/acs.jcim.4c00457 – ident: 908_CR58 doi: 10.25080/Majora-92bf1922-00a – ident: 908_CR2 doi: 10.1021/ja01062a035 – ident: 908_CR87 – ident: 908_CR98 doi: 10.1021/ci5001168 – volume: 58 start-page: 673 issue: 3 year: 2018 ident: 908_CR43 publication-title: Journal of Chemical Information and Modeling doi: 10.1021/acs.jcim.7b00523 – volume: 49 start-page: 3525 issue: 11 year: 2020 ident: 908_CR1 publication-title: Chem Soc Rev doi: 10.1039/D0CS00098A – volume: 61 start-page: 1657 issue: 4 year: 2021 ident: 908_CR21 publication-title: J Chem Inform Model doi: 10.1021/acs.jcim.1c00086 – ident: 908_CR30 doi: 10.26434/chemrxiv-2023-q11q4-v2 – ident: 908_CR76 – ident: 908_CR48 – ident: 908_CR77 doi: 10.48550/arXiv.2211.12858 – volume: 12 start-page: 11 issue: 1 year: 2020 ident: 908_CR19 publication-title: J Cheminform doi: 10.1186/s13321-020-0413-0 – ident: 908_CR82 – volume: 12 start-page: 9 issue: 1 year: 2020 ident: 908_CR40 publication-title: J Cheminform doi: 10.1186/s13321-020-0408-x – ident: 908_CR18 doi: 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| Snippet | Building reliable and robust quantitative structure–property relationship (QSPR) models is a challenging task. First, the experimental data needs to be... Building reliable and robust quantitative structure-property relationship (QSPR) models is a challenging task. First, the experimental data needs to be... Abstract Building reliable and robust quantitative structure–property relationship (QSPR) models is a challenging task. First, the experimental data needs to... |
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| SubjectTerms | Algorithms Biological activity Cheminformatics Chemistry Chemistry and Materials Science Computational Biology/Bioinformatics Computer Applications in Chemistry Data analysis Datasets Documentation and Information in Chemistry Machine learning Modelling Open source software Packages Proteochemometrics QSAR modelling QSPR modelling Reproducibility Research from the Ninth Joint Sheffield Conference on Chemoinformatics Software Task complexity Theoretical and Computational Chemistry Workflow |
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| Title | QSPRpred: a Flexible Open-Source Quantitative Structure-Property Relationship Modelling Tool |
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