{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T04:29:23Z","timestamp":1772166563044,"version":"3.50.1"},"reference-count":56,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2023,7,25]],"date-time":"2023-07-25T00:00:00Z","timestamp":1690243200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,7,25]],"date-time":"2023-07-25T00:00:00Z","timestamp":1690243200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/100006112","name":"Microsoft Research","doi-asserted-by":"crossref","id":[{"id":"10.13039\/100006112","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/100004336","name":"Novartis","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100004336","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Cheminform"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Explainable machine learning is increasingly used in drug discovery to help rationalize compound property predictions. Feature attribution techniques are popular choices to identify which molecular substructures are responsible for a predicted property change. However, established molecular feature attribution methods have so far displayed low performance for popular deep learning algorithms such as graph neural networks (GNNs), especially when compared with simpler modeling alternatives such as random forests coupled with atom masking. To mitigate this problem, a modification of the regression objective for GNNs is proposed to specifically account for common core structures between pairs of molecules. The presented approach shows higher accuracy on a recently-proposed explainability benchmark. This methodology has the potential to assist with model explainability in drug discovery pipelines, particularly in lead optimization efforts where specific chemical series are investigated.<\/jats:p>","DOI":"10.1186\/s13321-023-00733-9","type":"journal-article","created":{"date-parts":[[2023,7,25]],"date-time":"2023-07-25T10:02:30Z","timestamp":1690279350000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Explaining compound activity predictions with a substructure-aware loss for graph neural networks"],"prefix":"10.1186","volume":"15","author":[{"given":"Kenza","family":"Amara","sequence":"first","affiliation":[]},{"given":"Raquel","family":"Rodr\u00edguez-P\u00e9rez","sequence":"additional","affiliation":[]},{"given":"Jos\u00e9","family":"Jim\u00e9nez-Luna","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,7,25]]},"reference":[{"issue":"6","key":"733_CR1","doi-asserted-by":"publisher","first-page":"1241","DOI":"10.1016\/j.drudis.2018.01.039","volume":"23","author":"H Chen","year":"2018","unstructured":"Chen H, Engkvist O, Wang Y, Olivecrona M, Blaschke T (2018) The rise of deep learning in drug discovery. Drug Discov Today 23(6):1241\u20131250","journal-title":"Drug Discov Today"},{"key":"733_CR2","doi-asserted-by":"publisher","first-page":"3525","DOI":"10.1039\/D0CS00098A","volume":"49","author":"EN Muratov","year":"2020","unstructured":"Muratov EN, Bajorath J, Sheridan RP, Tetko IV, Filimonov D, Poroikov V, Oprea TI, Baskin II, Varnek A, Roitberg A, Isayev O, Curtalolo S, Fourches D, Cohen Y, Aspuru-Guzik A, Winkler DA, Agrafiotis D, Cherkasov A, Tropsha A (2020) Qsar without borders. Chem Soc Rev 49:3525\u20133564. https:\/\/doi.org\/10.1039\/D0CS00098A","journal-title":"Chem Soc Rev"},{"key":"733_CR3","doi-asserted-by":"publisher","DOI":"10.1021\/jm4004285","author":"A Cherkasov","year":"2014","unstructured":"Cherkasov A, Muratov EN, Fourches D, Varnek A, Baskin II, Cronin M, Dearden J, Gramatica P, Martin YC, Todeschini R, Consonni V, Kuzmin VE, Cramer R, Benigni R, Yang C, Rathman J, Terfloth L, Gasteiger J, Richard A, Tropsha A (2014) QSAR modeling: where have you been? where are you going to? J Med Chem. https:\/\/doi.org\/10.1021\/jm4004285","journal-title":"J Med Chem"},{"key":"733_CR4","unstructured":"Gilmer J, Schoenholz SS, Riley PF, Vinyals O, Dahl GE (2017) Neural message passing for quantum chemistry. In: International conference on machine learning, PMLR. pp. 1263\u20131272"},{"key":"733_CR5","doi-asserted-by":"publisher","first-page":"3180","DOI":"10.1021\/acs.jcim.2c00412","volume":"62","author":"S Hamzic","year":"2022","unstructured":"Hamzic S, Lewis R, Desrayaud S, Soylu C, Fortunato M, Gr\u00e9gori G, Rodr\u00edguez-P\u00e9rez R (2022) Predicting in vivo compound brain penetration using multi-task graph neural networks. J Chem Inf Model 62:3180\u20133190","journal-title":"J Chem Inf Model"},{"key":"733_CR6","doi-asserted-by":"publisher","DOI":"10.1021\/acs.molpharmaceut.2c00680","author":"R Rodr\u00edguez-P\u00e9rez","year":"2022","unstructured":"Rodr\u00edguez-P\u00e9rez R, Trunzer M, Schneider N, Faller B, Gerebtzoff G (2022) Multispecies machine learning predictions of in vitro intrinsic clearance with uncertainty quantification analyses. Mol Pharm. https:\/\/doi.org\/10.1021\/acs.molpharmaceut.2c00680","journal-title":"Mol Pharm"},{"key":"733_CR7","doi-asserted-by":"publisher","first-page":"44","DOI":"10.3390\/molecules25010044","volume":"25","author":"F Montanari","year":"2020","unstructured":"Montanari F, Kuhnke L, Laak AT, Clevert D-A (2020) Modeling physico-chemical ADMET endpoints with multitask graph convolutional networks. Molecules 25:44","journal-title":"Molecules"},{"key":"733_CR8","doi-asserted-by":"publisher","first-page":"513","DOI":"10.1039\/C7SC02664A","volume":"9","author":"Z Wu","year":"2018","unstructured":"Wu Z, Ramsundar B, Feinberg EN, Gomes J, Geniesse C, Pappu AS, Leswing K, Pande V (2018) MoleculeNet: a benchmark for molecular machine learning. Chem Sci 9:513\u2013530","journal-title":"Chem Sci"},{"key":"733_CR9","doi-asserted-by":"publisher","first-page":"3370","DOI":"10.1021\/acs.jcim.9b00237","volume":"59","author":"K Yang","year":"2019","unstructured":"Yang K, Swanson K, Jin W, Coley C, Eiden P, Gao H, Guzman-Perez A, Hopper T, Kelley B, Mathea M, Palmer A, Settels V, Jaakkola T, Jensen K, Barzilay R (2019) Analyzing learned molecular representations for property prediction. J Chem Inf Model 59:3370\u20133388","journal-title":"J Chem Inf Model"},{"issue":"10","key":"733_CR10","doi-asserted-by":"publisher","first-page":"573","DOI":"10.1038\/s42256-020-00236-4","volume":"2","author":"J Jim\u00e9nez-Luna","year":"2020","unstructured":"Jim\u00e9nez-Luna J, Grisoni F, Schneider G (2020) Drug discovery with explainable artificial intelligence. Nat Mach Intell 2(10):573\u2013584","journal-title":"Nat Mach Intell"},{"issue":"24","key":"733_CR11","doi-asserted-by":"publisher","first-page":"17744","DOI":"10.1021\/acs.jmedchem.1c01789","volume":"64","author":"R Rodr\u00edguez-P\u00e9rez","year":"2021","unstructured":"Rodr\u00edguez-P\u00e9rez R, Bajorath J (2021) Explainable machine learning for property predictions in compound optimization. J Med Chem 64(24):17744\u201317752","journal-title":"J Med Chem"},{"key":"733_CR12","doi-asserted-by":"publisher","DOI":"10.1016\/j.ailsci.2021.100009","volume":"1","author":"R Rodr\u00edguez-P\u00e9rez","year":"2021","unstructured":"Rodr\u00edguez-P\u00e9rez R, Bajorath J (2021) Chemistry-centric explanation of machine learning models. Artif Intell Life Sci 1:100009. https:\/\/doi.org\/10.1016\/j.ailsci.2021.100009","journal-title":"Artif Intell Life Sci"},{"key":"733_CR13","doi-asserted-by":"crossref","unstructured":"Gandhi HA, White AD (2022) Explaining molecular properties with natural language","DOI":"10.26434\/chemrxiv-2022-v5p6m-v3"},{"issue":"13","key":"733_CR14","doi-asserted-by":"publisher","first-page":"3697","DOI":"10.1039\/D1SC05259D","volume":"13","author":"GP Wellawatte","year":"2022","unstructured":"Wellawatte GP, Seshadri A, White AD (2022) Model agnostic generation of counterfactual explanations for molecules. Chem Sci 13(13):3697\u20133705","journal-title":"Chem Sci"},{"issue":"1","key":"733_CR15","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13321-022-00600-z","volume":"14","author":"C Humer","year":"2022","unstructured":"Humer C, Heberle H, Montanari F, Wolf T, Huber F, Henderson R, Heinrich J, Streit M (2022) Cheminformatics model explorer (cime): Exploratory analysis of chemical model explanations. J Cheminformatics 14(1):1\u201314","journal-title":"J Cheminformatics"},{"key":"733_CR16","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jctc.2c01235","author":"GP Wellawatte","year":"2022","unstructured":"Wellawatte GP, Gandhi HA, Seshadri A, White AD (2022) A perspective on explanations of molecular prediction models. J Chem Theory Comp. https:\/\/doi.org\/10.1021\/acs.jctc.2c01235","journal-title":"J Chem Theory Comp"},{"issue":"3","key":"733_CR17","doi-asserted-by":"publisher","first-page":"447","DOI":"10.1021\/acs.jcim.1c01263","volume":"62","author":"T Harren","year":"2022","unstructured":"Harren T, Matter H, Hessler G, Rarey M, Grebner C (2022) Interpretation of structure-activity relationships in real-world drug design data sets using explainable artificial intelligence. J Chem Inf Model 62(3):447\u2013462","journal-title":"J Chem Inf Model"},{"issue":"9","key":"733_CR18","doi-asserted-by":"publisher","DOI":"10.1016\/j.isci.2022.105023","volume":"25","author":"C Feldmann","year":"2022","unstructured":"Feldmann C, Bajorath J (2022) Calculation of exact shapley values for support vector machines with Tanimoto kernel enables model interpretation. Iscience 25(9):105023","journal-title":"Iscience"},{"key":"733_CR19","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1186\/1758-2946-5-43","volume":"5","author":"S Riniker","year":"2016","unstructured":"Riniker S, Landrum G (2016) Similarity maps\u2013a visualization strategy for molecular fingerprints and machine-learning methods. J Cheminformatics 5:43. https:\/\/doi.org\/10.1186\/1758-2946-5-43","journal-title":"J Cheminformatics"},{"issue":"24","key":"733_CR20","doi-asserted-by":"publisher","first-page":"11624","DOI":"10.1073\/pnas.1820657116","volume":"116","author":"K McCloskey","year":"2019","unstructured":"McCloskey K, Taly A, Monti F, Brenner MP, Colwell LJ (2019) Using attribution to decode binding mechanism in neural network models for chemistry. Proc Natl Acad Sci USA 116(24):11624\u201311629","journal-title":"Proc Natl Acad Sci USA"},{"issue":"1","key":"733_CR21","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13321-021-00519-x","volume":"13","author":"M Matveieva","year":"2021","unstructured":"Matveieva M, Polishchuk P (2021) Benchmarks for interpretation of QSAR models. J Cheminformatics 13(1):1\u201320","journal-title":"J Cheminformatics"},{"key":"733_CR22","first-page":"5898","volume":"33","author":"B Sanchez-Lengeling","year":"2020","unstructured":"Sanchez-Lengeling B, Wei J, Lee B, Reif E, Wang P, Qian W, McCloskey K, Colwell L, Wiltschko A (2020) Evaluating attribution for graph neural networks. Adv Neural Inform Proc Syst 33:5898\u20135910","journal-title":"Adv Neural Inform Proc Syst"},{"key":"733_CR23","doi-asserted-by":"crossref","unstructured":"Rasmussen MH, Christensen DS, Jensen JH (2022) Do machines dream of atoms? A quantitative molecular benchmark for explainable AI heatmaps, ChemRxiv.","DOI":"10.26434\/chemrxiv-2022-gnq3w"},{"key":"733_CR24","doi-asserted-by":"crossref","unstructured":"Rao J, Zheng S, Yang Y (2021) Quantitative evaluation of explainable graph neural networks for molecular property prediction. arXiv preprint. arXiv:2107.04119","DOI":"10.1016\/j.patter.2022.100628"},{"issue":"2","key":"733_CR25","doi-asserted-by":"publisher","first-page":"274","DOI":"10.1021\/acs.jcim.1c01163","volume":"62","author":"J Jim\u00e9nez-Luna","year":"2022","unstructured":"Jim\u00e9nez-Luna J, Skalic M, Weskamp N (2022) Benchmarking molecular feature attribution methods with activity cliffs. J Chem Inf Model 62(2):274\u2013283","journal-title":"J Chem Inf Model"},{"issue":"4","key":"733_CR26","doi-asserted-by":"publisher","first-page":"1324","DOI":"10.1021\/acs.jcim.8b00825","volume":"59","author":"RP Sheridan","year":"2019","unstructured":"Sheridan RP (2019) Interpretation of QSAR models by coloring atoms according to changes in predicted activity: How robust is it? J Chem Inf Model 59(4):1324\u20131337","journal-title":"J Chem Inf Model"},{"key":"733_CR27","unstructured":"Wang H, Li W, Jin X, Cho K, Ji H, Han J, Burke MD (2021) Chemical-reaction-aware molecule representation learning. arXiv preprint. arXiv:2109.09888"},{"key":"733_CR28","first-page":"1138","volume":"25","author":"X Hu","year":"2021","unstructured":"Hu X, Hu Y, Vogt M, Stumpfe D, Bajorath J (2021) Mmp-cliffs: systematic identification of activity cliffs on the basis of matched molecular pairs. J Chem Inf Model 25:1138\u20131145","journal-title":"J Chem Inf Model"},{"key":"733_CR29","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1186\/s13321-021-00525-z","volume":"13","author":"D Gogishvili","year":"2021","unstructured":"Gogishvili D, Nittinger E, Margreitter C, Tyrchan C (2021) Nonadditivity in public and inhouse data: implications for drug design. J Cheminformatics 13:47","journal-title":"J Cheminformatics"},{"issue":"9","key":"733_CR30","doi-asserted-by":"publisher","first-page":"4062","DOI":"10.1021\/acs.jmedchem.5b01746","volume":"59","author":"Y Hu","year":"2016","unstructured":"Hu Y, Stumpfe D, Bajorath J (2016) Computational exploration of molecular scaffolds in medicinal chemistry. J Med Chem 59(9):4062\u20134076. https:\/\/doi.org\/10.1021\/acs.jmedchem.5b01746","journal-title":"J Med Chem"},{"issue":"1","key":"733_CR31","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/1758-2946-5-1","volume":"5","author":"A Dalke","year":"2013","unstructured":"Dalke A, Hastings J (2013) FMCS: a novel algorithm for the multiple MCS problem. J Cheminformatics 5(1):1\u20131","journal-title":"J Cheminformatics"},{"issue":"1\u201379","key":"733_CR32","first-page":"4","volume":"1","author":"G Landrum","year":"2013","unstructured":"Landrum G (2013) Release. RDKit documentation 1(1\u201379):4","journal-title":"RDKit documentation"},{"issue":"1","key":"733_CR33","doi-asserted-by":"publisher","first-page":"198","DOI":"10.1093\/nar\/gkl999","volume":"35","author":"T Liu","year":"2007","unstructured":"Liu T, Lin Y, Wen X, Jorissen RN, Gilson MK (2007) Bindingdb: a web-accessible database of experimentally determined protein-ligand binding affinities. Nucleic Acids Res 35(1):198\u2013201","journal-title":"Nucleic Acids Res"},{"key":"733_CR34","volume-title":"On outliers and activity cliffs why QSAR often disappoints","author":"GM Maggiora","year":"2006","unstructured":"Maggiora GM (2006) On outliers and activity cliffs why QSAR often disappoints. ACS Publications, Washington"},{"issue":"23","key":"733_CR35","doi-asserted-by":"publisher","first-page":"5938","DOI":"10.1021\/acs.jcim.2c01073","volume":"62","author":"D van Tilborg","year":"2022","unstructured":"van Tilborg D, Alenicheva A, Grisoni F (2022) Exposing the limitations of molecular machine learning with activity cliffs. J Chem Inf Model 62(23):5938\u20135951","journal-title":"J Chem Inf Model"},{"issue":"1","key":"733_CR36","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13321-022-00676-7","volume":"15","author":"S Tamura","year":"2023","unstructured":"Tamura S, Miyao T, Bajorath J (2023) Large-scale prediction of activity cliffs using machine and deep learning methods of increasing complexity. J Cheminformatics 15(1):1\u201311","journal-title":"J Cheminformatics"},{"key":"733_CR37","doi-asserted-by":"publisher","first-page":"3605","DOI":"10.1016\/j.bmc.2019.06.045","volume":"27","author":"D Stumpfe","year":"2019","unstructured":"Stumpfe D, Huabin H, Bajorath J (2019) Introducing a new category of activity cliffs with chemical modifications at multiple sites and rationalizing contributions of individual substitutions. Bioorg Med Chem 27:3605\u20133612","journal-title":"Bioorg Med Chem"},{"key":"733_CR38","doi-asserted-by":"publisher","first-page":"2354","DOI":"10.1021\/ci300306a","volume":"52","author":"K Heikamp","year":"2012","unstructured":"Heikamp K, Hu X, Yan A, J\u00fcrgen B (2012) Prediction of activity cliffs using support vector machines. J Chem Inf Model 52:2354\u20132365","journal-title":"J Chem Inf Model"},{"key":"733_CR39","doi-asserted-by":"publisher","first-page":"1631","DOI":"10.1021\/acs.jcim.6b00359","volume":"56","author":"D Horvath","year":"2016","unstructured":"Horvath D, Marcou G, Varnek A, Kayastha S, Vega de Leon A, J\u00fcrgen B, (2016) Prediction of activity cliffs using condensed graphs of reaction representations, descriptor recombination, support vector machine classification, and support vector regression. J Chem Inf Model 56:1631\u20131640","journal-title":"J Chem Inf Model"},{"key":"733_CR40","doi-asserted-by":"crossref","unstructured":"Simonovsky M, Komodakis N (2017) Dynamic edge-conditioned filters in convolutional neural networks on graphs. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 3693\u20133702","DOI":"10.1109\/CVPR.2017.11"},{"key":"733_CR41","doi-asserted-by":"crossref","unstructured":"Zhou B, Khosla A, Lapedriza A, Oliva A, Torralba A (2016) Learning deep features for discriminative localization. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 2921\u20132929","DOI":"10.1109\/CVPR.2016.319"},{"key":"733_CR42","unstructured":"Shrikumar A, Greenside P, Kundaje A (2017) Learning important features through propagating activation differences. In: International conference on machine learning, pp. 3145\u20133153. PMLR"},{"key":"733_CR43","unstructured":"Sundararajan M, Taly A, Yan Q (2017) Axiomatic attribution for deep networks. In: International Conference on Machine Learning, PMLR. pp. 3319\u20133328"},{"key":"733_CR44","doi-asserted-by":"crossref","unstructured":"Selvaraju RR, Cogswell M, Das A, Vedantam R, Parikh D, Batra D (2017) Grad-CAM: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE international conference on computer vision, pp. 618\u2013626","DOI":"10.1109\/ICCV.2017.74"},{"issue":"6","key":"733_CR45","doi-asserted-by":"publisher","first-page":"647","DOI":"10.4155\/fmc.11.23","volume":"3","author":"U Johansson","year":"2011","unstructured":"Johansson U, S\u00f6nstr\u00f6d C, Norinder U, Bostr\u00f6m H (2011) Trade-off between accuracy and interpretability for predictive in silico modeling. Future Med Chem 3(6):647\u2013663","journal-title":"Future Med Chem"},{"issue":"4","key":"733_CR46","doi-asserted-by":"publisher","first-page":"783","DOI":"10.1021\/ci400084k","volume":"53","author":"RP Sheridan","year":"2013","unstructured":"Sheridan RP (2013) Time-split cross-validation as a method for estimating the goodness of prospective prediction. J Chem Inf Model 53(4):783\u2013790","journal-title":"J Chem Inf Model"},{"issue":"15","key":"733_CR47","doi-asserted-by":"publisher","first-page":"2887","DOI":"10.1021\/jm9602928","volume":"39","author":"GW Bemis","year":"1996","unstructured":"Bemis GW, Murcko MA (1996) The properties of known drugs.1. molecular frameworks. J Med Chem 39(15):2887\u20132893","journal-title":"J Med Chem"},{"issue":"2","key":"733_CR48","doi-asserted-by":"crossref","first-page":"455","DOI":"10.1002\/jcc.21334","volume":"31","author":"O Trott","year":"2010","unstructured":"Trott O, Olson AJ (2010) Autodock vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J Comput Chem 31(2):455\u2013461","journal-title":"J Comput Chem"},{"issue":"22","key":"733_CR49","doi-asserted-by":"publisher","first-page":"7739","DOI":"10.1021\/jm200452d","volume":"54","author":"E Griffen","year":"2011","unstructured":"Griffen E, Leach AG, Robb GR, Warner DJ (2011) Matched molecular pairs as a medicinal chemistry tool: miniperspective. J Med Chem 54(22):7739\u20137750","journal-title":"J Med Chem"},{"key":"733_CR50","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jcim.2c00327","author":"J Park","year":"2022","unstructured":"Park J, Sung G, Lee S, Kang S, Park C (2022) Acgcn: graph convolutional networks for activity cliff prediction between matched molecular pairs. J Chem Inf Model. https:\/\/doi.org\/10.1021\/acs.jcim.2c00327","journal-title":"J Chem Inf Model"},{"key":"733_CR51","doi-asserted-by":"crossref","unstructured":"Chen D, Lin Y, Li W, Li P, Zhou J, Sun X (2020) Measuring and relieving the over-smoothing problem for graph neural networks from the topological view. In: Proceedings of the AAAI Conference on Artificial Intelligence. 34: 3438\u20133445","DOI":"10.1609\/aaai.v34i04.5747"},{"key":"733_CR52","unstructured":"Godwin J, Schaarschmidt M, Gaunt AL, Sanchez-Gonzalez A, Rubanova Y, Veli\u010dkovi\u0107 P, Kirkpatrick J, Battaglia P (2021) Simple gnn regularisation for 3d molecular property prediction and beyond. In: International conference on learning representations"},{"key":"733_CR53","first-page":"5812","volume":"33","author":"Y You","year":"2020","unstructured":"You Y, Chen T, Sui Y, Chen T, Wang Z, Shen Y (2020) Graph contrastive learning with augmentations. Adv Neural Inform Process Syst 33:5812\u20135823","journal-title":"Adv Neural Inform Process Syst"},{"key":"733_CR54","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jcim.2c00495","author":"Y Wang","year":"2022","unstructured":"Wang Y, Magar R, Liang C, Barati Farimani A (2022) Improving molecular contrastive learning via faulty negative mitigation and decomposed fragment contrast. J Chem Inf Model. https:\/\/doi.org\/10.1021\/acs.jcim.2c00495","journal-title":"J Chem Inf Model"},{"key":"733_CR55","unstructured":"St\u00e4rk H, Beaini D, Corso G, Tossou P, Dallago C, G\u00fcnnemann S, Li\u00f2 P (2022) 3d infomax improves gnns for molecular property prediction. In: International Conference on Machine Learning, PMLR. pp. 20479\u201320502"},{"key":"733_CR56","unstructured":"Zaidi S, Schaarschmidt M, Martens J, Kim H, Teh YW, Sanchez-Gonzalez A, Battaglia P, Pascanu R, Godwin J (2022) Pre-training via denoising for molecular property prediction. arXiv preprint. arXiv:2206.00133"}],"container-title":["Journal of Cheminformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13321-023-00733-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s13321-023-00733-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13321-023-00733-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,12,17]],"date-time":"2023-12-17T13:57:52Z","timestamp":1702821472000},"score":1,"resource":{"primary":{"URL":"https:\/\/jcheminf.biomedcentral.com\/articles\/10.1186\/s13321-023-00733-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,7,25]]},"references-count":56,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2023,12]]}},"alternative-id":["733"],"URL":"https:\/\/doi.org\/10.1186\/s13321-023-00733-9","relation":{"has-preprint":[{"id-type":"doi","id":"10.26434\/chemrxiv-2022-qxq56-v4","asserted-by":"object"}]},"ISSN":["1758-2946"],"issn-type":[{"value":"1758-2946","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,7,25]]},"assertion":[{"value":"4 April 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 July 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 July 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"67"}}