{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T17:31:08Z","timestamp":1782927068705,"version":"3.54.5"},"reference-count":42,"publisher":"Springer Science and Business Media LLC","issue":"8","license":[{"start":{"date-parts":[[2016,8,1]],"date-time":"2016-08-01T00:00:00Z","timestamp":1470009600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2016,8,1]],"date-time":"2016-08-01T00:00:00Z","timestamp":1470009600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["1S10RR02664701"],"award-info":[{"award-number":["1S10RR02664701"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["5U19AI109662-02"],"award-info":[{"award-number":["5U19AI109662-02"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Comput Aided Mol Des"],"published-print":{"date-parts":[[2016,8]]},"DOI":"10.1007\/s10822-016-9938-8","type":"journal-article","created":{"date-parts":[[2016,8,24]],"date-time":"2016-08-24T08:46:36Z","timestamp":1472028396000},"page":"595-608","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1319,"title":["Molecular graph convolutions: moving beyond fingerprints"],"prefix":"10.1007","volume":"30","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4579-4388","authenticated-orcid":false,"given":"Steven","family":"Kearnes","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kevin","family":"McCloskey","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marc","family":"Berndl","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vijay","family":"Pande","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Patrick","family":"Riley","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2016,8,24]]},"reference":[{"key":"9938_CR1","unstructured":"Abadi M, Agarwal A, Barham P, Brevdo E, Chen Z, Citro C, Corrado GS, Davis A, Dean J, Devin M et al (2015) TensorFlow: large-scale machine learning on heterogeneous systems. Software. http:\/\/tensorflow.org"},{"issue":"10","key":"9938_CR2","doi-asserted-by":"publisher","first-page":"1711","DOI":"10.1002\/jcc.20681","volume":"28","author":"PJ Ballester","year":"2007","unstructured":"Ballester PJ, Richards WG (2007) Ultrafast shape recognition to search compound databases for similar molecular shapes. J Comput Chem 28(10):1711\u20131723","journal-title":"J Comput Chem"},{"key":"9938_CR3","unstructured":"Bruna J, Zaremba W, Szlam A, LeCun Y (2013) Spectral networks and locally connected networks on graphs. arXiv:1312.6203"},{"issue":"2","key":"9938_CR4","doi-asserted-by":"publisher","first-page":"64","DOI":"10.1021\/ci00046a002","volume":"25","author":"RE Carhart","year":"1985","unstructured":"Carhart RE, Smith DH, Venkataraghavan R (1985) Atom pairs as molecular features in structure-activity studies: definition and applications. J Chem Inf Comput Sci 25(2):64\u201373","journal-title":"J Chem Inf Comput Sci"},{"key":"9938_CR5","unstructured":"Dahl G (2012) Deep learning how I did it: Merck 1st place interview. http:\/\/blog.kaggle.com\/2012\/11\/01\/deep-learning-how-i-did-it-merck-1st-place-interview"},{"key":"9938_CR6","unstructured":"Dahl GE, Jaitly N, Salakhutdinov R (2014) Multi-task neural networks for QSAR predictions. arXiv:1406.1231"},{"key":"9938_CR7","unstructured":"Dieleman S (2015) Classifying plankton with deep neural networks. 17 Mar 2015. http:\/\/benanne.github.io\/2015\/03\/17\/plankton.html"},{"key":"9938_CR8","first-page":"2121","volume":"12","author":"J Duchi","year":"2011","unstructured":"Duchi J, Hazan E, Singer Y (2011) Adaptive subgradient methods for online learning and stochastic optimization. J Mach Learn Res 12:2121\u20132159","journal-title":"J Mach Learn Res"},{"key":"9938_CR9","unstructured":"Duvenaud DK, Maclaurin D, Iparraguirre J, Bombarell R, Hirzel T, Aspuru-Guzik A, Adams RP (2015) Convolutional networks on graphs for learning molecular fingerprints. In: Advances in neural information processing systems, pp 2224\u20132232"},{"issue":"22","key":"9938_CR10","doi-asserted-by":"publisher","first-page":"3219","DOI":"10.1016\/0040-4020(80)80168-2","volume":"36","author":"J Gasteiger","year":"1980","unstructured":"Gasteiger J, Marsili M (1980) Iterative partial equalization of orbital electronegativity\u2013a rapid access to atomic charges. Tetrahedron 36(22):3219\u20133228","journal-title":"Tetrahedron"},{"issue":"1","key":"9938_CR11","doi-asserted-by":"publisher","first-page":"74","DOI":"10.1021\/jm0603365","volume":"50","author":"PCD Hawkins","year":"2007","unstructured":"Hawkins PCD, Skillman AG, Nicholls A (2007) Comparison of shape-matching and docking as virtual screening tools. J Med Chem 50(1):74\u201382","journal-title":"J Med Chem"},{"key":"9938_CR12","unstructured":"Ioffe S, Szegedy C (2015) Batch normalization: accelerating deep network training by reducing internal covariate shift. arXiv:1502.03167"},{"issue":"3\u20134","key":"9938_CR13","doi-asserted-by":"publisher","first-page":"133","DOI":"10.1007\/s10822-008-9196-5","volume":"22","author":"AN Jain","year":"2008","unstructured":"Jain AN, Nicholls A (2008) Recommendations for evaluation of computational methods. J Comput Aided Mol Des 22(3\u20134):133\u2013139","journal-title":"J Comput Aided Mol Des"},{"key":"9938_CR14","unstructured":"Landrum G (2014) RDKit: open-source cheminformatics. http:\/\/www.rdkit.org"},{"issue":"7553","key":"9938_CR15","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/nature14539","volume":"521","author":"Y LeCun","year":"2015","unstructured":"LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436\u2013444","journal-title":"Nature"},{"issue":"7","key":"9938_CR16","doi-asserted-by":"publisher","first-page":"1563","DOI":"10.1021\/ci400187y","volume":"53","author":"A Lusci","year":"2013","unstructured":"Lusci A, Pollastri G, Baldi P (2013) Deep architectures and deep learning in chemoinformatics: the prediction of aqueous solubility for drug-like molecules. J Chem Inf Model 53(7):1563\u20131575","journal-title":"J Chem Inf Model"},{"issue":"2","key":"9938_CR17","doi-asserted-by":"publisher","first-page":"263","DOI":"10.1021\/ci500747n","volume":"55","author":"J Ma","year":"2015","unstructured":"Ma J, Sheridan RP, Liaw A, Dahl GE, Svetnik V (2015) Deep neural nets as a method for quantitative structure\u2013activity relationships. J Chem Inf Model 55(2):263\u2013274","journal-title":"J Chem Inf Model"},{"key":"9938_CR18","doi-asserted-by":"crossref","unstructured":"Masci J, Boscaini D, Bronstein M, Vandergheynst P (2015) Geodesic convolutional neural networks on riemannian manifolds. In: Proceedings of the IEEE international conference on computer vision workshops, pp 37\u201345","DOI":"10.1109\/ICCVW.2015.112"},{"key":"9938_CR19","first-page":"80","volume":"3","author":"A Mayr","year":"2015","unstructured":"Mayr A, Klambauer G, Unterthiner T, Hochreiter S (2015) Deeptox: toxicity prediction using deep learning. Front Environ Sci 3:80","journal-title":"Front Environ Sci"},{"issue":"1","key":"9938_CR20","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1080\/00031305.1978.10479236","volume":"32","author":"R McGill","year":"1978","unstructured":"McGill R, Tukey JW, Larsen WA (1978) Variations of box plots. Am Stat 32(1):12\u201316","journal-title":"Am Stat"},{"issue":"5","key":"9938_CR21","doi-asserted-by":"publisher","first-page":"1159","DOI":"10.1021\/ci049613b","volume":"45","author":"C Merkwirth","year":"2005","unstructured":"Merkwirth C, Lengauer T (2005) Automatic generation of complementary descriptors with molecular graph networks. J Chem Inf Model 45(5):1159\u20131168","journal-title":"J Chem Inf Model"},{"issue":"3","key":"9938_CR22","doi-asserted-by":"publisher","first-page":"498","DOI":"10.1109\/TNN.2008.2010350","volume":"20","author":"A Micheli","year":"2009","unstructured":"Micheli A (2009) Neural network for graphs: a contextual constructive approach. IEEE Trans Neural Netw 20(3):498\u2013511","journal-title":"IEEE Trans Neural Netw"},{"issue":"2","key":"9938_CR23","doi-asserted-by":"publisher","first-page":"174","DOI":"10.1111\/j.1747-0285.2006.00341.x","volume":"67","author":"SW Muchmore","year":"2006","unstructured":"Muchmore SW, Souers AJ, Akritopoulou-Zanze I (2006) The use of three-dimensional shape and electrostatic similarity searching in the identification of a melanin-concentrating hormone receptor 1 antagonist. Chem Biol Drug Des 67(2):174\u2013176","journal-title":"Chem Biol Drug Des"},{"issue":"14","key":"9938_CR24","doi-asserted-by":"publisher","first-page":"6582","DOI":"10.1021\/jm300687e","volume":"55","author":"MM Mysinger","year":"2012","unstructured":"Mysinger MM, Carchia M, Irwin JJ, Shoichet BK (2012) Directory of useful decoys, enhanced (DUD-E): better ligands and decoys for better benchmarking. J Med Chem 55(14):6582\u20136594","journal-title":"J Med Chem"},{"issue":"10","key":"9938_CR25","doi-asserted-by":"publisher","first-page":"3862","DOI":"10.1021\/jm900818s","volume":"53","author":"A Nicholls","year":"2010","unstructured":"Nicholls A, McGaughey GB, Sheridan RP, Good AC, Warren G, Mathieu M, Muchmore SW, Brown SP, Grant JA, Haigh JA et al (2010) Molecular shape and medicinal chemistry: a perspective. J Med Chem 53(10):3862\u20133886","journal-title":"J Med Chem"},{"key":"9938_CR26","unstructured":"OpenEye GraphSim Toolkit. OpenEye Scientific Software, Santa Fe, NM. http:\/\/www.eyesopen.com"},{"key":"9938_CR27","first-page":"2825","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V et al (2011) Scikit-learn: machine learning in python. J Mach Learn Res 12:2825\u20132830","journal-title":"J Mach Learn Res"},{"issue":"8","key":"9938_CR28","doi-asserted-by":"publisher","first-page":"1399","DOI":"10.1021\/cb3001028","volume":"7","author":"PM Petrone","year":"2012","unstructured":"Petrone PM, Simms B, Nigsch F, Lounkine E, Kutchukian P, Cornett A, Deng Z, Davies JW, Jenkins JL, Glick M (2012) Rethinking molecular similarity: comparing compounds on the basis of biological activity. ACS Chem Biol 7(8):1399\u20131409","journal-title":"ACS Chem Biol"},{"key":"9938_CR29","unstructured":"Ramsundar B, Kearnes S, Riley P, Webster D, Konerding D, Pande V (2015) Massively multitask networks for drug discovery. arXiv:1502.02072"},{"issue":"5","key":"9938_CR30","doi-asserted-by":"publisher","first-page":"742","DOI":"10.1021\/ci100050t","volume":"50","author":"D Rogers","year":"2010","unstructured":"Rogers D, Hahn M (2010) Extended-connectivity fingerprints. J Chem Inf Model 50(5):742\u2013754","journal-title":"J Chem Inf Model"},{"issue":"2","key":"9938_CR31","doi-asserted-by":"publisher","first-page":"169","DOI":"10.1021\/ci8002649","volume":"49","author":"SG Rohrer","year":"2009","unstructured":"Rohrer SG, Baumann K (2009) Maximum unbiased validation (MUV) data sets for virtual screening based on pubchem bioactivity data. J Chem Inf Model 49(2):169\u2013184","journal-title":"J Chem Inf Model"},{"key":"9938_CR32","doi-asserted-by":"publisher","first-page":"533","DOI":"10.1038\/323533a0","volume":"323","author":"DE Rumelhart","year":"1986","unstructured":"Rumelhart DE, Hinton GE, Williams RJ (1986) Learning representations by back-propagating errors. Nature 323:533\u2013536","journal-title":"Nature"},{"issue":"1","key":"9938_CR33","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1109\/TNN.2008.2005605","volume":"20","author":"F Scarselli","year":"2009","unstructured":"Scarselli F, Gori M, Tsoi AC, Hagenbuchner M, Monfardini G (2009) The graph neural network model. IEEE Trans Neural Netw 20(1):61\u201380","journal-title":"IEEE Trans Neural Netw"},{"key":"9938_CR34","doi-asserted-by":"crossref","unstructured":"Seabold S, Perktold J (2010) Statsmodels: econometric and statistical modeling with python. In: Proceedings of the 9th Python in science conference, pp 57\u201361","DOI":"10.25080\/Majora-92bf1922-011"},{"issue":"1","key":"9938_CR35","first-page":"1929","volume":"15","author":"N Srivastava","year":"2014","unstructured":"Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R (2014) Dropout: a simple way to prevent neural networks from overfitting. J Mach Learn Res 15(1):1929\u20131958","journal-title":"J Mach Learn Res"},{"issue":"4","key":"9938_CR36","doi-asserted-by":"publisher","first-page":"756","DOI":"10.1021\/ci8004379","volume":"49","author":"JS Swamidass","year":"2009","unstructured":"Swamidass JS, Azencott C-A, Lin T-W, Gramajo H, Tsai S-C, Baldi P (2009) Influence relevance voting: an accurate and interpretable virtual high throughput screening method. J Chem Inf Model 49(4):756\u2013766","journal-title":"J Chem Inf Model"},{"key":"9938_CR37","doi-asserted-by":"crossref","unstructured":"Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2015) Going deeper with convolutions. In: CVPR 2015. arxiv.org\/abs\/1409.4842","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"9938_CR38","doi-asserted-by":"publisher","DOI":"10.1002\/9783527628766","volume-title":"Molecular descriptors for chemoinformatics, volume 41 (2 volume set)","author":"R Todeschini","year":"2009","unstructured":"Todeschini R, Consonni V (2009) Molecular descriptors for chemoinformatics, volume 41 (2 volume set), vol 41. Wiley, New York"},{"issue":"2","key":"9938_CR39","doi-asserted-by":"publisher","first-page":"488","DOI":"10.1021\/ci600426e","volume":"47","author":"J-F Truchon","year":"2007","unstructured":"Truchon J-F, Bayly CI (2007) Evaluating virtual screening methods: good and bad metrics metrics for the \u00e2\u0102IJearly recognition\u00e2\u0102\u0130 problem. J Chem Inf Model 47(2):488\u2013508","journal-title":"J Chem Inf Model"},{"key":"9938_CR40","unstructured":"Wallach I, Dzamba M, Heifets A (2015) AtomNet: a deep convolutional neural network for bioactivity prediction in structure-based drug discovery. arXiv:1510.02855"},{"issue":"D1","key":"9938_CR41","doi-asserted-by":"publisher","first-page":"D400","DOI":"10.1093\/nar\/gkr1132","volume":"40","author":"W Yanli","year":"2012","unstructured":"Yanli W, Xiao J, Suzek TO, Zhang J, Wang J, Zhou Z, Han L, Karapetyan K, Dracheva S, Shoemaker BA et al (2012) PubChem\u2019s BioAssay database. Nucl Acids Res 40(D1):D400\u2013D412","journal-title":"Nucl Acids Res"},{"issue":"3","key":"9938_CR42","doi-asserted-by":"publisher","first-page":"338","DOI":"10.1016\/S0019-9958(65)90241-X","volume":"8","author":"LA Zadeh","year":"1965","unstructured":"Zadeh LA (1965) Fuzzy sets. Inf Control 8(3):338\u2013353","journal-title":"Inf Control"}],"container-title":["Journal of Computer-Aided Molecular Design"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10822-016-9938-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s10822-016-9938-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10822-016-9938-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,6,19]],"date-time":"2024-06-19T01:21:17Z","timestamp":1718760077000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s10822-016-9938-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016,8]]},"references-count":42,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2016,8]]}},"alternative-id":["9938"],"URL":"https:\/\/doi.org\/10.1007\/s10822-016-9938-8","relation":{},"ISSN":["0920-654X","1573-4951"],"issn-type":[{"value":"0920-654X","type":"print"},{"value":"1573-4951","type":"electronic"}],"subject":[],"published":{"date-parts":[[2016,8]]},"assertion":[{"value":"4 March 2016","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 August 2016","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 August 2016","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}