{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T18:06:47Z","timestamp":1773943607955,"version":"3.50.1"},"reference-count":46,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,2,5]],"date-time":"2026-02-05T00:00:00Z","timestamp":1770249600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,2,5]],"date-time":"2026-02-05T00:00:00Z","timestamp":1770249600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"The Government of Hong Kong Special Administrative Region of the People's Republic of China"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Comput Aided Mol Des"],"published-print":{"date-parts":[[2026,12]]},"DOI":"10.1007\/s10822-026-00761-1","type":"journal-article","created":{"date-parts":[[2026,2,5]],"date-time":"2026-02-05T03:18:41Z","timestamp":1770261521000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Comparing massively-multitask regression algorithms for drug discovery"],"prefix":"10.1007","volume":"40","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7040-5108","authenticated-orcid":false,"given":"Eric J.","family":"Martin","sequence":"first","affiliation":[]},{"given":"Xiang-Wei","family":"Zhu","sequence":"additional","affiliation":[]},{"given":"Patrick","family":"Riley","sequence":"additional","affiliation":[]},{"given":"Steven","family":"Kearnes","sequence":"additional","affiliation":[]},{"given":"Ekaterina A.","family":"Sosnina","sequence":"additional","affiliation":[]},{"given":"Li","family":"Tian","sequence":"additional","affiliation":[]},{"given":"Chi-Ming","family":"Che","sequence":"additional","affiliation":[]},{"given":"Zijian","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Ying","family":"Wei","sequence":"additional","affiliation":[]},{"given":"Thomas M.","family":"Whitehead","sequence":"additional","affiliation":[]},{"given":"Gareth J.","family":"Conduit","sequence":"additional","affiliation":[]},{"given":"Matthew D.","family":"Segall","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2026,2,5]]},"reference":[{"issue":"61","key":"761_CR1","first-page":"284","volume":"39","author":"C Hansch","year":"1963","unstructured":"Hansch C, Fujita Vol T, Roberts RB, Cowie DB, Britten R, Abelson PH, Moses V, Holm-Hansen O, Bassham JA, Calvin M, Mol Biol J, Corwin Hansch B, Fujita T (1963) Analysis. A method for the correlation of biological activity and chemical structure. J Biol Chem 39(61):284","journal-title":"J Biol Chem"},{"issue":"8","key":"761_CR2","doi-asserted-by":"publisher","first-page":"1616","DOI":"10.1021\/JA01062A035","volume":"86","author":"C Hansch","year":"1964","unstructured":"Hansch C, Fujita VT, Roberts RB, Cowie DB, Britten R, Abelson PH, Moses V, Holm-Hansen O, Bassham JA, Calvin M, Mol Biol J, Corwin Hansch B, Fujita T (1964) A method for the correlation of biological activity and chemical structure. J Am Chem Soc 86(8):1616\u20131626. https:\/\/doi.org\/10.1021\/JA01062A035","journal-title":"J Am Chem Soc"},{"issue":"1","key":"761_CR3","doi-asserted-by":"publisher","first-page":"96","DOI":"10.1021\/JA01280A022\/ASSET\/JA01280A022.FP.PNG_V03","volume":"59","author":"LP Hammett","year":"1937","unstructured":"Hammett LP (1937) The effect of structure upon the reactions of organic compounds. Benzene derivatives. J Am Chem Soc 59(1):96\u2013103. https:\/\/doi.org\/10.1021\/JA01280A022\/ASSET\/JA01280A022.FP.PNG_V03","journal-title":"J Am Chem Soc"},{"issue":"1\u20132","key":"761_CR4","doi-asserted-by":"publisher","first-page":"180","DOI":"10.1016\/S0304-4165(00)00187-2","volume":"1525","author":"M Lapinsh","year":"2001","unstructured":"Lapinsh M, Prusis P, Gutcaits A, Lundstedt T, Wikberg JES (2001) Development of proteo-chemometrics: a novel technology for the analysis of drug-receptor interactions. Biochimica et Biophysica Acta (BBA) - General Subjects 1525(1\u20132):180\u2013190. https:\/\/doi.org\/10.1016\/S0304-4165(00)00187-2","journal-title":"Biochimica et Biophysica Acta (BBA) - General Subjects"},{"issue":"8","key":"761_CR5","doi-asserted-by":"publisher","first-page":"2077","DOI":"10.1021\/acs.jcim.7b00166","volume":"57","author":"EJ Martin","year":"2017","unstructured":"Martin EJ, Polyakov VR, Tian L, Perez RC (2017) Profile-QSAR 2.0: kinase virtual screening accuracy comparable to four-concentration IC50s for realistically novel compounds. J Chem Inf Model 57(8):2077\u20132088. https:\/\/doi.org\/10.1021\/acs.jcim.7b00166","journal-title":"J Chem Inf Model"},{"issue":"4","key":"761_CR6","doi-asserted-by":"publisher","first-page":"183","DOI":"10.1007\/s10822-023-00500-w","volume":"37","author":"EA Sosnina","year":"2023","unstructured":"Sosnina EA, Sosnin S, Fedorov MV (2023) Improvement of multi-task learning by data enrichment: application for drug discovery. J Comput Aided Mol Des 37(4):183\u2013200. https:\/\/doi.org\/10.1007\/s10822-023-00500-w","journal-title":"J Comput Aided Mol Des"},{"issue":"1","key":"761_CR7","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1186\/s13321-023-00799-5","volume":"16","author":"J Wu","year":"2024","unstructured":"Wu J, Chen Y, Wu J, Zhao D, Huang J, Lin M, Wang L (2024) Large-scale comparison of machine learning methods for profiling prediction of kinase inhibitors. J Cheminform 16(1):13. https:\/\/doi.org\/10.1186\/s13321-023-00799-5","journal-title":"J Cheminform"},{"issue":"8","key":"761_CR8","doi-asserted-by":"publisher","first-page":"962","DOI":"10.1038\/s42256-024-00876-w","volume":"6","author":"B Feng","year":"2024","unstructured":"Feng B, Liu Z, Huang N, Xiao Z, Zhang H, Mirzoyan S, Xu H, Hao J, Xu Y, Zhang M, Wang S (2024) A bioactivity foundation model using pairwise meta-learning. Nat Mach Intell 6(8):962\u2013974. https:\/\/doi.org\/10.1038\/s42256-024-00876-w","journal-title":"Nat Mach Intell"},{"key":"761_CR9","unstructured":"Martin E, Sullivan D (2006) Iterative Kinase Medium-Throughput Screening with Profile-QSAR and Ensemble Surrogate AutoShim for Fast Predictive Kinase Docking without a Crystal Structure. In Keystone symposium: Structure Based Drug Discovery (D6); Burley, S. K., Shoichet, B. K., Bartlett, P. A., Eds.; Whistler, BC, Canada"},{"key":"761_CR10","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jcim.9b00375","author":"EJ Martin","year":"2019","unstructured":"Martin EJ, Polyakov VR, Zhu X-W, Tian L, Mukherjee P, Liu X (2019) All-Assay-Max2 PQSAR: activity predictions as accurate as four-concentration IC<inf>50<\/Inf>s for 8558 Novartis assays. J Chem Inf Model. https:\/\/doi.org\/10.1021\/acs.jcim.9b00375","journal-title":"J Chem Inf Model"},{"key":"761_CR11","doi-asserted-by":"publisher","DOI":"10.1021\/ACS.JCIM.9B00375","author":"EJ Martin","year":"2019","unstructured":"Martin EJ, Polyakov VR, Zhu XW, Tian L, Mukherjee P, Liu X (2019) All-Assay-Max2 PQSAR: activity predictions as accurate as four-concentration IC50s for 8558 Novartis assays. J Chem Inf Model. https:\/\/doi.org\/10.1021\/ACS.JCIM.9B00375","journal-title":"J Chem Inf Model"},{"key":"761_CR12","doi-asserted-by":"publisher","first-page":"1942","DOI":"10.1021\/ci1005004","volume":"51","author":"E Martin","year":"2011","unstructured":"Martin E, Mukherjee P, Sullivan D, Jansen J (2011) Profile-QSAR: a novel meta-QSAR method that combines activities across the kinase family to accurately predict affinity, selectivity, and cellular activity. J Chem Inf Model 51:1942\u20131956. https:\/\/doi.org\/10.1021\/ci1005004","journal-title":"J Chem Inf Model"},{"issue":"4","key":"761_CR13","doi-asserted-by":"publisher","first-page":"1603","DOI":"10.1021\/ACS.JCIM.0C01342","volume":"61","author":"EJ Martin","year":"2021","unstructured":"Martin EJ, Zhu XW (2021) Collaborative profile-QSAR: a natural platform for building collaborative models among competing companies. J Chem Inf Model 61(4):1603\u20131616. https:\/\/doi.org\/10.1021\/ACS.JCIM.0C01342","journal-title":"J Chem Inf Model"},{"key":"761_CR14","doi-asserted-by":"publisher","first-page":"82","DOI":"10.1016\/j.scriptamat.2017.11.008","volume":"146","author":"BD Conduit","year":"2018","unstructured":"Conduit BD, Jones NG, Stone HJ, Conduit GJ (2018) Probabilistic design of a molybdenum-base alloy using a neural network. Scr Mater 146:82\u201386. https:\/\/doi.org\/10.1016\/j.scriptamat.2017.11.008","journal-title":"Scr Mater"},{"key":"761_CR15","doi-asserted-by":"publisher","first-page":"358","DOI":"10.1016\/j.matdes.2017.06.007","volume":"131","author":"BD Conduit","year":"2017","unstructured":"Conduit BD, Jones NG, Stone HJ, Conduit GJ (2017) Design of a nickel-base superalloy using a neural network. Mater Des 131:358\u2013365. https:\/\/doi.org\/10.1016\/j.matdes.2017.06.007","journal-title":"Mater Des"},{"issue":"2","key":"761_CR16","doi-asserted-by":"publisher","first-page":"130","DOI":"10.1595\/205651322x16270488736796","volume":"66","author":"TM Whitehead","year":"2022","unstructured":"Whitehead TM, Chen F, Daly C, Conduit GJ (2022) Accelerating the design of automotive catalyst products using machine learning. Johnson Matthey Technol Rev 66(2):130\u2013136. https:\/\/doi.org\/10.1595\/205651322x16270488736796","journal-title":"Johnson Matthey Technol Rev"},{"issue":"5","key":"761_CR17","doi-asserted-by":"publisher","first-page":"1488","DOI":"10.1021\/acs.molpharmaceut.2c00027","volume":"19","author":"O Obrezanova","year":"2022","unstructured":"Obrezanova O, Martinsson A, Whitehead T, Mahmoud S, Bender A, Miljkovi\u0107 F, Grabowski P, Irwin B, Oprisiu I, Conduit G, Segall M, Smith GF, Williamson B, Winiwarter S, Greene N (2022) Prediction of in vivo pharmacokinetic parameters and time-exposure curves in rats using machine learning from the chemical structure. Mol Pharm 19(5):1488\u20131504. https:\/\/doi.org\/10.1021\/acs.molpharmaceut.2c00027","journal-title":"Mol Pharm"},{"issue":"22","key":"761_CR18","doi-asserted-by":"publisher","first-page":"16450","DOI":"10.1021\/acs.jmedchem.1c00313","volume":"64","author":"EG Tse","year":"2021","unstructured":"Tse EG, Aithani L, Anderson M, Cardoso-Silva J, Cincilla G, Conduit GJ, Galushka M, Guan D, Hallyburton I, Irwin BWJ, Kirk K, Lehane AM, Lindblom JCR, Lui R, Matthews S, McCulloch J, Motion A, Ng HL, \u00d6eren M, Robertson MN, Spadavecchio V, Tatsis VA, van Hoorn WP, Wade AD, Whitehead TM, Willis P, Todd MH (2021) An open drug discovery competition: experimental validation of predictive models in a series of novel antimalarials. J Med Chem 64(22):16450\u201316463. https:\/\/doi.org\/10.1021\/acs.jmedchem.1c00313","journal-title":"J Med Chem"},{"key":"761_CR19","doi-asserted-by":"publisher","DOI":"10.1002\/ail2.31","author":"BWJ Irwin","year":"2021","unstructured":"Irwin BWJ, Whitehead TM, Rowland S, Mahmoud SY, Conduit GJ, Segall MD (2021) Deep imputation on large\u2010scale drug discovery data. Appl AI Lett. https:\/\/doi.org\/10.1002\/ail2.31","journal-title":"Appl AI Lett"},{"issue":"6","key":"761_CR20","doi-asserted-by":"publisher","first-page":"2848","DOI":"10.1021\/acs.jcim.0c00443","volume":"60","author":"BWJ Irwin","year":"2020","unstructured":"Irwin BWJ, Levell JR, Whitehead TM, Segall MD, Conduit GJ (2020) Practical applications of deep learning to impute heterogeneous drug discovery data. J Chem Inf Model 60(6):2848\u20132857. https:\/\/doi.org\/10.1021\/acs.jcim.0c00443","journal-title":"J Chem Inf Model"},{"issue":"3","key":"761_CR21","doi-asserted-by":"publisher","first-page":"1197","DOI":"10.1021\/acs.jcim.8b00768","volume":"59","author":"T Whitehead","year":"2019","unstructured":"Whitehead T, Irwin B, Hunt P, Segall M, Conduit G (2019) Imputation of assay bioactivity data using deep learning. J Chem Inf Model 59(3):1197\u20131204. https:\/\/doi.org\/10.1021\/acs.jcim.8b00768","journal-title":"J Chem Inf Model"},{"key":"761_CR22","unstructured":"Stuckner J, Whitehead TM, Parini RC, Conduit GJ, Benafan O, Arnold SM (2022) Design of Materials With Alchemite. NASA Tech Memo"},{"key":"761_CR23","unstructured":"Simm J, Arany A, Zakeri P, Haber T, Wegner JK, Chupakhin V, Ceulemans H, Moreau Y (2015) Macau: Scalable Bayesian Multi-Relational Factorization with Side Information Using MCMC"},{"key":"761_CR24","doi-asserted-by":"crossref","unstructured":"Simm J, Arany A, Zakeri P, Haber T, Wegner JK, Chupakhin V, Ceulemans H, Moreau Y (2017) Macau: Scalable Bayesian Factorization with High-Dimensional Side Information Using MCMC. In 2017: IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP), pp 1\u20136","DOI":"10.1109\/MLSP.2017.8168143"},{"key":"761_CR25","unstructured":"Jain P (2013) Dhillon, I. S. Provable Inductive Matrix Completion"},{"issue":"1","key":"761_CR26","first-page":"1","volume":"7","author":"J Dem\u0161ar","year":"2006","unstructured":"Dem\u0161ar J (2006) Statistical comparisons of classifiers over multiple data sets. J Mach Learn Res 7(1):1\u201330","journal-title":"J Mach Learn Res"},{"issue":"5","key":"761_CR27","first-page":"1","volume":"17","author":"A Benavoli","year":"2016","unstructured":"Benavoli A, Corani G, Mangili F (2016) Should we really use post-hoc tests based on mean-ranks? J Mach Learn Res 17(5):1\u201310","journal-title":"J Mach Learn Res"},{"issue":"4","key":"761_CR28","doi-asserted-by":"publisher","first-page":"800","DOI":"10.1093\/biomet\/75.4.800","volume":"75","author":"Y Hochberg","year":"1988","unstructured":"Hochberg Y (1988) A sharper bonferroni procedure for multiple tests of significance. Biometrika 75(4):800\u2013802. https:\/\/doi.org\/10.1093\/biomet\/75.4.800","journal-title":"Biometrika"},{"key":"761_CR29","unstructured":"Bergstra J, Bardenet R, Bengio Y, K\u00e9gl B (2011) Algorithms for Hyper-Parameter Optimization. In Advances in Neural Information Processing Systems; Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K. Q., Eds.; Curran Associates, Inc."},{"key":"761_CR30","unstructured":"Yao H, Huang L-K, Zhang L, Wei Y, Tian L, Zou J, Huang J, Li Z (2021. Improving Generalization in Meta-Learning via Task Augmentation. In: Proceedings of the 38th International Conference on Machine Learning; Meila, M., Zhang, T., Eds.; Proceedings of Machine Learning Research; PMLR, pp 11887\u201311897"},{"key":"761_CR31","doi-asserted-by":"publisher","DOI":"10.1101\/2025.01.09.632140","author":"L Tian","year":"2025","unstructured":"Tian L, Huang HH, Martin E, Wei JY, Xu S, Huang J (2025) Transfer learning applied in predicting small molecule bioactivity. bioRxiv. https:\/\/doi.org\/10.1101\/2025.01.09.632140","journal-title":"bioRxiv"},{"key":"761_CR32","unstructured":"Paszke A, Gross S, Massa F, Lerer A, Bradbury J, Chanan G, Killeen T, Lin Z, Gimelshein N, Antiga L, Desmaison A, Kopf A, Yang E, DeVito Z, Raison M, Tejani A, Chilamkurthy S, Steiner B, Fang L, Bai J, Chintala, S (2019) PyTorch: an imperative style, high-performance deep learning library. In: advances in neural information processing systems 32; Curran Associates, Inc., pp 8024\u20138035."},{"key":"761_CR33","doi-asserted-by":"crossref","unstructured":"Salakhutdinov RMA (2008) Bayesian Probabilistic Matrix Factorization Using Markov Chain Monte Carlo. In: Proceedings of the 25th international conference on Machine learning (ICML \u201808); ACM: New York, pp 880\u2013887","DOI":"10.1145\/1390156.1390267"},{"key":"761_CR34","unstructured":"Sosnina EA, Sosnin S, Fedorov MV (2023) ImprovingMTT. GitHub repository. GitHub. https:\/\/github.com\/ekaterina-sea\/ImprovingMTT."},{"key":"761_CR35","unstructured":"Simm, J. SMURFF - Scalable Matrix Factorization Framework. https:\/\/github.com\/ExaScience\/smurff."},{"key":"761_CR36","unstructured":"Simm Jaak. SMURFF - Scalable Matrix Factorization Framework. https:\/\/smurff.readthedocs.io\/en\/release-0.16\/index.html."},{"key":"761_CR37","unstructured":"Optibrium StarDrop. https:\/\/optibrium.com\/stardrop\/."},{"key":"761_CR38","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jcim.3c00799","author":"W Heyndrickx","year":"2024","unstructured":"Heyndrickx W, Mervin L, Morawietz T, Sturm N, Friedrich L, Zalewski A, Pentina A, Humbeck L, Oldenhof M, Niwayama R, Schmidtke P, Fechner N, Simm J, Arany A, Drizard N, Jabal R, Afanasyeva A, Loeb R, Verma S, Harnqvist S, Holmes M, Pejo B, Telenczuk M, Holway N, Dieckmann A, Rieke N, Zumsande F, Clevert D-A, Krug M, Luscombe C, Green D, Ertl P, Antal P, Marcus D, Do Huu N, Fuji H, Pickett S, Acs G, Boniface E, Beck B, Sun Y, Gohier A, Rippmann F, Engkvist O, G\u00f6ller AH, Moreau Y, Galtier MN, Schuffenhauer A, Ceulemans H (2024) MELLODDY: cross-Pharma federated learning at unprecedented scale unlocks benefits in QSAR without compromising proprietary information. J Chem Inf Model. https:\/\/doi.org\/10.1021\/acs.jcim.3c00799","journal-title":"J Chem Inf Model"},{"key":"761_CR39","unstructured":"Cerella home page. https:\/\/optibrium.com\/cerella\/"},{"issue":"11","key":"761_CR40","doi-asserted-by":"publisher","first-page":"1125","DOI":"10.1007\/s10822-021-00424-3","volume":"35","author":"S Mahmoud","year":"2021","unstructured":"Mahmoud S, Irwin B, Chekmarev D, Vyas S, Kattas J, Whitehead T, Mansley T, Bikker J, Conduit G, Segall M (2021) Imputation of sensory properties using deep learning. J Comput Aided Mol Des 35(11):1125\u20131140. https:\/\/doi.org\/10.1007\/s10822-021-00424-3","journal-title":"J Comput Aided Mol Des"},{"issue":"7","key":"761_CR41","doi-asserted-by":"publisher","first-page":"2624","DOI":"10.1021\/acs.jcim.3c01695","volume":"64","author":"TM Whitehead","year":"2024","unstructured":"Whitehead TM, Strickland J, Conduit GJ, Borrel A, Mucs D, Baskerville-Abraham I (2024) Quantifying the benefits of imputation over QSAR methods in toxicology data modeling. J Chem Inf Model 64(7):2624\u20132636. https:\/\/doi.org\/10.1021\/acs.jcim.3c01695","journal-title":"J Chem Inf Model"},{"key":"761_CR42","doi-asserted-by":"publisher","unstructured":"The Theano Development Team; Al-Rfou R, Alain G, Almahairi A, Angermueller C, Bahdanau D, Ballas N, Bastien F, Bayer J, Belikov A, Belopolsky A, Bengio Y, Bergeron A, Bergstra J, Bisson V, Snyder J. B, Bouchard N, Boulanger-Lewandowski N, Bouthillier X, de Brebisson A, Breuleux O, Carrier P.-L, Cho K, Chorowski J, Christiano P, Cooijmans T, Cote M.-A, Cote M, Courville A, Dauphin Y. N, Delalleau O, Demouth J, Desjardins G, Dieleman S, Dinh L, Ducoffe M, Dumoulin V, Kahou S. E, Erhan D, Fan Z, Firat O, Germain M, Glorot X, Goodfellow I, Graham M, Gulcehre C, Hamel P, Harlouchet I, Heng J.-P, Hidasi B, Honari S, Jain A, Jean S, Jia K, Korobov M, Kulkarni V, Lamb A, Lamblin P, Larsen E, Laurent C, Lee S, Lefrancois S, Lemieux S, Leonard N, Lin Z, Livezey J. A, Lorenz C, Lowin J, Ma Q, Manzagol P.-A, Mastropietro O, McGibbon R. T, Memisevic R, van Merrienboer B, Michalski V, Mirza M, Orlandi A, Pal C, Pascanu R, Pezeshki M, Raffel C, Renshaw D, Rocklin M, Romero A, Roth M, Sadowski P, Salvatier J, Savard F, Schl\u00fcter J, Schulman J, Schwartz G, Serban I. V, Serdyuk D, Shabanian S, Simon \u00c9tienne; Spieckermann S, Subramanyam S. R, Sygnowski J, Tanguay J, van Tulder G, Turian J, Urban S, Vincent P, Visin F, de Vries H, Warde-Farley D, Webb D. J, Willson M, Xu K, Xue L, Yao L, Zhang S, Zhang Y. (2016) Theano: A Python Framework for Fast Computation of Mathematical Expressions. https:\/\/doi.org\/10.48550\/arXiv.1605.02688.","DOI":"10.48550\/arXiv.1605.02688"},{"key":"761_CR43","unstructured":"Abadi M, Barham P, Chen J, Chen Z, Davis A, Dean J, Devin M, Ghemawat S, Irving G, Isard M, et al. (2016) Tensorflow: A System for Large-Scale Machine Learning. In 12th {USENIX} Symposium on Operating Systems Design and Implementation ({OSDI} 16). 265\u2013283."},{"key":"761_CR44","doi-asserted-by":"publisher","unstructured":"Tokui S, Okuta R, Akiba T, Niitani Y, Ogawa T, Saito S, Suzuki S, Uenishi K, Vogel B, Vincent HY (2019) Chainer: A Deep Learning Framework for Accelerating the Research Cycle. https:\/\/doi.org\/10.48550\/arXiv.1908.00213.","DOI":"10.48550\/arXiv.1908.00213"},{"key":"761_CR45","unstructured":"Collobert R, Kavukcuoglu K, Farabet, C (2011) Torch7: A Matlab-like Environment for Machine Learning. In BigLearn, NIPS Workshop"},{"key":"761_CR46","doi-asserted-by":"publisher","unstructured":"Paszke A, Gross S, Massa F, Lerer A, Bradbury J, Chanan G, Killeen T, Lin Z, Gimelshein N, Antiga L, Desmaison A, Kopf A, Yang E, DeVito Z, Raison M, Tejani A, Chilamkurthy, S, Steiner B, Fang L, Bai J, Chintala S (2019) PyTorch: An Imperative Style, High-Performance Deep Learning Library. In Advances in Neural Information Processing Systems 32; Wallach, H., Larochelle, H., Beygelzimer, A., d Alch\u00e9-Buc, F., Fox, E., Garnett, R., Eds.; Curran Associates, Inc., 8024\u20138035. https:\/\/doi.org\/10.48550\/arXiv.1912.01703.","DOI":"10.48550\/arXiv.1912.01703"}],"container-title":["Journal of Computer-Aided Molecular Design"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10822-026-00761-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10822-026-00761-1","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10822-026-00761-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,5]],"date-time":"2026-02-05T03:18:50Z","timestamp":1770261530000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10822-026-00761-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2,5]]},"references-count":46,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2026,12]]}},"alternative-id":["761"],"URL":"https:\/\/doi.org\/10.1007\/s10822-026-00761-1","relation":{"has-preprint":[{"id-type":"doi","id":"10.21203\/rs.3.rs-7482715\/v1","asserted-by":"object"}]},"ISSN":["0920-654X","1573-4951"],"issn-type":[{"value":"0920-654X","type":"print"},{"value":"1573-4951","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,2,5]]},"assertion":[{"value":"28 August 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 January 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 February 2026","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 no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"58"}}