{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,10]],"date-time":"2026-07-10T23:15:13Z","timestamp":1783725313710,"version":"3.55.0"},"reference-count":80,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T00:00:00Z","timestamp":1783382400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100016356","name":"ENEOS Tonen General Research Development Encouragement and Scholarship Foundation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100016356","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Computers & Chemical Engineering"],"published-print":{"date-parts":[[2026,11]]},"DOI":"10.1016\/j.compchemeng.2026.109789","type":"journal-article","created":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T07:04:42Z","timestamp":1783494282000},"page":"109789","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Transfer learning-derived molecular descriptors enable reliable property prediction under extreme data scarcity"],"prefix":"10.1016","volume":"214","author":[{"given":"Yuya","family":"Murakami","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.compchemeng.2026.109789_bib0001","unstructured":"Ahmad, W., Simon, E., Chithrananda, S., Grand, G., Ramsundar, B., 2022. ChemBERTa-2: towards chemical foundation models. arXiv [cs.LG]. https:\/\/doi.org\/10.48550\/arXiv.2209.01712."},{"key":"10.1016\/j.compchemeng.2026.109789_bib0002","unstructured":"Anderson, B., Hy, T.S., Kondor, R., 2019. Cormorant: covariant molecular neural networks. presented at the advances in neural information Processing Systems 32, pp. 14537\u201314546."},{"key":"10.1016\/j.compchemeng.2026.109789_bib0003","series-title":"33. Presented at the Advances in Neural Information Processing Systems","first-page":"12449","article-title":"wav2vec 2.0: A framework for self-supervised learning of speech representations","author":"Baevski","year":"2020"},{"key":"10.1016\/j.compchemeng.2026.109789_bib0004","doi-asserted-by":"crossref","first-page":"423","DOI":"10.1109\/TPAMI.2018.2798607","article-title":"Multimodal machine learning: a survey and taxonomy","volume":"41","author":"Baltrusaitis","year":"2019","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.compchemeng.2026.109789_bib0005","doi-asserted-by":"crossref","first-page":"509","DOI":"10.1126\/science.286.5439.509","article-title":"Emergence of scaling in random networks","volume":"286","author":"Barabasi","year":"1999","journal-title":"Science"},{"key":"10.1016\/j.compchemeng.2026.109789_bib0006","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1016\/S0169-7552(98)00110-X","article-title":"The anatomy of a large-scale hypertextual web search engine","volume":"30","author":"Brin","year":"1998","journal-title":"Comput. Netw. ISDN Syst."},{"key":"10.1016\/j.compchemeng.2026.109789_bib0007","doi-asserted-by":"crossref","first-page":"547","DOI":"10.1038\/s41586-018-0337-2","article-title":"Machine learning for molecular and materials science","volume":"559","author":"Butler","year":"2018","journal-title":"Nature"},{"key":"10.1016\/j.compchemeng.2026.109789_bib0008","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1021\/ci00046a002","article-title":"Atom pairs as molecular features in structure-activity studies: definition and applications","volume":"25","author":"Carhart","year":"1985","journal-title":"J. Chem. Inf. Comput. Sci."},{"key":"10.1016\/j.compchemeng.2026.109789_bib0009","doi-asserted-by":"crossref","first-page":"3564","DOI":"10.1021\/acs.chemmater.9b01294","article-title":"Graph networks as a universal machine learning framework for molecules and crystals","volume":"31","author":"Chen","year":"2019","journal-title":"Chem. Mater."},{"key":"10.1016\/j.compchemeng.2026.109789_bib0010","unstructured":"Chithrananda, S., Grand, G., Ramsundar, B., 2020. ChemBERTa: large-scale self-supervised pretraining for molecular property prediction. arXiv [cs.LG]. https:\/\/doi.org\/10.48550\/arXiv.2010.09885."},{"key":"10.1016\/j.compchemeng.2026.109789_bib0011","doi-asserted-by":"crossref","first-page":"218","DOI":"10.1016\/j.commatsci.2012.02.005","article-title":"AFLOW: an automatic framework for high-throughput materials discovery","volume":"58","author":"Curtarolo","year":"2012","journal-title":"Comput. Mater. Sci."},{"key":"10.1016\/j.compchemeng.2026.109789_bib0012","unstructured":"Devlin, J., Chang, M.-W., Lee, K., Toutanova, K., 2018. BERT: pre-training of deep bidirectional transformers for language understanding. arXiv [cs.CL]."},{"key":"10.1016\/j.compchemeng.2026.109789_bib0013","doi-asserted-by":"crossref","unstructured":"Doimo, D., Glielmo, A., Goldt, S., Laio, A., 2021. Redundant representations help generalization in wide neural networks. arXiv [stat.ML].","DOI":"10.52202\/068431-1429"},{"key":"10.1016\/j.compchemeng.2026.109789_bib0014","unstructured":"Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., Houlsby, N., 2020. An image is worth 16x16 words: transformers for image recognition at scale. arXiv [cs.CV]."},{"key":"10.1016\/j.compchemeng.2026.109789_bib0015","doi-asserted-by":"crossref","first-page":"1273","DOI":"10.1021\/ci010132r","article-title":"Reoptimization of MDL keys for use in drug discovery","volume":"42","author":"Durant","year":"2002","journal-title":"J. Chem. Inf. Comput. Sci."},{"key":"10.1016\/j.compchemeng.2026.109789_bib0016","series-title":"The properties of gases and liquids, sixth edition","author":"Elliott","year":"2023"},{"key":"10.1016\/j.compchemeng.2026.109789_bib0017","doi-asserted-by":"crossref","first-page":"3714","DOI":"10.1021\/jm000942e","article-title":"Fast calculation of molecular polar surface area as a sum of fragment-based contributions and its application to the prediction of drug transport properties","volume":"43","author":"Ertl","year":"2000","journal-title":"J. Med. Chem."},{"key":"10.1016\/j.compchemeng.2026.109789_bib0018","doi-asserted-by":"crossref","first-page":"5255","DOI":"10.1021\/acs.jctc.7b00577","article-title":"Prediction errors of molecular machine learning models lower than hybrid DFT error","volume":"13","author":"Faber","year":"2017","journal-title":"J. Chem. Theory Comput."},{"key":"10.1016\/j.compchemeng.2026.109789_bib0019","unstructured":"Fabian, B., Edlich, T., Gaspar, H., Segler, M., Meyers, J., Fiscato, M., Ahmed, M., 2020. Molecular representation learning with language models and domain-relevant auxiliary tasks. arXiv [cs.LG]. https:\/\/doi.org\/10.48550\/arXiv.2011.13230."},{"key":"10.1016\/j.compchemeng.2026.109789_bib0020","unstructured":"Fey, M., Lenssen, J.E., 2019. Fast graph representation learning with PyTorch Geometric. arXiv [cs.LG]."},{"key":"10.1016\/j.compchemeng.2026.109789_bib0021","unstructured":"Fey, M., Sunil, J., Nitta, A., Puri, R., Shah, M., Stojanovi\u010d, B., Bendias, R., Barghi, A., Kocijan, V., Zhang, Z., He, X., Lenssen, J.E., Leskovec, J., 2025. PyG 2.0: scalable learning on real world graphs. arXiv [cs.LG]."},{"key":"10.1016\/j.compchemeng.2026.109789_bib0022","doi-asserted-by":"crossref","first-page":"1086","DOI":"10.1002\/aic.690210607","article-title":"Group-contribution estimation of activity coefficients in nonideal liquid mixtures","volume":"21","author":"Fredenslund","year":"1975","journal-title":"AIChE Journal"},{"key":"10.1016\/j.compchemeng.2026.109789_bib0023","unstructured":"Gasteiger, J., Gro\u00df, J., G\u00fcnnemann, S., 2020. Directional message passing for molecular graphs. arXiv [cs.LG]."},{"key":"10.1016\/j.compchemeng.2026.109789_bib0024","unstructured":"Gilmer, J., Schoenholz, S.S., Riley, P.F., Vinyals, O., Dahl, G.E., 2017. Neural message passing for quantum chemistry. arXiv [cs.LG]."},{"key":"10.1016\/j.compchemeng.2026.109789_bib0025","doi-asserted-by":"crossref","first-page":"1085","DOI":"10.1103\/RevModPhys.71.1085","article-title":"Linear scaling electronic structure methods","volume":"71","author":"Goedecker","year":"1999","journal-title":"Rev. Mod. Phys."},{"key":"10.1016\/j.compchemeng.2026.109789_bib0026","doi-asserted-by":"crossref","first-page":"1291","DOI":"10.1002\/jcc.24764","article-title":"Deep learning for computational chemistry","volume":"38","author":"Goh","year":"2017","journal-title":"J. Comput. Chem."},{"key":"10.1016\/j.compchemeng.2026.109789_bib0027","doi-asserted-by":"crossref","unstructured":"Guo, Z., Zhang, C., Yu, W., Herr, J., Wiest, O., Jiang, M., Chawla, N.V., 2021. Few-shot graph learning for molecular property prediction. arXiv [cs.LG]. https:\/\/doi.org\/10.1145\/3442381.3450112.","DOI":"10.1145\/3442381.3450112"},{"key":"10.1016\/j.compchemeng.2026.109789_bib0028","unstructured":"Hamilton, W.L., Ying, Z., Leskovec, J., 2017. Inductive representation learning on large graphs. Neural inf process syst abs\/1706.02216."},{"key":"10.1016\/j.compchemeng.2026.109789_bib0029","series-title":"Hansen solubility parameters","author":"Hansen","year":"2007"},{"key":"10.1016\/j.compchemeng.2026.109789_bib0030","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1021\/acs.jcim.3c01250","article-title":"Chemprop: A machine learning package for chemical property prediction","volume":"64","author":"Heid","year":"2024","journal-title":"J. Chem. Inf. Model."},{"key":"10.1016\/j.compchemeng.2026.109789_bib0031","doi-asserted-by":"crossref","first-page":"417","DOI":"10.1037\/h0071325","article-title":"Analysis of a complex of statistical variables into principal components","volume":"24","author":"Hotelling","year":"1933","journal-title":"J. Educ. Psychol."},{"key":"10.1016\/j.compchemeng.2026.109789_bib0032","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1021\/acs.jcim.7b00616","article-title":"Mol2vec: Unsupervised machine learning approach with chemical intuition","volume":"58","author":"Jaeger","year":"2018","journal-title":"J. Chem. Inf. Model."},{"key":"10.1016\/j.compchemeng.2026.109789_bib0033","doi-asserted-by":"crossref","first-page":"5037","DOI":"10.1002\/cpe.3505","article-title":"FireWorks: a dynamic workflow system designed for high-throughput applications","volume":"27","author":"Jain","year":"2015","journal-title":"Concurr. Comput."},{"key":"10.1016\/j.compchemeng.2026.109789_bib0034","doi-asserted-by":"crossref","DOI":"10.1063\/1.4812323","article-title":"Commentary: The Materials Project: A materials genome approach to accelerating materials innovation","volume":"1","author":"Jain","year":"2013","journal-title":"APL Mater"},{"key":"10.1016\/j.compchemeng.2026.109789_bib0035","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1080\/00986448708960487","article-title":"Estimation of pure-component properties from group-contributions","volume":"57","author":"Joback","year":"1987","journal-title":"Chem. Eng. Commun."},{"key":"10.1016\/j.compchemeng.2026.109789_bib0036","article-title":"Principal component analysis: a review and recent developments","volume":"374","author":"Jolliffe","year":"2016","journal-title":"Philos. Trans. A Math. Phys. Eng. Sci."},{"key":"10.1016\/j.compchemeng.2026.109789_bib0037","doi-asserted-by":"crossref","first-page":"122","DOI":"10.1016\/j.neunet.2023.03.034","article-title":"Few-shot molecular property prediction via Hierarchically Structured learning on relation graphs","volume":"163","author":"Ju","year":"2023","journal-title":"Neural Netw"},{"key":"10.1016\/j.compchemeng.2026.109789_bib0038","doi-asserted-by":"crossref","first-page":"422","DOI":"10.1038\/s42254-021-00314-5","article-title":"Physics-informed machine learning","volume":"3","author":"Karniadakis","year":"2021","journal-title":"Nat. Rev. Phys."},{"key":"10.1016\/j.compchemeng.2026.109789_bib0039","doi-asserted-by":"crossref","DOI":"10.1002\/aic.70450","article-title":"SigmaFormer: augmenting transformer encoders with COSMO sigma profiles for pure component property prediction","author":"Kim","year":"2026","journal-title":"AIChE J"},{"key":"10.1016\/j.compchemeng.2026.109789_bib0040","unstructured":"Kipf, T.N., Welling, M., 2016. Semi-supervised classification with graph convolutional networks. arXiv [cs.LG]."},{"key":"10.1016\/j.compchemeng.2026.109789_bib0041","doi-asserted-by":"crossref","first-page":"A1133","DOI":"10.1103\/PhysRev.140.A1133","article-title":"Self-consistent equations including exchange and correlation effects","volume":"140","author":"Kohn","year":"1965","journal-title":"Phys. Rev."},{"key":"10.1016\/j.compchemeng.2026.109789_bib0042","doi-asserted-by":"crossref","first-page":"1506","DOI":"10.1021\/ci200096q","article-title":"ThermoData Engine (TDE): software implementation of the dynamic data evaluation concept. 6. Dynamic web-based data dissemination through the NIST web Thermo Tables","volume":"51","author":"Kroenlein","year":"2011","journal-title":"J. Chem. Inf. Model."},{"key":"10.1016\/j.compchemeng.2026.109789_bib0043","unstructured":"Landrum, G., n.d. RDKit: A software suite for cheminformatics, computational chemistry, and predictive modeling."},{"key":"10.1016\/j.compchemeng.2026.109789_bib0044","doi-asserted-by":"crossref","DOI":"10.1016\/j.commatsci.2021.110314","article-title":"Transfer learning for materials informatics using crystal graph convolutional neural network","volume":"190","author":"Lee","year":"2021","journal-title":"Comput. Mater. Sci."},{"key":"10.1016\/j.compchemeng.2026.109789_bib0045","unstructured":"Liu, Y., Ding, S., Zhou, S., Fan, W., Tan, Q., 2024. MolecularGPT: open large language model (LLM) for few-shot molecular property prediction. arXiv [q-bio.QM]."},{"key":"10.1016\/j.compchemeng.2026.109789_bib0046","doi-asserted-by":"crossref","first-page":"1066","DOI":"10.1021\/acs.jcim.0c01224","article-title":"Transferable multilevel attention neural network for accurate prediction of quantum chemistry properties via multitask learning","volume":"61","author":"Liu","year":"2021","journal-title":"J. Chem. Inf. Model."},{"key":"10.1016\/j.compchemeng.2026.109789_bib0047","doi-asserted-by":"crossref","unstructured":"Lu, C., Liu, Q., Wang, C., Huang, Z., Lin, P., He, L., 2019. Molecular property prediction: A multilevel quantum interactions modeling perspective. arXiv [physics.comp-ph].","DOI":"10.1609\/aaai.v33i01.33011052"},{"key":"10.1016\/j.compchemeng.2026.109789_bib0048","unstructured":"Lundberg, S., Lee, S.-I., 2017. A unified approach to interpreting model predictions. arXiv [cs.AI]."},{"key":"10.1016\/j.compchemeng.2026.109789_bib0049","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1038\/s42256-019-0138-9","article-title":"From local explanations to global understanding with explainable AI for trees","volume":"2","author":"Lundberg","year":"2020","journal-title":"Nat. Mach. Intell."},{"key":"10.1016\/j.compchemeng.2026.109789_bib0050","unstructured":"Lundberg, S.M., Erion, G.G., Lee, S.-I., 2018. Consistent individualized feature attribution for tree ensembles. arXiv [cs.LG]."},{"key":"10.1016\/j.compchemeng.2026.109789_bib0051","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1021\/c160017a018","article-title":"The generation of a unique machine description for chemical structures-a technique developed at chemical abstracts service","volume":"5","author":"Morgan","year":"1965","journal-title":"J. Chem. Doc."},{"key":"10.1016\/j.compchemeng.2026.109789_bib0052","doi-asserted-by":"crossref","unstructured":"Nanda, V., Speicher, T., Dickerson, J.P., Feizi, S., Gummadi, K.P., Weller, A., 2023. Diffused redundancy in pre-trained representations. arXiv [cs.LG].","DOI":"10.52202\/075280-0177"},{"key":"10.1016\/j.compchemeng.2026.109789_bib0053","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1016\/j.fluid.2004.09.001","article-title":"Estimation of pure component properties","volume":"226","author":"Nannoolal","year":"2004","journal-title":"Fluid Phase Equilib"},{"key":"10.1016\/j.compchemeng.2026.109789_bib0054","doi-asserted-by":"crossref","DOI":"10.1007\/s11704-024-40791-2","article-title":"Factor-wise disentangled contrastive learning for cross-domain few-shot molecular property prediction","volume":"19","author":"Ni","year":"2025","journal-title":"Front. Comput. Sci."},{"key":"10.1016\/j.compchemeng.2026.109789_bib0055","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1021\/ci00054a008","article-title":"Topological torsion: a new molecular descriptor for SAR applications. Comparison with other descriptors","volume":"27","author":"Nilakantan","year":"1987","journal-title":"J. Chem. Inf. Comput. Sci."},{"key":"10.1016\/j.compchemeng.2026.109789_bib0056","doi-asserted-by":"crossref","DOI":"10.1002\/aic.18923","article-title":"Predicting both thermodynamic and kinetic properties of crystallizing molecules via transformer-based language model","volume":"71","author":"Pahari","year":"2025","journal-title":"AIChE J"},{"key":"10.1016\/j.compchemeng.2026.109789_bib0057","unstructured":"Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., K\u00f6pf, 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. arXiv [cs.LG]."},{"key":"10.1016\/j.compchemeng.2026.109789_bib0058","doi-asserted-by":"crossref","first-page":"235","DOI":"10.1108\/eb024099","article-title":"Installation and operational experiences with MACCS (Molecular Access System)","volume":"6","author":"Polton","year":"1982","journal-title":"Online Rev"},{"key":"10.1016\/j.compchemeng.2026.109789_bib0059","doi-asserted-by":"crossref","first-page":"23940","DOI":"10.1021\/acsomega.4c02147","article-title":"Meta learning with attention based FP-GNNs for few-shot molecular property prediction","volume":"9","author":"Qian","year":"2024","journal-title":"ACS Omega"},{"key":"10.1016\/j.compchemeng.2026.109789_bib0060","unstructured":"Radford, A., Kim, J.W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., Krueger, G., Sutskever, I., 2021. Learning transferable visual models from natural language supervision. arXiv [cs.CV]."},{"key":"10.1016\/j.compchemeng.2026.109789_bib0061","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/sdata.2014.22","article-title":"Quantum chemistry structures and properties of 134 kilo molecules","volume":"1","author":"Ramakrishnan","year":"2014","journal-title":"Scientific Data"},{"key":"10.1016\/j.compchemeng.2026.109789_bib0062","doi-asserted-by":"crossref","first-page":"742","DOI":"10.1021\/ci100050t","article-title":"Extended-connectivity fingerprints","volume":"50","author":"Rogers","year":"2010","journal-title":"J. Chem. Inf. Model."},{"key":"10.1016\/j.compchemeng.2026.109789_bib0063","doi-asserted-by":"crossref","first-page":"1256","DOI":"10.1038\/s42256-022-00580-7","article-title":"Large-scale chemical language representations capture molecular structure and properties","volume":"4","author":"Ross","year":"2022","journal-title":"Nat. Mach. Intell."},{"key":"10.1016\/j.compchemeng.2026.109789_bib0064","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1109\/TNN.2008.2005605","article-title":"The graph neural network model","volume":"20","author":"Scarselli","year":"2009","journal-title":"IEEE Trans. Neural Netw."},{"key":"10.1016\/j.compchemeng.2026.109789_bib0065","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41524-019-0221-0","article-title":"Recent advances and applications of machine learning in solid-state materials science","volume":"5","author":"Schmidt","year":"2019","journal-title":"Npj Comput. Mater."},{"key":"10.1016\/j.compchemeng.2026.109789_bib0066","unstructured":"Sch\u00fctt, K.T., Kindermans, P.-J., Sauceda, H.E., Chmiela, S., Tkatchenko, A., M\u00fcller, K.-R., 2017. SchNet: A continuous-filter convolutional neural network for modeling quantum interactions. arXiv [stat.ML]."},{"key":"10.1016\/j.compchemeng.2026.109789_bib0067","unstructured":"Sch\u00fctt, K.T., Unke, O.T., Gastegger, M., 2021. Equivariant message passing for the prediction of tensorial properties and molecular spectra. arXiv [cs.LG]."},{"key":"10.1016\/j.compchemeng.2026.109789_bib0068","doi-asserted-by":"crossref","first-page":"8682","DOI":"10.1021\/acsomega.7b01464","article-title":"JRgui: A Python program of Joback and Reid method","volume":"2","author":"Shi","year":"2017","journal-title":"ACS Omega"},{"key":"10.1016\/j.compchemeng.2026.109789_bib0069","doi-asserted-by":"crossref","first-page":"3192","DOI":"10.1039\/C6SC05720A","article-title":"ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost","volume":"8","author":"Smith","year":"2017","journal-title":"Chem. Sci."},{"key":"10.1016\/j.compchemeng.2026.109789_bib0070","doi-asserted-by":"crossref","DOI":"10.1063\/1.5099132","article-title":"Message-passing neural networks for high-throughput polymer screening","volume":"150","author":"St John","year":"2019","journal-title":"J. Chem. Phys."},{"key":"10.1016\/j.compchemeng.2026.109789_bib0071","doi-asserted-by":"crossref","unstructured":"Tan, C., Sun, F., Kong, T., Zhang, W., Yang, C., Liu, C., 2018. A survey on deep transfer learning. arXiv [cs.LG].","DOI":"10.1007\/978-3-030-01424-7_27"},{"key":"10.1016\/j.compchemeng.2026.109789_bib0072","series-title":"Chemical Graph Theory","author":"Trinajstic","year":"1992"},{"key":"10.1016\/j.compchemeng.2026.109789_bib0073","doi-asserted-by":"crossref","DOI":"10.1016\/j.cej.2023.144874","article-title":"Beyond group additivity: transfer learning for molecular thermochemistry prediction","volume":"472","author":"Ureel","year":"2023","journal-title":"Chem. Eng. J."},{"key":"10.1016\/j.compchemeng.2026.109789_bib0074","unstructured":"Veli\u010dkovi\u0107, P., Cucurull, G., Casanova, A., Romero, A., Li\u00f2, P., Bengio, Y., 2017. Graph Attention Networks. arXiv [stat.ML]."},{"key":"10.1016\/j.compchemeng.2026.109789_bib0075","series-title":"Social Network Analysis","author":"Wasserman","year":"2012"},{"key":"10.1016\/j.compchemeng.2026.109789_bib0076","doi-asserted-by":"crossref","first-page":"868","DOI":"10.1021\/ci990307l","article-title":"Prediction of physicochemical parameters by atomic contributions","volume":"39","author":"Wildman","year":"1999","journal-title":"J. Chem. Inf. Comput. Sci."},{"key":"10.1016\/j.compchemeng.2026.109789_bib0077","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1109\/TNNLS.2020.2978386","article-title":"A comprehensive survey on graph neural networks","volume":"32","author":"Wu","year":"2021","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"10.1016\/j.compchemeng.2026.109789_bib0078","doi-asserted-by":"crossref","unstructured":"Xie, T., Grossman, J.C., 2017. Crystal graph convolutional neural networks for an accurate and interpretable prediction of material properties. arXiv [cond-mat.mtrl-sci].","DOI":"10.1103\/PhysRevLett.120.145301"},{"key":"10.1016\/j.compchemeng.2026.109789_bib0079","doi-asserted-by":"crossref","first-page":"3370","DOI":"10.1021\/acs.jcim.9b00237","article-title":"Analyzing learned molecular representations for property prediction","volume":"59","author":"Yang","year":"2019","journal-title":"J. Chem. Inf. Model."},{"key":"10.1016\/j.compchemeng.2026.109789_bib0080","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1016\/j.aiopen.2021.01.001","article-title":"Graph neural networks: a review of methods and applications","volume":"1","author":"Zhou","year":"2020","journal-title":"AI Open"}],"container-title":["Computers & Chemical Engineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0098135426002425?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0098135426002425?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,7,10]],"date-time":"2026-07-10T22:47:36Z","timestamp":1783723656000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0098135426002425"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,11]]},"references-count":80,"alternative-id":["S0098135426002425"],"URL":"https:\/\/doi.org\/10.1016\/j.compchemeng.2026.109789","relation":{},"ISSN":["0098-1354"],"issn-type":[{"value":"0098-1354","type":"print"}],"subject":[],"published":{"date-parts":[[2026,11]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Transfer learning-derived molecular descriptors enable reliable property prediction under extreme data scarcity","name":"articletitle","label":"Article Title"},{"value":"Computers & Chemical Engineering","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.compchemeng.2026.109789","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 The Author(s). Published by Elsevier Ltd.","name":"copyright","label":"Copyright"}],"article-number":"109789"}}