FusionCLM: enhanced molecular property prediction via knowledge fusion of chemical language models
Chemical Language Models (CLMs) have demonstrated capabilities in extracting patterns and predicting from vast volume of the Simplified Molecular Input Line Entry System (SMILES), a notation used to represent molecular structures. Different CLMs, developed from various architectures, can provide uni...
Uloženo v:
| Vydáno v: | Journal of cheminformatics Ročník 17; číslo 1; s. 133 - 12 |
|---|---|
| Hlavní autoři: | , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Cham
Springer International Publishing
29.08.2025
BioMed Central Ltd Springer Nature B.V BMC |
| Témata: | |
| ISSN: | 1758-2946, 1758-2946 |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Abstract | Chemical Language Models (CLMs) have demonstrated capabilities in extracting patterns and predicting from vast volume of the Simplified Molecular Input Line Entry System (SMILES), a notation used to represent molecular structures. Different CLMs, developed from various architectures, can provide unique insights into molecular properties. To harness the uniqueness of different CLMs, we propose FusionCLM, a novel stacking-ensemble learning algorithm that integrate the outputs of multiple CLMs into a unified framework. FusionCLM first generates SMILES embeddings, predictions, and losses from each CLM. Auxiliary models are trained on these first-level predictions and embeddings to estimate test losses during inference. The losses and predictions are then concatenated to create an integrated feature matrix, which trains second-level meta-models for final predictions. Empirical testing on five datasets demonstrates that FusionCLM have better performance than individual CLM at the first level and three advanced multimodal deep learning frameworks, showcasing FusionCLM’s potential in advancing molecular property prediction.
Scientific Contribution
FusionCLM uses the stacking-ensemble learning method that integrates unique representation learning from multiple CLMs, allowing a more comprehensive learning of molecular SMILES data. This results in providing more accurate molecular property prediction, which can help in facilitating early discovery and development of promising drug candidates. By evaluating and comparing its performance against individual CLMs and existing multimodal deep learning frameworks, FusionCLM demonstrates improvements in prediction accuracy, distinguishing itself from prior models in this domain. |
|---|---|
| AbstractList | Chemical Language Models (CLMs) have demonstrated capabilities in extracting patterns and predicting from vast volume of the Simplified Molecular Input Line Entry System (SMILES), a notation used to represent molecular structures. Different CLMs, developed from various architectures, can provide unique insights into molecular properties. To harness the uniqueness of different CLMs, we propose FusionCLM, a novel stacking-ensemble learning algorithm that integrate the outputs of multiple CLMs into a unified framework. FusionCLM first generates SMILES embeddings, predictions, and losses from each CLM. Auxiliary models are trained on these first-level predictions and embeddings to estimate test losses during inference. The losses and predictions are then concatenated to create an integrated feature matrix, which trains second-level meta-models for final predictions. Empirical testing on five datasets demonstrates that FusionCLM have better performance than individual CLM at the first level and three advanced multimodal deep learning frameworks, showcasing FusionCLM’s potential in advancing molecular property prediction. FusionCLM uses the stacking-ensemble learning method that integrates unique representation learning from multiple CLMs, allowing a more comprehensive learning of molecular SMILES data. This results in providing more accurate molecular property prediction, which can help in facilitating early discovery and development of promising drug candidates. By evaluating and comparing its performance against individual CLMs and existing multimodal deep learning frameworks, FusionCLM demonstrates improvements in prediction accuracy, distinguishing itself from prior models in this domain. Chemical Language Models (CLMs) have demonstrated capabilities in extracting patterns and predicting from vast volume of the Simplified Molecular Input Line Entry System (SMILES), a notation used to represent molecular structures. Different CLMs, developed from various architectures, can provide unique insights into molecular properties. To harness the uniqueness of different CLMs, we propose FusionCLM, a novel stacking-ensemble learning algorithm that integrate the outputs of multiple CLMs into a unified framework. FusionCLM first generates SMILES embeddings, predictions, and losses from each CLM. Auxiliary models are trained on these first-level predictions and embeddings to estimate test losses during inference. The losses and predictions are then concatenated to create an integrated feature matrix, which trains second-level meta-models for final predictions. Empirical testing on five datasets demonstrates that FusionCLM have better performance than individual CLM at the first level and three advanced multimodal deep learning frameworks, showcasing FusionCLM’s potential in advancing molecular property prediction. Scientific Contribution FusionCLM uses the stacking-ensemble learning method that integrates unique representation learning from multiple CLMs, allowing a more comprehensive learning of molecular SMILES data. This results in providing more accurate molecular property prediction, which can help in facilitating early discovery and development of promising drug candidates. By evaluating and comparing its performance against individual CLMs and existing multimodal deep learning frameworks, FusionCLM demonstrates improvements in prediction accuracy, distinguishing itself from prior models in this domain. Chemical Language Models (CLMs) have demonstrated capabilities in extracting patterns and predicting from vast volume of the Simplified Molecular Input Line Entry System (SMILES), a notation used to represent molecular structures. Different CLMs, developed from various architectures, can provide unique insights into molecular properties. To harness the uniqueness of different CLMs, we propose FusionCLM, a novel stacking-ensemble learning algorithm that integrate the outputs of multiple CLMs into a unified framework. FusionCLM first generates SMILES embeddings, predictions, and losses from each CLM. Auxiliary models are trained on these first-level predictions and embeddings to estimate test losses during inference. The losses and predictions are then concatenated to create an integrated feature matrix, which trains second-level meta-models for final predictions. Empirical testing on five datasets demonstrates that FusionCLM have better performance than individual CLM at the first level and three advanced multimodal deep learning frameworks, showcasing FusionCLM's potential in advancing molecular property prediction. Keywords: Large language Models, Knowledge fusion, Ensemble learning, Molecular property prediction, Drug discovery Chemical Language Models (CLMs) have demonstrated capabilities in extracting patterns and predicting from vast volume of the Simplified Molecular Input Line Entry System (SMILES), a notation used to represent molecular structures. Different CLMs, developed from various architectures, can provide unique insights into molecular properties. To harness the uniqueness of different CLMs, we propose FusionCLM, a novel stacking-ensemble learning algorithm that integrate the outputs of multiple CLMs into a unified framework. FusionCLM first generates SMILES embeddings, predictions, and losses from each CLM. Auxiliary models are trained on these first-level predictions and embeddings to estimate test losses during inference. The losses and predictions are then concatenated to create an integrated feature matrix, which trains second-level meta-models for final predictions. Empirical testing on five datasets demonstrates that FusionCLM have better performance than individual CLM at the first level and three advanced multimodal deep learning frameworks, showcasing FusionCLM's potential in advancing molecular property prediction.Chemical Language Models (CLMs) have demonstrated capabilities in extracting patterns and predicting from vast volume of the Simplified Molecular Input Line Entry System (SMILES), a notation used to represent molecular structures. Different CLMs, developed from various architectures, can provide unique insights into molecular properties. To harness the uniqueness of different CLMs, we propose FusionCLM, a novel stacking-ensemble learning algorithm that integrate the outputs of multiple CLMs into a unified framework. FusionCLM first generates SMILES embeddings, predictions, and losses from each CLM. Auxiliary models are trained on these first-level predictions and embeddings to estimate test losses during inference. The losses and predictions are then concatenated to create an integrated feature matrix, which trains second-level meta-models for final predictions. Empirical testing on five datasets demonstrates that FusionCLM have better performance than individual CLM at the first level and three advanced multimodal deep learning frameworks, showcasing FusionCLM's potential in advancing molecular property prediction. Abstract Chemical Language Models (CLMs) have demonstrated capabilities in extracting patterns and predicting from vast volume of the Simplified Molecular Input Line Entry System (SMILES), a notation used to represent molecular structures. Different CLMs, developed from various architectures, can provide unique insights into molecular properties. To harness the uniqueness of different CLMs, we propose FusionCLM, a novel stacking-ensemble learning algorithm that integrate the outputs of multiple CLMs into a unified framework. FusionCLM first generates SMILES embeddings, predictions, and losses from each CLM. Auxiliary models are trained on these first-level predictions and embeddings to estimate test losses during inference. The losses and predictions are then concatenated to create an integrated feature matrix, which trains second-level meta-models for final predictions. Empirical testing on five datasets demonstrates that FusionCLM have better performance than individual CLM at the first level and three advanced multimodal deep learning frameworks, showcasing FusionCLM’s potential in advancing molecular property prediction. Chemical Language Models (CLMs) have demonstrated capabilities in extracting patterns and predicting from vast volume of the Simplified Molecular Input Line Entry System (SMILES), a notation used to represent molecular structures. Different CLMs, developed from various architectures, can provide unique insights into molecular properties. To harness the uniqueness of different CLMs, we propose FusionCLM, a novel stacking-ensemble learning algorithm that integrate the outputs of multiple CLMs into a unified framework. FusionCLM first generates SMILES embeddings, predictions, and losses from each CLM. Auxiliary models are trained on these first-level predictions and embeddings to estimate test losses during inference. The losses and predictions are then concatenated to create an integrated feature matrix, which trains second-level meta-models for final predictions. Empirical testing on five datasets demonstrates that FusionCLM have better performance than individual CLM at the first level and three advanced multimodal deep learning frameworks, showcasing FusionCLM's potential in advancing molecular property prediction. Chemical Language Models (CLMs) have demonstrated capabilities in extracting patterns and predicting from vast volume of the Simplified Molecular Input Line Entry System (SMILES), a notation used to represent molecular structures. Different CLMs, developed from various architectures, can provide unique insights into molecular properties. To harness the uniqueness of different CLMs, we propose FusionCLM, a novel stacking-ensemble learning algorithm that integrate the outputs of multiple CLMs into a unified framework. FusionCLM first generates SMILES embeddings, predictions, and losses from each CLM. Auxiliary models are trained on these first-level predictions and embeddings to estimate test losses during inference. The losses and predictions are then concatenated to create an integrated feature matrix, which trains second-level meta-models for final predictions. Empirical testing on five datasets demonstrates that FusionCLM have better performance than individual CLM at the first level and three advanced multimodal deep learning frameworks, showcasing FusionCLM’s potential in advancing molecular property prediction.Scientific ContributionFusionCLM uses the stacking-ensemble learning method that integrates unique representation learning from multiple CLMs, allowing a more comprehensive learning of molecular SMILES data. This results in providing more accurate molecular property prediction, which can help in facilitating early discovery and development of promising drug candidates. By evaluating and comparing its performance against individual CLMs and existing multimodal deep learning frameworks, FusionCLM demonstrates improvements in prediction accuracy, distinguishing itself from prior models in this domain. |
| ArticleNumber | 133 |
| Audience | Academic |
| Author | Hu, Pingzhao Lu, Yutong Li, Yan Yi Sun, Yan |
| Author_xml | – sequence: 1 givenname: Yutong surname: Lu fullname: Lu, Yutong organization: Biostatistics Division, Dalla Lana School of Public Health, University of Toronto – sequence: 2 givenname: Yan Yi surname: Li fullname: Li, Yan Yi organization: Biostatistics Division, Dalla Lana School of Public Health, University of Toronto – sequence: 3 givenname: Yan surname: Sun fullname: Sun, Yan organization: Department of Biochemistry, Western University, Department of Computer Science, Western University – sequence: 4 givenname: Pingzhao surname: Hu fullname: Hu, Pingzhao email: phu49@uwo.ca organization: Biostatistics Division, Dalla Lana School of Public Health, University of Toronto, Department of Biochemistry, Western University, Department of Computer Science, Western University, Department of Oncology, Western University, Department of Epidemiology and Biostatistics, Western University, The Children’s Health Research Institute, Lawson Health Research Institute |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/40883821$$D View this record in MEDLINE/PubMed |
| BookMark | eNp9kktv1DAUhSNURB_wB1igSGxgkeJH7NhsUDWiMNIgJB5r68a5yWTI2FM7KfTf45kpbQch5IUt-zvH8vE5zY6cd5hlzyk5p1TJN5FyzmhBmCgIJRUv5KPshFZCFUyX8ujB-jg7jXFFiBQVqZ5kxyVRiitGT7L6coq9d7PFp7c5uiU4i02-9gPaaYCQb4LfYBhv0gKb3o4Jza97yH84_3PApsO83elz3-Z2ievewpAP4LoJ0tnaNzjEp9njFoaIz27ns-z75ftvs4_F4vOH-exiUVihxVjI2rYMlGUtTbPWHIQA4LaWNUpFW6opgJKogZWMQFOhtpwSYVXLqKgsP8vme9_Gw8psQr-GcGM89Ga34UNnIIy9HdCUmkustCUg67KRRNdKEE050FY2FWmS17u912aq19hYdGOA4cD08MT1S9P5a0MZ16KiMjm8unUI_mrCOJp1Hy0OKRz0UzSclZILrRVJ6Mu_0JWfgktZbalSSyVYeU91kF7Qu9ani-3W1FwowRXXSrJEnf-DSqPZfk7qT9un_QPB6wNBYkb8NXYwxWjmX78csi8epnIXx586JYDtARt8jAHbO4QSs-2s2XfWpM6aXWfNNim-F8UEuw7D_fP_o_oNS-Xsqg |
| Cites_doi | 10.1016/B978-0-12-373593-5.00069-0 10.1023/A:1010933404324 10.6019/CHEMBL3301361 10.1186/s13321-02000441-8 10.1039/C7SC02664A 10.1093/bib/bbac408 10.1093/nar/gky1033 10.1023/B:MACH.0000015881.36452.6e 10.1021/ci049714+ 10.1037/h0078850 10.1021/ci00057a005 10.21203/rs.3.rs-1570270/v1 10.1101/095653 10.1016/j.chembiol.2016.07.023 10.3389/fphar.2022.1046524 10.1021/ci034243x 10.1016/j.ddtec.2020.05.001 10.1021/ci300124c 10.1021/acs.jmedchem.1c00927 10.1093/nar/gkw1074 10.1038/s42256-022-00557-6 10.1145/3307339.3342186 10.48550/arXiv.1310.5103 10.48550/arXiv.2306.02561 10.1109/UIC-ATC.2017.8397411 10.1021/acs.jcim.8b00839 10.48550/arXiv.2209.01712 10.1038/nature14539 10.1021/acs.jcim.6b00290 10.1038/s42256-02300626-4 10.1186/s12859020-3406-0 10.1016/j.dche.2022.100034 10.48550/arXiv.2208.13994 10.2307/3001968 10.48550/arXiv.2011.13230 10.1016/S0893-6080(05)80023-1 10.1109/CVPR52729.2023.02271 10.1186/s12859-0193135-4 10.1093/bib/bbad306 |
| ContentType | Journal Article |
| Copyright | The Author(s) 2025 2025. The Author(s). COPYRIGHT 2025 BioMed Central Ltd. The Author(s) 2025. This work is published under http://creativecommons.org/licenses/by-nc-nd/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. The Author(s) 2025 2025 |
| Copyright_xml | – notice: The Author(s) 2025 – notice: 2025. The Author(s). – notice: COPYRIGHT 2025 BioMed Central Ltd. – notice: The Author(s) 2025. This work is published under http://creativecommons.org/licenses/by-nc-nd/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. – notice: The Author(s) 2025 2025 |
| DBID | C6C AAYXX CITATION NPM ISR 3V. 7QO 7X7 7XB 8AO 8FD 8FE 8FG 8FH 8FI 8FJ 8FK ABJCF ABUWG AEUYN AFKRA ARAPS AZQEC BBNVY BENPR BGLVJ BHPHI CCPQU D1I DWQXO FR3 FYUFA GHDGH GNUQQ HCIFZ K9. KB. LK8 M0S M7P P5Z P62 P64 PDBOC PHGZM PHGZT PIMPY PJZUB PKEHL PPXIY PQEST PQGLB PQQKQ PQUKI PRINS 7X8 5PM DOA |
| DOI | 10.1186/s13321-025-01073-6 |
| DatabaseName | Springer Nature OA Free Journals CrossRef PubMed Gale In Context: Science ProQuest Central (Corporate) Biotechnology Research Abstracts Health & Medical Collection ProQuest Central (purchase pre-March 2016) ProQuest Pharma Collection Technology Research Database ProQuest SciTech Collection ProQuest Technology Collection ProQuest Natural Science Collection Hospital Premium Collection Hospital Premium Collection (Alumni Edition) ProQuest Central (Alumni) (purchase pre-March 2016) Materials Science & Engineering Collection ProQuest Central (Alumni) ProQuest One Sustainability ProQuest Central UK/Ireland Advanced Technologies & Computer Science Collection ProQuest Central Essentials - QC Biological Science Collection ProQuest Central ProQuest Technology Collection Natural Science Collection ProQuest One ProQuest Materials Science Collection ProQuest Central Korea Engineering Research Database Health Research Premium Collection Health Research Premium Collection (Alumni) ProQuest Central Student SciTech Premium Collection ProQuest Health & Medical Complete (Alumni) Materials Science Database ProQuest Biological Science Collection ProQuest Health & Medical Collection Biological Science Database Advanced Technologies & Aerospace Database ProQuest Advanced Technologies & Aerospace Collection Biotechnology and BioEngineering Abstracts Materials Science Collection Proquest Central Premium ProQuest One Academic (New) ProQuest Publicly Available Content Database ProQuest Health & Medical Research Collection ProQuest One Academic Middle East (New) ProQuest One Health & Nursing ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Applied & Life Sciences ProQuest One Academic (retired) ProQuest One Academic UKI Edition ProQuest Central China MEDLINE - Academic PubMed Central (Full Participant titles) DOAJ Directory of Open Access Journals |
| DatabaseTitle | CrossRef PubMed Publicly Available Content Database ProQuest Central Student Technology Collection Technology Research Database ProQuest One Academic Middle East (New) ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Essentials Materials Science Collection ProQuest Health & Medical Complete (Alumni) ProQuest Central (Alumni Edition) SciTech Premium Collection ProQuest One Community College ProQuest One Health & Nursing ProQuest Natural Science Collection ProQuest Pharma Collection ProQuest Central China ProQuest Central ProQuest One Applied & Life Sciences ProQuest One Sustainability ProQuest Health & Medical Research Collection Health Research Premium Collection Biotechnology Research Abstracts Health and Medicine Complete (Alumni Edition) Natural Science Collection ProQuest Central Korea Biological Science Collection Materials Science Database ProQuest Central (New) ProQuest Materials Science Collection Advanced Technologies & Aerospace Collection ProQuest Biological Science Collection ProQuest One Academic Eastern Edition ProQuest Hospital Collection ProQuest Technology Collection Health Research Premium Collection (Alumni) Biological Science Database ProQuest SciTech Collection ProQuest Hospital Collection (Alumni) Biotechnology and BioEngineering Abstracts Advanced Technologies & Aerospace Database ProQuest Health & Medical Complete ProQuest One Academic UKI Edition Materials Science & Engineering Collection Engineering Research Database ProQuest One Academic ProQuest One Academic (New) ProQuest Central (Alumni) MEDLINE - Academic |
| DatabaseTitleList | MEDLINE - Academic Publicly Available Content Database PubMed |
| Database_xml | – sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 3 dbid: PIMPY name: Publicly Available Content Database url: http://search.proquest.com/publiccontent sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Chemistry |
| EISSN | 1758-2946 |
| EndPage | 12 |
| ExternalDocumentID | oai_doaj_org_article_4936e79c0a6b4d609b850913a1f6d70d PMC12395716 A853839862 40883821 10_1186_s13321_025_01073_6 |
| Genre | Journal Article |
| GrantInformation_xml | – fundername: Canadian Institutes of Health Research grantid: PLL 185683, PJT 190272 – fundername: Natural Sciences and Engineering Research Council of Canada grantid: RGPIN-2021-04072 – fundername: Canada Foundation for Innovation (CFI) John R. Evans Leaders Fund (JELF) program grantid: #43481 – fundername: Canada Research Chairs Tier II Program grantid: CRC-2021-00482 – fundername: CIHR grantid: PLL 185683, PJT 190272 |
| GroupedDBID | 0R~ 29K 2WC 4.4 40G 53G 5VS 7X7 8AO 8FE 8FG 8FH 8FI 8FJ AAFWJ AAJSJ AAKKN AAKPC AASML ABDBF ABEEZ ABJCF ABUWG ACACY ACGFS ACIHN ACIWK ACPRK ACUHS ACULB ADBBV ADRAZ ADUKV AEAQA AENEX AEUYN AFGXO AFKRA AFPKN AFRAH AHBYD AHMBA AHYZX ALMA_UNASSIGNED_HOLDINGS AMKLP AMTXH AOIJS ARAPS BAPOH BAWUL BBNVY BCNDV BENPR BFQNJ BGLVJ BHPHI BMC BPHCQ BVXVI C24 C6C CCPQU D-I D1I DIK E3Z EBLON EBS ESX F5P FYUFA GROUPED_DOAJ GX1 HCIFZ HH5 HMCUK HYE IAO IGS IHR ISR ITC KB. KQ8 LK8 M7P MK0 M~E O5R O5S OK1 P62 PDBOC PGMZT PHGZM PHGZT PIMPY PQGLB PQQKQ PROAC PUEGO RBZ RNS RPM RVI SOJ SPH TR2 TUS U2A UKHRP AAYXX AFFHD CITATION NPM 3V. 7QO 7XB 8FD 8FK AZQEC DWQXO FR3 GNUQQ K9. P64 PJZUB PKEHL PPXIY PQEST PQUKI PRINS 7X8 5PM |
| ID | FETCH-LOGICAL-c595t-6bcf2a8c2f1f2a993a55aa3cb6be681f191aa86e9a2420ad7e9c3105c8f2157c3 |
| IEDL.DBID | C24 |
| ISICitedReferencesCount | 0 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001560396600002&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 1758-2946 |
| IngestDate | Fri Oct 03 12:35:13 EDT 2025 Tue Nov 04 02:05:20 EST 2025 Thu Sep 04 12:32:53 EDT 2025 Sat Oct 18 23:46:55 EDT 2025 Tue Nov 11 10:47:26 EST 2025 Tue Nov 04 18:12:04 EST 2025 Thu Nov 13 15:55:57 EST 2025 Tue Sep 16 01:43:43 EDT 2025 Sat Nov 29 07:30:16 EST 2025 Sat Aug 30 01:21:07 EDT 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 1 |
| Keywords | Knowledge fusion Large language Models Molecular property prediction Drug discovery Ensemble learning |
| Language | English |
| License | 2025. The Author(s). Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/. |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c595t-6bcf2a8c2f1f2a993a55aa3cb6be681f191aa86e9a2420ad7e9c3105c8f2157c3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 |
| OpenAccessLink | https://link.springer.com/10.1186/s13321-025-01073-6 |
| PMID | 40883821 |
| PQID | 3244968524 |
| PQPubID | 54992 |
| PageCount | 12 |
| ParticipantIDs | doaj_primary_oai_doaj_org_article_4936e79c0a6b4d609b850913a1f6d70d pubmedcentral_primary_oai_pubmedcentral_nih_gov_12395716 proquest_miscellaneous_3246359980 proquest_journals_3244968524 gale_infotracmisc_A853839862 gale_infotracacademiconefile_A853839862 gale_incontextgauss_ISR_A853839862 pubmed_primary_40883821 crossref_primary_10_1186_s13321_025_01073_6 springer_journals_10_1186_s13321_025_01073_6 |
| PublicationCentury | 2000 |
| PublicationDate | 2025-08-29 |
| PublicationDateYYYYMMDD | 2025-08-29 |
| PublicationDate_xml | – month: 08 year: 2025 text: 2025-08-29 day: 29 |
| PublicationDecade | 2020 |
| PublicationPlace | Cham |
| PublicationPlace_xml | – name: Cham – name: England – name: London |
| PublicationTitle | Journal of cheminformatics |
| PublicationTitleAbbrev | J Cheminform |
| PublicationTitleAlternate | J Cheminform |
| PublicationYear | 2025 |
| Publisher | Springer International Publishing BioMed Central Ltd Springer Nature B.V BMC |
| Publisher_xml | – name: Springer International Publishing – name: BioMed Central Ltd – name: Springer Nature B.V – name: BMC |
| References | D Weininger (1073_CR7) 1988; 28 X Tong (1073_CR38) 2021; 64 N Ding (1073_CR6) 2023; 5 Y LeCun (1073_CR4) 2015; 521 F Wilcoxon (1073_CR34) 1945; 1 G Shafer (1073_CR41) 2007; 9 1073_CR1 S Kim (1073_CR22) 2019; 47 M Barton (1073_CR16) 2022; 3 1073_CR40 AV Artemov (1073_CR31) 2016 L Breiman (1073_CR25) 2001; 45 A Gaulton (1073_CR24) 2017; 45 W Kong (1073_CR37) 2022; 13 X Zeng (1073_CR3) 2022; 4 J Gao (1073_CR20) 2023 JS Delaney (1073_CR28) 2004; 44 S Kwon (1073_CR2) 2019; 20 1073_CR26 H Cai (1073_CR18) 2022 S Džeroski (1073_CR14) 2004; 54 DH Wolpert (1073_CR13) 1992; 5 J Shen (1073_CR5) 2019; 32–33 M Wenlock (1073_CR27) 2015 W Zhu (1073_CR19) 2022; 63 1073_CR39 N Brown (1073_CR23) 2019; 59 JJ Irwin (1073_CR21) 2005; 45 KM Gayvert (1073_CR30) 2016; 23 DW Zimmerman (1073_CR35) 1993; 47 Z Wu (1073_CR17) 2018; 9 G Subramanian (1073_CR29) 2016; 56 1073_CR11 H-C Yi (1073_CR15) 2020; 21 IF Martins (1073_CR32) 2012; 52 1073_CR33 1073_CR10 J Ross (1073_CR9) 2022; 4 J Arús-Pous (1073_CR36) 2020; 12 1073_CR12 1073_CR8 |
| References_xml | – ident: 1073_CR1 doi: 10.1016/B978-0-12-373593-5.00069-0 – volume: 45 start-page: 5 issue: 1 year: 2001 ident: 1073_CR25 publication-title: Mach Learn doi: 10.1023/A:1010933404324 – year: 2015 ident: 1073_CR27 doi: 10.6019/CHEMBL3301361 – volume: 12 start-page: 38 issue: 1 year: 2020 ident: 1073_CR36 publication-title: J Cheminform doi: 10.1186/s13321-02000441-8 – volume: 9 start-page: 513 issue: 2 year: 2018 ident: 1073_CR17 publication-title: Chem Sci doi: 10.1039/C7SC02664A – year: 2022 ident: 1073_CR18 publication-title: Brief Bioinform doi: 10.1093/bib/bbac408 – volume: 47 start-page: D1102 issue: D1 year: 2019 ident: 1073_CR22 publication-title: Nucleic Acids Res doi: 10.1093/nar/gky1033 – volume: 54 start-page: 255 issue: 3 year: 2004 ident: 1073_CR14 publication-title: Mach Learn doi: 10.1023/B:MACH.0000015881.36452.6e – volume: 45 start-page: 177 issue: 1 year: 2005 ident: 1073_CR21 publication-title: J Chem Inf Model doi: 10.1021/ci049714+ – volume: 47 start-page: 523 issue: 3 year: 1993 ident: 1073_CR35 publication-title: Can J Exp Psychol doi: 10.1037/h0078850 – volume: 28 start-page: 31 issue: 1 year: 1988 ident: 1073_CR7 publication-title: J Chem Inf Comput Sci doi: 10.1021/ci00057a005 – volume: 4 start-page: 1256 issue: 12 year: 2022 ident: 1073_CR9 publication-title: Nat Mach Intell doi: 10.21203/rs.3.rs-1570270/v1 – year: 2016 ident: 1073_CR31 publication-title: BioRxiv doi: 10.1101/095653 – volume: 23 start-page: 1294 issue: 10 year: 2016 ident: 1073_CR30 publication-title: Cell Chem Biol doi: 10.1016/j.chembiol.2016.07.023 – volume: 13 start-page: 1046524 year: 2022 ident: 1073_CR37 publication-title: Front Pharmacol doi: 10.3389/fphar.2022.1046524 – volume: 44 start-page: 1000 issue: 3 year: 2004 ident: 1073_CR28 publication-title: J Chem Inf Comput Sci doi: 10.1021/ci034243x – volume: 32–33 start-page: 29 year: 2019 ident: 1073_CR5 publication-title: Drug Discov Today Technol doi: 10.1016/j.ddtec.2020.05.001 – volume: 52 start-page: 1686 issue: 6 year: 2012 ident: 1073_CR32 publication-title: J Chem Inf Model doi: 10.1021/ci300124c – volume: 64 start-page: 14011 issue: 19 year: 2021 ident: 1073_CR38 publication-title: J Med Chem doi: 10.1021/acs.jmedchem.1c00927 – volume: 45 start-page: D945 issue: D1 year: 2017 ident: 1073_CR24 publication-title: Nucleic Acids Res doi: 10.1093/nar/gkw1074 – volume: 4 start-page: 1004 issue: 11 year: 2022 ident: 1073_CR3 publication-title: Nat Mach Intell doi: 10.1038/s42256-022-00557-6 – ident: 1073_CR11 doi: 10.1145/3307339.3342186 – ident: 1073_CR33 doi: 10.48550/arXiv.1310.5103 – ident: 1073_CR26 doi: 10.48550/arXiv.2306.02561 – ident: 1073_CR39 doi: 10.1109/UIC-ATC.2017.8397411 – volume: 59 start-page: 1096 issue: 3 year: 2019 ident: 1073_CR23 publication-title: J Chem Inf Model doi: 10.1021/acs.jcim.8b00839 – ident: 1073_CR8 doi: 10.48550/arXiv.2209.01712 – volume: 521 start-page: 436 issue: 7553 year: 2015 ident: 1073_CR4 publication-title: Nature doi: 10.1038/nature14539 – ident: 1073_CR40 – volume: 56 start-page: 1936 issue: 10 year: 2016 ident: 1073_CR29 publication-title: J Chem Inf Model doi: 10.1021/acs.jcim.6b00290 – volume: 5 start-page: 220 issue: 3 year: 2023 ident: 1073_CR6 publication-title: Nat Mach Intell doi: 10.1038/s42256-02300626-4 – volume: 21 start-page: 60 issue: 1 year: 2020 ident: 1073_CR15 publication-title: BMC Bioinform doi: 10.1186/s12859020-3406-0 – volume: 3 year: 2022 ident: 1073_CR16 publication-title: Digit Chem Eng doi: 10.1016/j.dche.2022.100034 – volume: 63 start-page: 43 issue: 1 year: 2022 ident: 1073_CR19 publication-title: J Chem Inf Model doi: 10.48550/arXiv.2208.13994 – volume: 1 start-page: 80 year: 1945 ident: 1073_CR34 publication-title: Biometrics Bulletin doi: 10.2307/3001968 – volume: 9 start-page: 371 issue: 2 year: 2007 ident: 1073_CR41 publication-title: J Mach Learn Res – ident: 1073_CR10 doi: 10.48550/arXiv.2011.13230 – volume: 5 start-page: 241 issue: 2 year: 1992 ident: 1073_CR13 publication-title: Neural Netw doi: 10.1016/S0893-6080(05)80023-1 – ident: 1073_CR12 doi: 10.1109/CVPR52729.2023.02271 – volume: 20 start-page: 521 issue: 1 year: 2019 ident: 1073_CR2 publication-title: BMC Bioinform doi: 10.1186/s12859-0193135-4 – year: 2023 ident: 1073_CR20 publication-title: Brief Bioinform doi: 10.1093/bib/bbad306 |
| SSID | ssj0065707 |
| Score | 2.36657 |
| Snippet | Chemical Language Models (CLMs) have demonstrated capabilities in extracting patterns and predicting from vast volume of the Simplified Molecular Input Line... Abstract Chemical Language Models (CLMs) have demonstrated capabilities in extracting patterns and predicting from vast volume of the Simplified Molecular... |
| SourceID | doaj pubmedcentral proquest gale pubmed crossref springer |
| SourceType | Open Website Open Access Repository Aggregation Database Index Database Publisher |
| StartPage | 133 |
| SubjectTerms | Chemistry Chemistry and Materials Science Computational Biology/Bioinformatics Computer Applications in Chemistry Data mining Datasets Deep learning Documentation and Information in Chemistry Drug development Drug discovery Ensemble learning Knowledge fusion Language Large language Models Machine learning Molecular properties Molecular property prediction Molecular structure Predictions Theoretical and Computational Chemistry Uniqueness |
| SummonAdditionalLinks | – databaseName: DOAJ Directory of Open Access Journals dbid: DOA link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV3di9QwEB_kEPRF_LZ6ShTBBy3XTZs08e1cXBTOQ_zi3kKSJt4-2D22uwf-986k7Xo9EV982mUzXZr5TZKZZPIbgOeiio1zSuShcVVe8ahz3UQ65G3qwLnTMvFsfzuqj4_VyYn-eKHUF-WE9fTAveIOKl3KUGtfWOmqRhYa_5i4LO0syqYuGpp9i1qPwVQ_B1M-Rz1ekVHyoMNIjGPYzClRDY06l5NlKLH1_zknX1iULidMXjo1TYvR4ibcGLxIdti__S24EtrbcG0-Fm-7A26xpW2w-dGH1yy0p-mYn_0YS-GyM9qCX29-4hc6qCFw2PnSst0OG4vpebaKzA-UAmzc2mSpek53F74u3n6Zv8uHcgq5F1pscul85FZ5Hmf4iX6JFcLa0jvpglSziJGbtUoGbXHZLixipT06f8KriH5B7ct7sNeu2vAAmJd0AxtbvAiVjF5hmBgjF1bVJeGUwctRu-asZ80wKdpQ0vRYGMTCJCyMzOANAbCTJMbr9APagRnswPzLDjJ4RvAZ4rRoKWnmu912nXn_-ZM5RJcE_UCM3TJ4MQjFFQLp7XAHAXtFNFgTyf2JJMLnp82jlZhh0HcGfdNKSyU49v_prpmepES2Nqy2SQZdPIxxiwzu90a163eFM36p-CwDNTG3iWKmLe3yNFGCo_-hBYa-GbwaLfP3e_1d8w__h-YfwXWeRhZd59mHvc16Gx7DVX--WXbrJ2lc_gLBCDpW priority: 102 providerName: Directory of Open Access Journals – databaseName: Biological Science Database dbid: M7P link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV3db9MwED_BQIKX8TkIDGQQEg8QLXVjx-YFjYoKpDFNfGlvluPYWx9IuqadxH_PnZt0ZAheeGpUX6I4v_N9-XwH8ELkoSpLJVJflXma86BTXQXa5K0Kz3mpZayz_f2gODxUx8f6qAu4tV1aZS8To6CuGkcx8j1U_LmWSvD87fwspa5RtLvatdC4CteoSgKPqXtHvSSmrI6iPyij5F6L_hhH55lTuhqydioHyijW7P9TMv-mmi6nTV7aO40qaXrrfydzG7Y7Y5Ttr7nnDlzx9V24Mel7wN2DcrqiaNrk4NMb5uvTmC3AfvQdddmcIvmL5U-8oP0ewpidzyzbBOpYiPezJjDXVSZgfYSUxSY87X34Nn3_dfIh7boypE5osUxl6QK3yvEwwl80b6wQ1o5dKUsv1SigA2itkl5b1P6ZRci1QxtSOBUQosKNd2Crbmr_EJiTdJAbR5zwuQxOobcZAhdWFWMryzyBVz08Zr4uvmGi06KkWYNpEEwTwTQygXeE4IaSCmfHP5rFienWocn1WPpCu4yeX8lMI59SaVQ7CrIqsiqB54S_odIYNeXenNhV25qPXz6bfbRs0JxEFzCBlx1RaJATnO2OMuCsqJrWgHJ3QInwueFwzx-mkx2tuWCOBJ5thulOyoerfbOKNGgpoqucJfBgzZWbeeeoOMaKjxJQA34dfJjhSD07jZXF0YzRAj3oBF73rH3xXn__8o_-PY3HcJPHRUfnfXZha7lY-Sdw3Z0vZ-3iaVyyvwD6LUkT priority: 102 providerName: ProQuest |
| Title | FusionCLM: enhanced molecular property prediction via knowledge fusion of chemical language models |
| URI | https://link.springer.com/article/10.1186/s13321-025-01073-6 https://www.ncbi.nlm.nih.gov/pubmed/40883821 https://www.proquest.com/docview/3244968524 https://www.proquest.com/docview/3246359980 https://pubmed.ncbi.nlm.nih.gov/PMC12395716 https://doaj.org/article/4936e79c0a6b4d609b850913a1f6d70d |
| Volume | 17 |
| WOSCitedRecordID | wos001560396600002&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVADU databaseName: BioMed Central Open Access Free customDbUrl: eissn: 1758-2946 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0065707 issn: 1758-2946 databaseCode: RBZ dateStart: 20090101 isFulltext: true titleUrlDefault: https://www.biomedcentral.com/search/ providerName: BioMedCentral – providerCode: PRVAON databaseName: DOAJ Directory of Open Access Journals customDbUrl: eissn: 1758-2946 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0065707 issn: 1758-2946 databaseCode: DOA dateStart: 20090101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources customDbUrl: eissn: 1758-2946 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0065707 issn: 1758-2946 databaseCode: M~E dateStart: 20090101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre – providerCode: PRVPQU databaseName: Advanced Technologies & Aerospace Database customDbUrl: eissn: 1758-2946 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0065707 issn: 1758-2946 databaseCode: P5Z dateStart: 20090101 isFulltext: true titleUrlDefault: https://search.proquest.com/hightechjournals providerName: ProQuest – providerCode: PRVPQU databaseName: Advanced Technologies & Aerospace Database customDbUrl: eissn: 1758-2946 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0065707 issn: 1758-2946 databaseCode: P5Z dateStart: 20240101 isFulltext: true titleUrlDefault: https://search.proquest.com/hightechjournals providerName: ProQuest – providerCode: PRVPQU databaseName: Biological Science Database customDbUrl: eissn: 1758-2946 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0065707 issn: 1758-2946 databaseCode: M7P dateStart: 20240101 isFulltext: true titleUrlDefault: http://search.proquest.com/biologicalscijournals providerName: ProQuest – providerCode: PRVPQU databaseName: Health & Medical Collection customDbUrl: eissn: 1758-2946 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0065707 issn: 1758-2946 databaseCode: 7X7 dateStart: 20090101 isFulltext: true titleUrlDefault: https://search.proquest.com/healthcomplete providerName: ProQuest – providerCode: PRVPQU databaseName: Health & Medical Collection customDbUrl: eissn: 1758-2946 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0065707 issn: 1758-2946 databaseCode: 7X7 dateStart: 20240101 isFulltext: true titleUrlDefault: https://search.proquest.com/healthcomplete providerName: ProQuest – providerCode: PRVPQU databaseName: Materials Science Database customDbUrl: eissn: 1758-2946 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0065707 issn: 1758-2946 databaseCode: KB. dateStart: 20240101 isFulltext: true titleUrlDefault: http://search.proquest.com/materialsscijournals providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: eissn: 1758-2946 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0065707 issn: 1758-2946 databaseCode: BENPR dateStart: 20240101 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: eissn: 1758-2946 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0065707 issn: 1758-2946 databaseCode: BENPR dateStart: 20090101 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVPQU databaseName: Publicly Available Content Database customDbUrl: eissn: 1758-2946 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0065707 issn: 1758-2946 databaseCode: PIMPY dateStart: 20240101 isFulltext: true titleUrlDefault: http://search.proquest.com/publiccontent providerName: ProQuest – providerCode: PRVAVX databaseName: SpringerOpen customDbUrl: eissn: 1758-2946 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0065707 issn: 1758-2946 databaseCode: C24 dateStart: 20090112 isFulltext: true titleUrlDefault: https://link.springer.com/search?facet-content-type=%22Journal%22 providerName: Springer Nature |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnR1db9Mw0GIbEnvhmxEYlUFIPECgdWPH5m2tWjGxVdH4UMeL5Tj21gfSqWkn8cJv585NChnwAC9OVJ-j-u7s-_DdmZDnPPFFnkseuyJP4oR5FavC4yFvkTrGciVCne3PR-lkIqdTldVJYVUT7d4cSYadOixrKd5UYE0xMH0ZBpsBY8Zii-zwnlQYyDfEHIf1_ouxHGmTHvPHcS0RFCr1_74f_yKQrgZLXjkxDYJofOv_pnCb3KwVT3qw5pQ75Jor75Ibw-a-t3skH6_QczY8On5LXXkeIgPo1-b2XHqBXvvF8hu84NkO0pNezgzdOOWoD-Pp3FNbVyGgjTeUhgt3qvvk03j0cfgurm9giC1XfBmL3HpmpGW-B09QZQznxvRtLnInZM-DsWeMFE4ZkPRdA-RVFvRFbqUHVSK1_Qdku5yX7iGhVmDSNvRY7hLhrQTL0nvGjUz7RuRJRF42RNEX60IbOhgoUug13jTgTQe8aRGRAdJtA4lFssMP88WZrtecTlRfuFTZLn6_EF0FPIllUE3PiyLtFhF5hlTXWAajxDibM7OqKn344UQfgBYDqiOYexF5UQP5OdDfmjptAWaFlbNakPstSCCfbXc3zKXrfaLSoM4mSkjOYP5PN904EmPfSjdfBRjQCsEs7kZkb82Lm3knICT6kvUiIltc2kJMu6ecnYcq4qCyKA7WckReNcz683_9HfOP_g38Mdllgd8x12efbC8XK_eEXLeXy1m16JCtdJqGVnbIzmA0yU46YTF3gm8E2veD1x2Mx82w_T6CNuNfADY7PM5OfwDcJUht |
| linkProvider | Springer Nature |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1bb9MwFD4aHdJ44X4JDDAIxANEa93EsZEQGoVp1dqqgg2NJ-M49tYHktK0Q_tT_EaO3aQjQ_C2B54a1U4UO9-5-dwAnsWRzdKUx6HJ0iiMqBWhyKxz8maJoTQVzNfZ_jxIRiN-eCjGa_CzzoVxYZU1T_SMOiu0OyPfQsEfCcZjGr2dfg9d1yjnXa1baCxhsWdOf6DJVr7pv8fv-5zSnQ_7vd2w6ioQ6ljE85Cl2lLFNbUd_EXxrOJYqa5OWWoY71g0YJTizAiF0qut8JWFRh0o1tyieEx0F597CdYjB_YWrI_7w_GXmve7OJKkTs3hbKtEC5CiuU5dgBwSU8ga4s93CfhTFvwmDM8Hap7z1nohuHPtf9u-63C1UrfJ9pI-bsCayW_CRq_ucncL0p2FOy_sDYavicmPfTwE-Vb3DCZT56uYzU_xwnm0HIrJyUSR1VEksf5-Uliiq9oLpD4DJr7NUHkbDi5kiXeglRe5uQdEM5eqjiM6NhGzmqM9bS2NFU-6iqVRAC9rOMjpsryI9GYZZ3IJHongkR48kgXwziFmNdOVBvd_FLMjWXEaGYkuM4nQbff8jLUFUqIr_qo6lmVJOwvgqcObdMU_chdddKQWZSn7nz7KbdTdUGFGIzeAF9UkWyDytKqSNXBVrl5YY-ZmYyZ-Pt0crvEoK-5YyjMwBvBkNezudBF_uSkWfg7qwkLwdgB3l1SwWneEorHLaScA3qCPxsY0R_LJsa-djoqaiJMObuWrmpTO3uvvO3__38t4DBu7-8OBHPRHew_gCvUE77KbNqE1ny3MQ7isT-aTcvaoYhgEvl40kf0CWheozw |
| linkToPdf | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Lj9MwELaWBQEX3o_AAgaBOEDU1o0dGwmhpUtFtaWqeGnFxTiOvdsDaWnaRfvX-HXMuEmXLILbHjg1qidR7HzjmfG8CHnME59nmeSxy7MkTphXsco9Onnz1DGWKRHqbH8epqOR3NtT4w3ys86FwbDKek8MG3U-tXhG3gLBnyghOUtavgqLGO_0X82-x9hBCj2tdTuNFUR23dEPMN_Kl4Md-NZPGOu_-dh7G1cdBmLLFV_EIrOeGWmZ78AviGrDuTFdm4nMCdnxYMwYI4VTBiRZ28DrKwv6ELfSg6hMbReee4acTcHGxHDCMf9SSwGMKEnrJB0pWiXYggwMd4ahcsBWsWgIwtAv4E-p8JtYPBmyecJvG8Rh__L_vJBXyKVKCafbK665SjZccY1c6NW9766TrL_EU8Te8N0L6oqDECVBv9WdhOkMPRjzxRFcoJ8LsU0PJ4auDyipD_fTqae2qshA65NhGpoPlTfIp1OZ4k2yWUwLd5tQKzCBHUYsd4nwVoKV7T3jRqZdI7IkIs9qaOjZquiIDsaaFHoFJA1A0gFIWkTkNaJnTYkFw8Mf0_m-rvYfnaiucKmybXx-LtoK-BNLwpqOF3naziPyCLGnsSRIgZjYN8uy1IMP7_U2aHSgRoPpG5GnFZGfAgqtqVI4YFZYRaxBudWghM9nm8M1NnW1Z5b6GJgRebgexjsxDrBw02WgAQ1ZKdmOyK0VR6znnYDA7ErWiYhs8EpjYZojxeQgVFQH9U3xtANL-bxmq-P3-vvK3_n3NB6Q88BZejgY7d4lF1ngfUx52iKbi_nS3SPn7OFiUs7vh52Dkq-nzWG_AARksDI |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=FusionCLM%3A+enhanced+molecular+property+prediction+via+knowledge+fusion+of+chemical+language+models&rft.jtitle=Journal+of+cheminformatics&rft.au=Lu%2C+Yutong&rft.au=Li%2C+Yan+Yi&rft.au=Sun%2C+Yan&rft.au=Hu%2C+Pingzhao&rft.date=2025-08-29&rft.pub=BioMed+Central+Ltd&rft.issn=1758-2946&rft.eissn=1758-2946&rft.volume=17&rft.issue=1&rft_id=info:doi/10.1186%2Fs13321-025-01073-6&rft.externalDocID=A853839862 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1758-2946&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1758-2946&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1758-2946&client=summon |