Fetching the paper…
Reading the bibliography…
The integration of biomolecular modeling with natural language (BL) has emerged as a promising interdisciplinary area at the intersection of artificial intelligence, chemistry and biology.
D. Weininger, “Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules,” Journal of chemical information and computer sciences , vol. 28, no. 1, pp. 31–36, 1988
1988
Earlier work this paper cites.
D. Weininger, A. Weininger, and J. L. Weininger, “Smiles. 2. algorithm for generation of unique smiles notation,” Journal of chemical information and computer sciences , vol. 29, no. 2, pp. 97–101, 1989
1989
Earlier work this paper cites.
W. R. Pearson, “Using the fasta program to search protein and dna sequence databases,” Computer Analysis of Sequence Data: Part I , pp. 307–331, 1994
1994
Earlier work this paper cites.
S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural computation , vol. 9, no. 8, pp. 1735–1780, 1997
1997
Earlier work this paper cites.
C. v. Mering, M. Huynen, D. Jaeggi, S. Schmidt, P. Bork, and B. Snel, “String: a database of predicted functional associations between proteins,” Nucleic acids research , vol. 31, no. 1, pp. 258–261, 2003
2003
Earlier work this paper cites.
P. D. Sun, C. E. Foster, and J. C. Boyington, “Overview of protein structural and functional folds,” Current protocols in protein science , vol. 35, no. 1, pp. 17–1, 2004
2004
Earlier work this paper cites.
G. O. Consortium, “The gene ontology (go) database and informatics resource,” Nucleic acids research , vol. 32, no. suppl_1, pp. D258–D261, 2004
2004
Earlier work this paper cites.
S. Banerjee and A. Lavie, “Meteor: An automatic metric for mt evaluation with improved correlation with human judgments,” in Proceedings of the acl workshop on intrinsic and extrinsic evaluation measures for machine translation and/or summarization , 2005, pp. 65–72
2005
Earlier work this paper cites.
D. S. Wishart, C. Knox, A. C. Guo, S. Shrivastava, M. Hassanali, P. Stothard, Z. Chang, and J. Woolsey, “Drugbank: a comprehensive resource for in silico drug discovery and exploration,” Nucleic acids research , vol. 34, no. suppl_1, pp. D668–D672, 2006
2006
Earlier work this paper cites.
E. Boutet, D. Lieberherr, M. Tognolli, M. Schneider, and A. Bairoch, “Uniprotkb/swiss-prot: the manually annotated section of the uniprot knowledgebase,” Plant bioinformatics: methods and protocols , 2007
2007
Earlier work this paper cites.
D. Rogers and M. Hahn, “Extended-connectivity fingerprints,” Journal of chemical information and modeling , vol. 50, no. 5, pp. 742–754, 2010
2010
Earlier work this paper cites.
J. J. Irwin, T. Sterling, M. M. Mysinger, E. S. Bolstad, and R. G. Coleman, “Zinc: a free tool to discover chemistry for biology,” Journal of chemical information and modeling , vol. 52, no. 7, pp. 1757–1768, 2012
2012
Earlier work this paper cites.
K. Canese and S. Weis, “Pubmed: the bibliographic database,” The NCBI handbook , vol. 2, no. 1, 2013
2013
Earlier work this paper cites.
S. Heller, A. McNaught, S. Stein, D. Tchekhovskoi, and I. Pletnev, “Inchi-the worldwide chemical structure identifier standard,” Journal of cheminformatics , vol. 5, no. 1, pp. 1–9, 2013
2013
Earlier work this paper cites.
G. Landrum et al. , “Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling,” Greg Landrum , vol. 8, p. 31, 2013
2013
Earlier work this paper cites.
2014
Earlier work this paper cites.
B. Aydin, Y. S. Y. Y. S. Yilmaz, Y. Li, Q. Li, J. Gao, and M. Demirbas, “Crowdsourcing for multiple-choice question answering,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 28, no. 2, 2014, pp. 2946–2953
2014
Earlier work this paper cites.
Z. Lin, M. Feng, C. N. dos Santos, M. Yu, B. Xiang, B. Zhou, and Y. Bengio, “A structured self-attentive sentence embedding,” in International Conference on Learning Representations , 2016
2016
Earlier work this paper cites.
A. E. Johnson, T. J. Pollard, L. Shen, L.-w. H. Lehman, M. Feng, M. Ghassemi, B. Moody, P. Szolovits, L. Anthony Celi, and R. G. Mark, “Mimic-iii, a freely accessible critical care database,” Scientific data , vol. 3, no. 1, pp. 1–9, 2016
2016
Earlier work this paper cites.
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, and I. Polosukhin, “Attention is all you need,” in NIPS , 2017, pp. 5998–6008
2017
Earlier work this paper cites.
N. O’Boyle and A. Dalke, “Deepsmiles: an adaptation of smiles for use in machine-learning of chemical structures,” ChemRxiv , 2018
2018
Earlier work this paper cites.
S. Jaeger, S. Fulle, and S. Turk, “Mol2vec: unsupervised machine learning approach with chemical intuition,” Journal of chemical information and modeling , vol. 58, no. 1, pp. 27–35, 2018
2018
Earlier work this paper cites.
Z. Wu, B. Ramsundar, E. N. Feinberg, J. Gomes, C. Geniesse, A. S. Pappu, K. Leswing, and V. Pande, “Moleculenet: a benchmark for molecular machine learning,” Chemical science , vol. 9, no. 2, pp. 513–530, 2018
2018
Earlier work this paper cites.
A. Radford, K. Narasimhan, T. Salimans, I. Sutskever et al. , “Improving language understanding by generative pre-training,” 2018
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
J. Devlin, M. Chang, K. Lee, and K. Toutanova, “BERT: pre-training of deep bidirectional transformers for language understanding,” in NAACL-HLT (1) . Association for Computational Linguistics, 2019, pp. 4171–4186
2019
Earlier work this paper cites.
I. Beltagy, K. Lo, and A. Cohan, “Scibert: A pretrained language model for scientific text,” in EMNLP/IJCNLP (1) . Association for Computational Linguistics, 2019, pp. 3613–3618
2019
Earlier work this paper cites.
E. Alsentzer, J. Murphy, W. Boag, W.-H. Weng, D. Jindi, T. Naumann, and M. McDermott, “Publicly available clinical bert embeddings,” in Proceedings of the 2nd Clinical Natural Language Processing Workshop , 2019, pp. 72–78
2019
Earlier work this paper cites.
Y. Peng, S. Yan, and Z. Lu, “Transfer learning in biomedical natural language processing: An evaluation of BERT and elmo on ten benchmarking datasets,” in BioNLP@ACL . Association for Computational Linguistics, 2019, pp. 58–65
2019
Earlier work this paper cites.
A. Radford, J. Wu, R. Child, D. Luan, D. Amodei, I. Sutskever et al. , “Language models are unsupervised multitask learners,” OpenAI blog , vol. 1, no. 8, p. 9, 2019
2019
Earlier work this paper cites.
R. Sever, T. Roeder, S. Hindle, L. Sussman, K.-J. Black, J. Argentine, W. Manos, and J. R. Inglis, “biorxiv: the preprint server for biology,” BioRxiv , p. 833400, 2019
2019
Earlier work this paper cites.
Q. Jin, B. Dhingra, Z. Liu, W. W. Cohen, and X. Lu, “Pubmedqa: A dataset for biomedical research question answering,” in EMNLP/IJCNLP (1) . Association for Computational Linguistics, 2019, pp. 2567–2577
2019
Earlier work this paper cites.
R. Rao, N. Bhattacharya, N. Thomas, Y. Duan, X. Chen, J. F. Canny, P. Abbeel, and Y. S. Song, “Evaluating protein transfer learning with TAPE,” in NeurIPS , 2019, pp. 9686–9698
2019
Earlier work this paper cites.
2020
Earlier work this paper cites.
J. Lee, W. Yoon, S. Kim, D. Kim, S. Kim, C. H. So, and J. Kang, “Biobert: a pre-trained biomedical language representation model for biomedical text mining,” Bioinform. , vol. 36, no. 4, pp. 1234–1240, 2020
2020
Earlier work this paper cites.
H. Shin, Y. Zhang, E. Bakhturina, R. Puri, M. Patwary, M. Shoeybi, and R. Mani, “Biomegatron: Larger biomedical domain language model,” in EMNLP (1) . Association for Computational Linguistics, 2020, pp. 4700–4706
2020
Earlier work this paper cites.
M. Krenn, F. Häse, A. Nigam, P. Friederich, and A. Aspuru-Guzik, “Self-referencing embedded strings (selfies): A 100% robust molecular string representation,” Machine Learning: Science and Technology , vol. 1, no. 4, p. 045024, 2020
2020
Earlier work this paper cites.
Z. Wu, S. Pan, F. Chen, G. Long, C. Zhang, and S. Y. Philip, “A comprehensive survey on graph neural networks,” IEEE transactions on neural networks and learning systems , vol. 32, no. 1, pp. 4–24, 2020
2020
Earlier work this paper cites.
J. Zhou, G. Cui, S. Hu, Z. Zhang, C. Yang, Z. Liu, L. Wang, C. Li, and M. Sun, “Graph neural networks: A review of methods and applications,” AI open , vol. 1, pp. 57–81, 2020
2020
Earlier work this paper cites.
C. Raffel, N. Shazeer, A. Roberts, K. Lee, S. Narang, M. Matena, Y. Zhou, W. Li, and P. J. Liu, “Exploring the limits of transfer learning with a unified text-to-text transformer,” The Journal of Machine Learning Research , vol. 21, no. 1, pp. 5485–5551, 2020
2020
Earlier work this paper cites.
T. B. Brown, B. Mann, N. Ryder, M. Subbiah, J. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell, S. Agarwal, A. Herbert-Voss, G. Krueger, T. Henighan, R. Child, A. Ramesh, D. M. Ziegler, J. Wu, C. Winter, C. Hesse, M. Chen, E. Sigler, M. Litwin, S. Gray, B. Chess, J. Clark, C. Berner, S. McCandlish, A. Radford, I. Sutskever, and D. Amodei, “Language models are few-shot learners,” in NeurIPS , 2020
2020
Earlier work this paper cites.
M. Lewis, Y. Liu, N. Goyal, M. Ghazvininejad, A. Mohamed, O. Levy, V. Stoyanov, and L. Zettlemoyer, “BART: denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension,” in ACL . Association for Computational Linguistics, 2020, pp. 7871–7880
2020
Earlier work this paper cites.
C. Raffel, N. Shazeer, A. Roberts, K. Lee, S. Narang, M. Matena, Y. Zhou, W. Li, and P. J. Liu, “Exploring the limits of transfer learning with a unified text-to-text transformer,” J. Mach. Learn. Res. , vol. 21, pp. 140:1–140:67, 2020
2020
Earlier work this paper cites.
K. Lo, L. L. Wang, M. Neumann, R. Kinney, and D. S. Weld, “S2ORC: the semantic scholar open research corpus,” in ACL . Association for Computational Linguistics, 2020, pp. 4969–4983
2020
Earlier work this paper cites.
A. Nentidis, K. Bougiatiotis, A. Krithara, and G. Paliouras, “Results of the seventh edition of the bioasq challenge,” in Machine Learning and Knowledge Discovery in Databases: International Workshops of ECML PKDD 2019, Würzburg, Germany, September 16–20, 2019, Proceedings, Part II . Springer, 2020, pp. 553–568
2020
Earlier work this paper cites.
P. S. H. Lewis, E. Perez, A. Piktus, F. Petroni, V. Karpukhin, N. Goyal, H. Küttler, M. Lewis, W. Yih, T. Rocktäschel, S. Riedel, and D. Kiela, “Retrieval-augmented generation for knowledge-intensive NLP tasks,” in NeurIPS , 2020
2020
Earlier work this paper cites.
A. Rives, J. Meier, T. Sercu, S. Goyal, Z. Lin, J. Liu, D. Guo, M. Ott, C. L. Zitnick, J. Ma et al. , “Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences,” Proceedings of the National Academy of Sciences , vol. 118, no. 15, p. e2016239118, 2021
2021
Earlier work this paper cites.
C. Ying, T. Cai, S. Luo, S. Zheng, G. Ke, D. He, Y. Shen, and T. Liu, “Do transformers really perform badly for graph representation?” in NeurIPS , 2021, pp. 28 877–28 888
2021
Earlier work this paper cites.
J. Jumper, R. Evans, A. Pritzel, T. Green, M. Figurnov, O. Ronneberger, K. Tunyasuvunakool, R. Bates, A. Žídek, A. Potapenko et al. , “Highly accurate protein structure prediction with alphafold,” Nature , vol. 596, no. 7873, pp. 583–589, 2021
2021
Earlier work this paper cites.
S. Alrowili and V. Shanker, “Biom-transformers: Building large biomedical language models with bert, ALBERT and ELECTRA,” in BioNLP@NAACL-HLT . Association for Computational Linguistics, 2021, pp. 221–227
2021
Earlier work this paper cites.
2021
Earlier work this paper cites.
C. Edwards, C. Zhai, and H. Ji, “Text2mol: Cross-modal molecule retrieval with natural language queries,” in EMNLP (1) . Association for Computational Linguistics, 2021, pp. 595–607
2021
Earlier work this paper cites.
C. Bodnar, F. Frasca, N. Otter, Y. Wang, P. Lio, G. F. Montufar, and M. Bronstein, “Weisfeiler and lehman go cellular: Cw networks,” Advances in Neural Information Processing Systems , vol. 34, pp. 2625–2640, 2021
2021
Earlier work this paper cites.
B. Jing, S. Eismann, P. Suriana, R. J. L. Townshend, and R. O. Dror, “Learning from protein structure with geometric vector perceptrons,” in ICLR . OpenReview.net, 2021
2021
Earlier work this paper cites.
B. Lester, R. Al-Rfou, and N. Constant, “The power of scale for parameter-efficient prompt tuning,” in EMNLP (1) . Association for Computational Linguistics, 2021, pp. 3045–3059
2021
Earlier work this paper cites.
Y. Qiu, Y. Zhang, Y. Deng, S. Liu, and W. Zhang, “A comprehensive review of computational methods for drug-drug interaction detection,” IEEE/ACM transactions on computational biology and bioinformatics , vol. 19, no. 4, pp. 1968–1985, 2021
2021
Earlier work this paper cites.
L. Hu, X. Wang, Y.-A. Huang, P. Hu, and Z.-H. You, “A survey on computational models for predicting protein–protein interactions,” Briefings in bioinformatics , vol. 22, no. 5, p. bbab036, 2021
2021
Earlier work this paper cites.
M. Bagherian, E. Sabeti, K. Wang, M. A. Sartor, Z. Nikolovska-Coleska, and K. Najarian, “Machine learning approaches and databases for prediction of drug–target interaction: a survey paper,” Briefings in bioinformatics , vol. 22, no. 1, pp. 247–269, 2021
2021
Earlier work this paper cites.
K. Huang, T. Fu, W. Gao, Y. Zhao, Y. Roohani, J. Leskovec, C. W. Coley, C. Xiao, J. Sun, and M. Zitnik, “Therapeutics data commons: Machine learning datasets and tasks for drug discovery and development,” in NeurIPS Datasets and Benchmarks , 2021
2021
Earlier work this paper cites.
C. Dallago, J. Mou, K. E. Johnston, B. J. Wittmann, N. Bhattacharya, S. Goldman, A. Madani, and K. K. Yang, “FLIP: benchmark tasks in fitness landscape inference for proteins,” in NeurIPS Datasets and Benchmarks , 2021
2021
Earlier work this paper cites.
M. Xu, Z. Zhang, J. Lu, Z. Zhu, Y. Zhang, C. Ma, R. Liu, and J. Tang, “PEER: A comprehensive and multi-task benchmark for protein sequence understanding,” in NeurIPS , 2022
2022
Earlier work this paper cites.
Y. Wang, J. Wang, Z. Cao, and A. B. Farimani, “Molecular contrastive learning of representations via graph neural networks,” Nat. Mach. Intell. , vol. 4, no. 3, pp. 279–287, 2022
2022
Cited alongside, same era.
J. Dauparas, I. Anishchenko, N. Bennett, H. Bai, R. J. Ragotte, L. F. Milles, B. I. Wicky, A. Courbet, R. J. de Haas, N. Bethel et al. , “Robust deep learning–based protein sequence design using proteinmpnn,” Science , vol. 378, no. 6615, pp. 49–56, 2022
2022
Cited alongside, same era.
C. Edwards, T. M. Lai, K. Ros, G. Honke, K. Cho, and H. Ji, “Translation between molecules and natural language,” in EMNLP . Association for Computational Linguistics, 2022, pp. 375–413
2022
Cited alongside, same era.
Z. Zeng, Y. Yao, Z. Liu, and M. Sun, “A deep-learning system bridging molecule structure and biomedical text with comprehension comparable to human professionals,” Nature communications , vol. 13, no. 1, p. 862, 2022
2022
Cited alongside, same era.
Z. Liu, S. Li, Y. Luo, H. Fei, Y. Cao, K. Kawaguchi, X. Wang, and T. Chua, “Molca: Molecular graph-language modeling with cross-modal projector and uni-modal adapter,” in EMNLP . Association for Computational Linguistics, 2023, pp. 15 623–15 638
2023
Later among the works it cites.
2023
Later among the works it cites.
H. Zhao, S. Liu, C. Ma, H. Xu, J. Fu, Z.-H. Deng, L. Kong, and Q. Liu, “GIMLET: A unified graph-text model for instruction-based molecule zero-shot learning,” in NeurIPS , 2023
2023
Later among the works it cites.
W. Zhao, D. Zhou, B. Cao, K. Zhang, and J. Chen, “Adversarial modality alignment network for cross-modal molecule retrieval,” IEEE Transactions on Artificial Intelligence , 2023
2023
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Y. Gu, R. Tinn, H. Cheng, M. Lucas, N. Usuyama, X. Liu, T. Naumann, J. Gao, and H. Poon, “Domain-specific language model pretraining for biomedical natural language processing,” ACM Trans. Comput. Heal. , vol. 3, no. 1, pp. 2:1–2:23, 2022
2022
Cited alongside, same era.
M. Yasunaga, J. Leskovec, and P. Liang, “Linkbert: Pretraining language models with document links,” in ACL (1) . Association for Computational Linguistics, 2022, pp. 8003–8016
2022
Cited alongside, same era.
2022
Cited alongside, same era.
R. Luo, L. Sun, Y. Xia, T. Qin, S. Zhang, H. Poon, and T. Liu, “Biogpt: generative pre-trained transformer for biomedical text generation and mining,” Briefings Bioinform. , vol. 23, no. 6, 2022
2022
Cited alongside, same era.
E. Bolton, D. Hall, M. Yasunaga, T. Lee, C. Manning, and P. Liang, “Biomedlm,” 2022. [Online]. Available: https://crfm.stanford.edu/2022/12/15/biomedlm.html
2022
Cited alongside, same era.
2022
Cited alongside, same era.
M. Yasunaga, A. Bosselut, H. Ren, X. Zhang, C. D. Manning, P. Liang, and J. Leskovec, “Deep bidirectional language-knowledge graph pretraining,” in NeurIPS , 2022
2022
Cited alongside, same era.
2022
Cited alongside, same era.
2023
Later among the works it cites.
X. Tang, A. Tran, J. Tan, and M. B. Gerstein, “Mollm: A unified language model to integrate biomedical text with 2d and 3d molecular representations,” bioRxiv , pp. 2023–11, 2023
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
H. Abdine, M. Chatzianastasis, C. Bouyioukos, and M. Vazirgiannis, “Prot2text: Multimodal protein’s function generation with gnns and transformers,” in NeurIPS 2023 AI for Science Workshop , 2023
2023
Later among the works it cites.
M. Xu, X. Yuan, S. Miret, and J. Tang, “Protst: Multi-modality learning of protein sequences and biomedical texts,” in ICML , ser. Proceedings of Machine Learning Research, vol. 202. PMLR, 2023, pp. 38 749–38 767
2023
Later among the works it cites.
H. Guo, M. Huo, R. Zhang, and P. Xie, “Proteinchat: Towards achieving chatgpt-like functionalities on protein 3d structures,” 2023
2023
Later among the works it cites.
H. Xu, A. Woicik, H. Poon, R. B. Altman, and S. Wang, “Multilingual translation for zero-shot biomedical classification using biotranslator,” Nature Communications , vol. 14, no. 1, p. 738, 2023
2023
Later among the works it cites.
2023
Later among the works it cites.
Z. Guo, K. Guo, B. Nan, Y. Tian, R. G. Iyer, Y. Ma, O. Wiest, X. Zhang, W. Wang, C. Zhang, and N. V. Chawla, “Graph-based molecular representation learning,” in IJCAI . ijcai.org, 2023, pp. 6638–6646
2023
Later among the works it cites.
J. Zhu, K. Wu, B. Wang, Y. Xia, S. Xie, Q. Meng, L. Wu, T. Qin, W. Zhou, H. Li, and T. Liu, “O-gnn: incorporating ring priors into molecular modeling,” in ICLR . OpenReview.net, 2023
2023
Later among the works it cites.
S. Luo, T. Chen, Y. Xu, S. Zheng, T. Liu, L. Wang, and D. He, “One transformer can understand both 2d & 3d molecular data,” in ICLR . OpenReview.net, 2023
2023
Later among the works it cites.
2023
Later among the works it cites.
J. Zhu, Y. Xia, L. Wu, S. Xie, W. Zhou, T. Qin, H. Li, and T.-Y. Liu, “Dual-view molecular pre-training,” in Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining , 2023, pp. 3615–3627
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
C. M. Castro Nascimento and A. S. Pimentel, “Do large language models understand chemistry? a conversation with chatgpt,” Journal of Chemical Information and Modeling , vol. 63, no. 6, pp. 1649–1655, 2023
2023
Later among the works it cites.
2023
Later among the works it cites.
W. Lin, H. Wang, H. Xiao, and Q. Ye, “OPI: Exploring and Benchmarking Large Language Models for Protein Modeling,” 2023. [Online]. Available: https://github.com/baaihealth/opi
2023
Later among the works it cites.
C. W. Kosonocky, C. O. Wilke, E. M. Marcotte, and A. D. Ellington, “Mining patents with large language models elucidates the chemical function landscape,” ArXiv , 2023
2023
Later among the works it cites.
K. M. Jablonka, Q. Ai, A. Al-Feghali, S. Badhwar, J. D. Bocarsly, A. M. Bran, S. Bringuier, L. C. Brinson, K. Choudhary, D. Circi et al. , “14 examples of how llms can transform materials science and chemistry: a reflection on a large language model hackathon,” Digital Discovery , vol. 2, no. 5, pp. 1233–1250, 2023
2023
Later among the works it cites.
D. S. Wishart, S. Girod, H. Peters, E. Oler, J. Jovel, Z. Budinski, R. Milford, V. W. Lui, Z. Sayeeda, R. Mah et al. , “Chemfont: the chemical functional ontology resource,” Nucleic Acids Research , vol. 51, no. D1, pp. D1220–D1229, 2023
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
H. Liu, C. Li, Q. Wu, and Y. J. Lee, “Visual instruction tuning,” Advances in neural information processing systems , vol. 36, 2024
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
T. Guo, B. Nan, Z. Liang, Z. Guo, N. Chawla, O. Wiest, X. Zhang et al. , “What can large language models do in chemistry? a comprehensive benchmark on eight tasks,” Advances in Neural Information Processing Systems , vol. 36, 2024
2024
Closest in time.
2024
Closest in time.
H. Qiu, L. Liu, X. Qiu, X. Dai, X. Ji, and Z.-Y. Sun, “Polync: a natural and chemical language model for the prediction of unified polymer properties,” Chemical Science , vol. 15, no. 2, pp. 534–544, 2024
2024
Closest in time.
S. Kim, J. Nam, S. Yu, Y. Shin, and J. Shin, “Data-efficient molecular generation with hierarchical textual inversion,” 2024. [Online]. Available: https://openreview.net/forum?id=wwotGBxtC3
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
S. Li, Z. Liu, Y. Luo, X. Wang, X. He, K. Kawaguchi, T.-S. Chua, and Q. Tian, “Towards 3d molecule-text interpretation in language models,” in ICLR . Openreview.net, 2024
2024
Closest in time.
P. Liu, Y. Ren, J. Tao, and Z. Ren, “Git-mol: A multi-modal large language model for molecular science with graph, image, and text,” Computers in Biology and Medicine , p. 108073, 2024
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
Y. Luo, S. Li, Z. Liu, J. Wu, Z. Yang, X. He, X. Wang, and Q. Tian, “Text-guided diffusion model for 3d molecule generation,” 2024. [Online]. Available: https://openreview.net/forum?id=FdUloEgBSE
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
C. Wang, H. Fan, R. Quan, and Y. Yang, “Protchatgpt: Towards understanding proteins with large language models,” 2024
2024
Closest in time.
2024
Closest in time.
S. Liu, J. Wang, Y. Yang, C. Wang, L. Liu, H. Guo, and C. Xiao, “Conversational drug editing using retrieval and domain feedback,” in ICLR . Openreview.net, 2024
2024
Closest in time.
Y. Fang, X. Liang, N. Zhang, K. Liu, R. Huang, Z. Chen, X. Fan, and H. Chen, “Mol-instructions - a large-scale biomolecular instruction dataset for large language models,” in ICLR . Openreview.net, 2024
2024
Closest in time.
Z. Wang, Z. Wang, B. Srinivasan, V. N. Ioannidis, H. Rangwala, and R. ANUBHAI, “Biobridge: Bridging biomedical foundation models via knowledge graph,” in ICLR . Openreview.net, 2024
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
C. Edwards, Q. Wang, L. Zhao, and H. Ji, “L+ m-24: Building a dataset for language+ molecules@ acl 2024.”
2024
Closest in time.
I. Jahan, M. T. R. Laskar, C. Peng, and J. X. Huang, “A comprehensive evaluation of large language models on benchmark biomedical text processing tasks,” Computers in Biology and Medicine , p. 108189, 2024
2024
Closest in time.
K. M. Jablonka, P. Schwaller, A. Ortega-Guerrero, and B. Smit, “Leveraging large language models for predictive chemistry,” Nature Machine Intelligence , pp. 1–9, 2024
2024
Closest in time.