Summarize this content to 100 words: Walters, W. P. & Barzilay, R. Applications of deep learning in molecule generation and molecular property prediction. Acc. Chem. Res. 54, 263–270 (2020).
Google Scholar
Shen, J. & Nicolaou, C. A. Molecular property prediction: Recent trends in the era of artificial intelligence. Drug Discov. Today. Technol. 32, 29–36 (2019).
Google Scholar
Ryu, J. Y., Kim, H. U. & Lee, S. Y. Deep learning improves prediction of drug-drug and drug-food interactions. Proc. Natl. Acad. Sci. USA 115, E4304–E4311 (2018).
Google Scholar
Lin, X., Quan, Z., Wang, Z.-J., Ma, T. & Zeng, X. Kgnn: Knowledge graph neural network for drug-drug interaction prediction. In IJCAI, 380, 2739–2745 (2020).Qiu, Y., Zhang, Y., Deng, Y., Liu, S. & Zhang, W. A comprehensive review of computational methods for drug-drug interaction detection. IEEE/ACM Trans. Comput. Biol. Bioinforma. 19, 1968–1985 (2021).
Google Scholar
Bagherian, M. et al. Machine learning approaches and databases for prediction of drug–target interaction: a survey paper. Brief. Bioinforma. 22, 247–269 (2021).
Google Scholar
Chen, X. et al. Drug–target interaction prediction: databases, web servers and computational models. Brief. Bioinforma. 17, 696–712 (2016).
Google Scholar
Todeschini, R. & Consonni, V. Handbook of molecular descriptors (John Wiley & Sons, 2008).Skoraczyński, G. et al. Predicting the outcomes of organic reactions via machine learning: are current descriptors sufficient? Sci. Rep. 7, 3582 (2017).
Google Scholar
Lu, C. et al. Molecular property prediction: a multilevel quantum interactions modeling perspective. In Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence and Thirty-First Innovative Applications of Artificial Intelligence Conference and Ninth AAAI Symposium on Educational Advances in Artificial Intelligence, 1052–1060 (2019).Wang, Z. et al. Advanced graph and sequence neural networks for molecular property prediction and drug discovery. Bioinformatics 38, 2579–2586 (2022).
Google Scholar
Bjerrum, E. J. Smiles enumeration as data augmentation for neural network modeling of molecules. arXiv preprint arXiv:1703.07076 (2017).Quan, Z. et al. A system for learning atoms based on long short-term memory recurrent neural networks. In 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 728–733 (IEEE, 2018).Weininger, D. Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. J. Chem. Inf. Comput. Sci. 28, 31–36 (1988).
Google Scholar
Gilmer, J., Schoenholz, S. S., Riley, P. F., Vinyals, O. & Dahl, G. E. Neural message passing for quantum chemistry. In International conference on machine learning, 1263–1272 (PMLR, 2017).Xiong, Z. et al. Pushing the boundaries of molecular representation for drug discovery with the graph attention mechanism. J. Med. Chem. 63, 8749–8760 (2019).
Google Scholar
Schütt, K. et al. Schnet: A continuous-filter convolutional neural network for modeling quantum interactions. Advances in neural information processing systems, 30, (2017).Gasteiger, J., Groß, J. & Günnemann, S. Directional message passing for molecular graphs. In International Conference on Learning Representations (2020).Hu, Z., Dong, Y., Wang, K., Chang, K.-W. & Sun, Y. Gpt-gnn: Generative pre-training of graph neural networks. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 1857–1867 (2020).Hu, W. et al. Strategies for pre-training graph neural networks. In International Conference on Learning Representations (2020).Zaidi, S. et al. Pre-training via denoising for molecular property prediction. In International Conference on Learning Representations (2023).Chithrananda, S., Grand, G. & Ramsundar, B. Chemberta: large-scale self-supervised pretraining for molecular property prediction. arXiv preprint arXiv:2010.09885 (2020).Ross, J. et al. Large-scale chemical language representations capture molecular structure and properties. Nat. Mach. Intell. 4, 1256–1264 (2022).
Google Scholar
Wang, Y., Wang, J., Cao, Z. & Barati Farimani, A. Molecular contrastive learning of representations via graph neural networks. Nat. Mach. Intell. 4, 279–287 (2022).
Google Scholar
You, Y. et al. Graph contrastive learning with augmentations. Adv. neural Inf. Process. Syst. 33, 5812–5823 (2020).
Google Scholar
You, Y., Chen, T., Shen, Y. & Wang, Z. Graph contrastive learning automated. In International Conference on Machine Learning, 12121–12132 (PMLR, 2021).Stärk, H. et al. 3d infomax improves gnns for molecular property prediction. In International Conference on Machine Learning, 20479–20502 (PMLR, 2022).Liu, S. et al. Multi-modal molecule structure–text model for text-based retrieval and editing. Nat. Mach. Intell. 5, 1447–1457 (2023).
Google Scholar
Su, B. et al. A molecular multimodal foundation model associating molecule graphs with natural language. arXiv preprint arXiv:2209.05481 (2022).Liu, S. et al. Pre-training molecular graph representation with 3d geometry. In International Conference on Learning Representations (2021).Zeng, Z., Yao, Y., Liu, Z. & Sun, M. A deep-learning system bridging molecule structure and biomedical text with comprehension comparable to human professionals. Nat. Commun. 13, 862 (2022).
Google Scholar
Liu, S., Du, W., Ma, Z.-M., Guo, H. & Tang, J. A group symmetric stochastic differential equation model for molecule multi-modal pretraining. In International Conference on Machine Learning, 21497–21526 (PMLR, 2023).Luo, Y., Yang, K., Hong, M., Liu, X. & Nie, Z. Molfm: A multimodal molecular foundation model. arXiv preprint arXiv:2307.09484 (2023).Zhu, Y. et al. Featurizations matter: a multiview contrastive learning approach to molecular pretraining. In ICML 2022 2nd AI for Science Workshop (2022).Bai, P., Miljković, F., John, B. & Lu, H. Interpretable bilinear attention network with domain adaptation improves drug–target prediction. Nat. Mach. Intell. 5, 126–136 (2023).
Google Scholar
Van der Maaten, L. & Hinton, G. Visualizing data using t-sne. J. Mach. Learning Res.9, 2579–2605 (2008).Long, Q., Wang, M. & Li, L. Generative imagination elevates machine translation. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 5738–5748 (2021).Xia, J. et al. Mole-bert: Rethinking pre-training graph neural networks for molecules. In The Eleventh International Conference on Learning Representations (2022).Zang, X., Zhao, X. & Tang, B. Hierarchical molecular graph self-supervised learning for property prediction. Commun. Chem. 6, 34 (2023).
Google Scholar
McCune, D. F., Gaivin, R. J., Rorabaugh, B. R. & Perez, D. M. Bulk is a determinant of oxymetazoline affinity for the α1a-adrenergic receptor. Receptors Channels 10, 109–116 (2004).
Google Scholar
Wang, T. & Isola, P. Understanding contrastive representation learning through alignment and uniformity on the hypersphere. In International Conference on Machine Learning, 9929–9939 (PMLR, 2020).Fang, Y. et al. Knowledge graph-enhanced molecular contrastive learning with functional prompt. Nat. Mach. Intel. 5, 542–553 (2023).BOTEV, Z., GROTOWSKI, J. & KROESE, D. Kernel density estimation via diffusion. Ann. Stat. 38, 2916–2957 (2010).
Google Scholar
Chen, M. et al. Dilirank: the largest reference drug list ranked by the risk for developing drug-induced liver injury in humans. Drug Discov. Today 21, 648–653 (2016).
Google Scholar
Goldberg, B. & Stern, A. The mechanism of oxidative hemolysis produced by phenylhydrazine. Mol. Pharmacol. 13, 832–839 (1977).
Google Scholar
García Marín, I. D. et al. New compounds from heterocyclic amines scaffold with multitarget inhibitory activity on aβ aggregation, ache, and bace1 in the alzheimer disease. Plos one 17, e0269129 (2022).
Google Scholar
Di, L. & Kerns, E. H. Drug-like properties: concepts, structure design and methods from ADME to toxicity optimization (Academic Press, 2015).Black, W. et al. From indomethacin to a selective cox-2 inhibitor: development of indolalkanoic acids as potent and selective cyclooxygenase-2 inhibitors. Bioorg. Med. Chem. Lett. 6, 725–730 (1996).
Google Scholar
Bavry, A. A. et al. Harmful effects of nsaids among patients with hypertension and coronary artery disease. Am. J. Med. 124, 614–620 (2011).
Google Scholar
Banik, M., Gopi, S. P., Ganguly, S. & Desiraju, G. R. Cocrystal and salt forms of furosemide: solubility and diffusion variations. Cryst. Growth Des. 16, 5418–5428 (2016).
Google Scholar
Goud, N. R. et al. Novel furosemide cocrystals and selection of high solubility drug forms. J. Pharm. Sci. 101, 664–680 (2012).
Google Scholar
Harriss, B. I., Vella-Zarb, L., Wilson, C. & Evans, I. R. Furosemide cocrystals: Structures, hydrogen bonding, and implications for properties. Cryst. growth Des. 14, 783–791 (2014).
Google Scholar
Karaytuğ, M. O. et al. Piperazine derivatives with potent drug moiety as efficient acetylcholinesterase, butyrylcholinesterase, and glutathione s-transferase inhibitors. J. Biochem. Mol. Toxicol. 37, e23259 (2023).
Google Scholar
Deshler, L. & Zuman, P. Polarographic reduction of aldehydes and ketones: Part xviii. ethacrynic acid. Analytica Chim. Acta 73, 337–354 (1974).
Google Scholar
Litwin, A., Adams, L. E., Zimmer, H. & Hess, E. V. Immunologic effects of hydralazine in hypertensive patients. Arthritis Rheumatism J. Am. Coll. Rheumatol. 24, 1074–1077 (1981).
Google Scholar
Main, B. G. & Tucker, H. 3 recent advances in β-adrenergic blocking agents. Prog. Med. Chem. 22, 121–164 (1985).
Google Scholar
Shrivastav, P. S., Buha, S. M. & Sanyal, M. Detection and quantitation of β-blockers in plasma and urine. Bioanalysis 2, 263–276 (2010).
Google Scholar
Rampášek, L. et al. Recipe for a general, powerful, scalable graph transformer. Adv. Neural Inf. Process. Syst. 35, 14501–14515 (2022).
Google Scholar
Wang, L., Liu, Y., Lin, Y., Liu, H. & Ji, S. Comenet: Towards complete and efficient message passing for 3d molecular graphs. In Koyejo, S. et al. (eds.) Advances in Neural Information Processing Systems, vol. 35, 650–664 (Curran Associates, Inc., 2022).Gu, Y. et al. Domain-specific language model pretraining for biomedical natural language processing. ACM Trans. Comput. Healthc. (HEALTH) 3, 1–23 (2021).
Google Scholar
Chen, T., Kornblith, S., Norouzi, M. & Hinton, G. A simple framework for contrastive learning of visual representations. In International conference on machine learning, 1597–1607 (PMLR, 2020).Ganin, Y. & Lempitsky, V. Unsupervised domain adaptation by backpropagation. In International conference on machine learning, 1180–1189 (PMLR, 2015).Wang, H. et al. Multi-modal learning with missing modality via shared-specific feature modelling. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 15878–15887 (2023).Wishart, D. S. et al. Drugbank 5.0: a major update to the drugbank database for 2018. Nucleic acids Res. 46, D1074–D1082 (2018).
Google Scholar
Wu, Z. et al. Moleculenet: A benchmark for molecular machine learning. Chem. Sci. 9, 513–530 (2018).
Google Scholar
Xiong, Z. et al. Multi-relational contrastive learning graph neural network for drug-drug interaction event prediction. In Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence. 5339–5347 (2023).Gilson, M. K. et al. Bindingdb in 2015: A public database for medicinal chemistry, computational chemistry and systems pharmacology. Nucleic acids Res. 44, D1045–D1053 (2016).
Google Scholar
Zitnik, M., Sosic, R. & Leskovec, J. Biosnap datasets: Stanford biomedical network dataset collection. http://snap.stanford.edu/biodata Cited by 5 (2018).Yu, Q. et al. Multimodal molecular pretraining via modality blending. In The Twelfth International Conference on Learning Representations.Hamilton, W., Ying, Z. & Leskovec, J. Inductive representation learning on large graphs. Advances in neural information processing systems 30, (2017).Sun, F.-Y., Hoffmann, J., Verma, V. & Tang, J. Infograph: Unsupervised and semi-supervised graph-level representation learning via mutual information maximization. In International Conference on Learning Representations (2020).Xu, M., Wang, H., Ni, B., Guo, H. & Tang, J. Self-supervised graph-level representation learning with local and global structure. In International Conference on Machine Learning, 11548–11558 (PMLR, 2021).Rong, Y. et al. Self-supervised graph transformer on large-scale molecular data. Adv. Neural Inf. Process. Syst. 33, 12559–12571 (2020).
Google Scholar
Yu, H., Zhao, S. & Shi, J. STNN-DDI: A substructure-aware tensor neural network to predict drug-drug interactions. Brief. Bioinforma. 23, bbac209 (2022).
Google Scholar
Nyamabo, A. K., Yu, H. & Shi, J.-Y. SSI-DDI: Substructure-substructure interactions for drug-drug interaction prediction. Brief. Bioinf. https://doi.org/10.1093/bib/bbab133 (2021).Nyamabo, A. K., Yu, H., Liu, Z. & Shi, J.-Y. Drug-drug interaction prediction with learnable size-adaptive molecular substructures. Brief. Bioinf. https://doi.org/10.1093/bib/bbab441 (2021).Yang, Z., Zhong, W., Lv, Q. & Chen, C. Y.-C. Learning size-adaptive molecular substructures for explainable drug-drug interaction prediction by substructure-aware graph neural network. Chem. Sci. 13, 8693–8703 (2022).
Google Scholar
Zhu, X., Shen, Y. & Lu, W. Molecular Substructure-Aware Network for Drug-Drug Interaction Prediction. In Proceedings of the 31st ACM International Conference on Information & Knowledge Management, 4757–4761 (2022).Li, Z. et al. DSN-DDI: an accurate and generalized framework for drug-drug interaction prediction by dual-view representation learning. Briefings Bioinformatics 24. https://doi.org/10.1093/bib/bbac597 (2023).He, H., Chen, G. & Yu-Chian Chen, C. 3DGT-DDI: 3D graph and text based neural network for drug-drug interaction prediction. Briefings in Bioinformatics 23, https://doi.org/10.1093/bib/bbac134, https://academic.oup.com/bib/article-pdf/23/3/bbac134/43745041/bbac134.pdf (2022).Cortes, C. & Vapnik, V. Support-vector networks. Mach. Learn. 20, 273–297 (1995).
Google Scholar
Ho, T. K. Random decision forests. In Proceedings of 3rd international conference on document analysis and recognition, vol. 1, 278–282 (IEEE, 1995).Lee, I., Keum, J. & Nam, H. Deepconv-dti: Prediction of drug-target interactions via deep learning with convolution on protein sequences. PLoS Comput. Biol. 15, e1007129 (2019).
Google Scholar
Nguyen, T. et al. Graphdta: Predicting drug–target binding affinity with graph neural networks. Bioinformatics 37, 1140–1147 (2021).
Google Scholar
Huang, K., Xiao, C., Glass, L. M. & Sun, J. Moltrans: molecular interaction transformer for drug–target interaction prediction. Bioinformatics 37, 830–836 (2021).
Google Scholar
Xiong, Z. Data of multi-to-uni modal knowledge transfer pre-training for molecular representation learning: V1.0, https://doi.org/10.57967/hf/7153 (2025).Xiong, Z. Source data files for paper “multi-to-uni modal knowledge transfer pre-training for molecular representation learning. https://doi.org/10.5281/zenodo.18219572 (2026).Xiong, Z. Zhankun-xiong/m2umol: M2umol, https://doi.org/10.5281/zenodo.17798744 (2025).Irwin, R., Dimitriadis, S., He, J. & Bjerrum, E. J. Chemformer: A pre-trained transformer for computational chemistry. Mach. Learn. Sci. Technol. 3, 015022 (2022).
Google Scholar
Yang, M., Chen, T., Liu, Y.-X. & Huang, L. Visualizing set relationships: Evenn’s comprehensive approach to venn diagrams. Imeta 3, e184 (2024).
Google Scholar
Multi-to-uni modal knowledge transfer pre-training for molecular representation learning
Leave a Comment
