AgainstOTE: A generative pretrain-tune framework for de novo molecular generation against off-target effects

Yechao Xu; Wen Fang; Shuai Wang; Hai-Bin Luo; Qiang Xu; Fen-Er Chen; Jiao Qu; Yang Sun

doi:10.59717/j.xinn-med.2025.100158

The Innovation Medicine > 2025 Vol. 3 > No. 4 > 100158

Next Article Previous Article

ARTICLE Open Access

Cite PDF

AgainstOTE: A generative pretrain-tune framework for de novo molecular generation against off-target effects

1.
Department of Computer Science and Technology, Nanjing University, 163 Xianlin Avenue, Nanjing 210023, China
2.
State Key Laboratory of Pharmaceutical Biotechnology and Nanjing Drum Tower Hospital the Affiliated Hospital of Nanjing University Medical School, School of Life Sciences, Nanjing University, 163 Xianlin Avenue, Nanjing 210023, China
3.
Engineering Center of Catalysis and Synthesis for Chiral Molecules, Department of Chemistry, Fudan University, Shanghai 200433, China
4.
Shanghai Engineering Center of Industrial Asymmetric Catalysis for Chiral Drugs, Shanghai 200433, China
5.
Key Laboratory of Tropical Biological Resources of Ministry of Education and Hainan Engineering Research Center for Drug Screening and Evaluation, School of Pharmaceutical Sciences, Hainan University, Haikou 570228, China
6.
Jiangsu Key Laboratory of New Drug Research and Clinical Pharmacy, Xuzhou Medical University, Xuzhou 221004, China
7.
These authors contributed equally

Show all affliationsShow less

More Information

Corresponding authors: yangsun@nju.edu.cn (Y.S.); qujiao@nju.edu.cn (J.Q.); rfchen@fudan.edu.cn (F.C.); qiangxu@nju.edu.cn (Q.X.)
Received Date: November 30, 2024

Accepted Date: September 17, 2025

Published Online: September 24, 2025

The Innovation Medicine 3(4), https://doi.org/10.59717/j.xinn-med.2025.100158 | Cite this article

GRAPHICAL ABSTRACT

GRAPHICAL ABSTRACT

DownLoad: Full size image

PUBLIC SUMMARY

PUBLIC SUMMARY
1. Preclinical AI-based drug discovery plays an important role in the development of innovative drugs.
  
  Our pre-experiments reveal off-target effects in AI produced molecules, highly risking clinical trial failures.
  
  A generative pretrain-tune framework is proposed to generate potential candidates against off-target effects.
  
  AI simulation reveals superior chemical properties and inhibited off-target binding affinity.
  
  Biological assay is carried out after chemical synthesis of predicted receptor-selective molecules.

ABSTRACT

ABSTRACT

De novo molecular generation is committed by the mission to create novel molecules from scratch with desired properties. Learning from massive protein-molecule data, generative models can serve drug discovery, accelerating the development of innovative medical treatment and products curing stubborn diseases. Previous research celebrates phased success by optimizing the joint distribution of molecule-protein pairs. However, our preliminary experiments show that AI-generated molecules exhibit off-target effects, potentially leading to the failure of clinical trials. This indicates precisely binding to the target yet not to others far from settled. Chemical candidate space may be quite narrow, instead of vastly searching only conditioned on target proteins. In this study, we present a generative pretrain-tune framework for de novo molecular generation against off-target effects (AgainstOTE). For pretraining an initialized generative model which is called AgainstOTE-R1 in this context, it exploits the dual-receptor cooperative training mechanism, randomly selecting any other protein as off-target input, providing neural networks the opportunity observing all kinds of structures while focusing on the target. Then it employs structural displacement to create structural randomly shifted samples for simulating conformational changes brought by mobility of proteins. Consistent chemical embeddings are accessed by a regularizer comprising of E(3)-equivariant graph projector and molecular principal component analysis (MolPCA). With structural regularization implemented by a molecular contrastive learning architecture normalizing searching space by evenly distributing molecular features from both target and off-target proteins, AgainstOTE-R1 produces plausible compounds with discriminative structures even when exposed binding sites are similar. For enhancing the physical and biological plausibility, an advanced molecular generation model is tuned from the R1 model. In this stage, more district biochemically meaningful off-target receptors are selected by protein language models as training samples. Additionally, molecular dynamics simulation is exploited in structural displacement for physically modeling temporal trajectories of proteins instead of random sampling. We conduct extensive AI simulation and biochemical experiments to evaluate performance of this framework while designing target-to-sidelobe ratios (TSR) especially to assess off-target effects. Experimental results warn of off-target effects in AI-generated molecules and provide important insights for designing a drug against it.

FullText(HTML)

REFERENCES

[1]	Li Y., Zhang L., Wang Y., et al. (2022). Generative deep learning enables the discovery of a potent and selective RIPK1 inhibitor. Nat. Commun. 13:6891. DOI:10.1038/s41467-022-34692-w View in Article CrossRef Google Scholar
[2]	Segler M. H. S., Preuss M. and Waller M. P. (2018). Planning chemical syntheses with deep neural networks and symbolic AI. Nature 555:604−610. DOI:10.1038/nature25978 View in Article CrossRef Google Scholar
[3]	Shi H., Doench J. G. and Chi H. (2023). CRISPR screens for functional interrogation of immunity. Nat. Rev. Immunol. 23:363−380. DOI:10.1038/s41577-022-00802-4 View in Article CrossRef Google Scholar
[4]	Vamathevan J., Clark D., Czodrowski P., et al. (2019). Applications of machine learning in drug discovery and development. Nat. Rev. Drug Discov. 18:463−477. DOI:10.1038/s41573-019-0024-5 View in Article CrossRef Google Scholar
[5]	Moret M., Pachon A. I., Cotos L., et al. (2023). Leveraging molecular structure and bioactivity with chemical language models for de novo drug design. Nat. Commun. 14:114. DOI:10.1038/s41467-022-35692-6 View in Article CrossRef Google Scholar
[6]	Nguyen M. N., Sen N., Lin M., et al. (2019). Discovering putative protein targets of small molecules: A Study of the p53 Activator Nutlin. J. Chem. Inf. Model. 59:1529−1546. DOI:10.1021/acs.jcim.8b00762 View in Article CrossRef Google Scholar
[7]	Cheng T., Li Q., Zhou Z., et al. (2012). Structure-based virtual screening for drug discovery: A problem-centric review. AAPS J. 14:133−141. DOI:10.1208/s12248-012-9322-0 View in Article CrossRef Google Scholar
[8]	Scheiber J., Chen B., Milik M., et al. (2019). Gaining insight into off-target mediated effects of drug candidates with a comprehensive systems chemical biology analysis. J. Chem. Inf. Model. 49:308−317. DOI:10.1021/ci800344p View in Article CrossRef Google Scholar
[9]	Peng X., Luo S., Guan J., et al. (2022). Pocket2Mol: Efficient molecular sampling based on 3D protein pockets. Proceedings of the 39th International Conference on Machine Learning 162:17644-17655. https://proceedings.mlr.press/v162/peng22b.html. View in Article Google Scholar
[10]	Zhavoronkov A., Ivanenkov Y. A., Aliper A. et al. (2019). Deep learning enables rapid identification of potent DDR1 kinase inhibitors. Nat. Biotechnol. 37:1038−1040. DOI:10.1038/s41587-019-0224-x View in Article CrossRef Google Scholar
[11]	Masuda T., Ragoza M. and Koes D. R. (2020). Generating 3D molecular structures conditional on a receptor binding site with deep generative models. arXiv Preprint arXiv:2010.14442. DOI:10.48550/arXiv.2010.14442 View in Article Google Scholar
[12]	Francoeur P. G., Masuda T., Sunseri J., et al., (2020). Three-dimensional convolutional neural networks and a cross-docked data set for structure-based drug design. J. Chem. Inf. Model. 60:4200–4215. DOI:10.1021/acs.jcim.0c00411 View in Article Google Scholar
[13]	Kotsias P. C., Arús-Pous J., Chen H., et al. (2020). Direct steering of de novo molecular generation with descriptor conditional recurrent neural networks. Nat. Mach. Intell. 2:254−265. DOI:10.1038/s42256-020-0174-5 View in Article CrossRef Google Scholar
[14]	Segler M. H. S., Kogej T., Tyrchan C., et al. (2018). Generating focused molecule libraries for drug discovery with recurrent neural networks. ACS Cent. Sci. 4:20−131. DOI:10.1021/acscentsci.7b00512 View in Article CrossRef Google Scholar
[15]	Zhang J. and Chen H. (2022). De novo molecule design using molecular generative models constrained by ligand-protein interactions. J. Chem. Inf. Model. 62:3291−3306. DOI:10.1021/acs.jcim.2c00177 View in Article CrossRef Google Scholar
[16]	Lim J., Ryu S., Kim J., et al. (2018). Molecular generative model based on conditional variational autoencoder for de novo molecular design. J. Cheminform. 10:31. DOI:10.1186/s13321-018-0286-7 View in Article CrossRef Google Scholar
[17]	Jin W., Barzilay R. and Jaakkola T. S. (2018). Junction tree variational autoencoder for molecular graph generation. Proceedings of the 35th International Conference on Machine Learning 80:2323-2332. https://proceedings.mlr.press/v80/jin18a. View in Article Google Scholar
[18]	Fu T., Xiao C., Qian C., et al. (2021). Probabilistic and dynamic molecule-disease interaction modeling for drug discovery. Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining pp:404–414. DOI:10.1145/3447548.3467286 View in Article Google Scholar
[19]	Lam H. Y. I., Pincket R., Han H., et al. (2023). Application of variational graph encoders as an effective generalist algorithm in computer-aided drug design. Nat. Mach. Intell. 5:754−764. DOI:10.1038/s42256-023-00683-9 View in Article CrossRef Google Scholar
[20]	Cao N. D. and Kipf T. (2018). MolGAN, an implicit generative model for small molecular graphs. arXiv Preprint arXiv:1805.11973. DOI:10.48550/arXiv.1805.11973 View in Article Google Scholar
[21]	Kadurin A., Nikolenko S., Khrabrov K., et al. (2017). druGAN: An advanced generative adversarial autoencoder model for de novo generation of new molecules with desired molecular properties in silico. Mol. Pharm. 14:3098−3104. DOI:10.1021/acs.molpharmaceut.7b00346 View in Article CrossRef Google Scholar
[22]	Prykhodko O., Johansson S. V., Kotsias P. C., et al. (2019). A de novo molecular generation method using latent vector based generative adversarial network. J. Cheminfo. 11:74. DOI:10.1186/s13321-019-0397-9 View in Article CrossRef Google Scholar
[23]	Polykovskiy D., Zhebrak A., Vetrov D., et al. (2018). Entangled conditional adversarial autoencoder for de novo drug discovery. Mol Pharm. 15:4398−4405. DOI:10.1021/acs.molpharmaceut.8b00839 View in Article CrossRef Google Scholar
[24]	Wang Y., Wang J., Cao Z., et al. (2022). Molecular contrastive learning of representations via graph neural networks. Nat. Mach. Intell. 4:279−287. DOI:10.1038/s42256-022-00447-x View in Article CrossRef Google Scholar
[25]	Fang Y., Zhang Q., Zhang N., et al. (2023). Knowledge graph-enhanced molecular contrastive learning with functional prompt. Nat. Mac. Intell. 5:542−553. DOI:10.1038/s42256-023-00654-0 View in Article CrossRef Google Scholar
[26]	Luo S., Guan J., Ma J., et al. (2021). A 3D generative model for structure-based drug design. Advances in Neural Information Processing Systems 34:6229-6239. https://proceedings.neurips.cc/paper/2021/hash/314450613369e0ee72d0da7f6fee773c-Abstract.html?ref=https://githubhelp.com. View in Article Google Scholar
[27]	Zhang Z., Min Y., Zheng S., et al. (2023). Molecule generation for target protein binding with structural motifs. The Eleventh International Conference on Learning Representations. https://openreview.net/forum?id=Rq13idF0F73. View in Article Google Scholar
[28]	Tong X., Liu X., Tan X., et al. (2021). Generative models for de novo drug design. J. Med. Chem. 64:14011−14027. DOI:10.1021/acs.jmedchem.1c00927 View in Article CrossRef Google Scholar
[29]	Sanchez-Lengeling B. and Aspuru-Guzik A. (2018). Inverse molecular design using machine learning: Generative models for matter engineering. Science 361:360−365. DOI:10.1126/science.aat2663 View in Article CrossRef Google Scholar
[30]	Jiang Y., Zhang G., You J., et al. (2024). PocketFlow is a data-and-knowledge-driven structure-based molecular generative model. Nat. Mach. Intell. 6:326−337. DOI:10.1038/s42256-024-00808-8 View in Article CrossRef Google Scholar
[31]	Sadybekov A. A., Sadybekov A. V., Liu Y., et al. (2022). Synthon-based ligand discovery in virtual libraries of over 11 billion compounds. Nature 601:452−459. DOI:10.1038/s41586-021-04220-9 View in Article CrossRef Google Scholar
[32]	Alhossary A., Handoko S. D., Mu Y., et al. (2015). Fast, accurate, and reliable molecular docking with QuickVina 2. Bioinformatics 31:2214−6. DOI:10.1093/bioinformatics/btv082 View in Article CrossRef Google Scholar
[33]	Jensen K. T., Kao T. C., Tripodi M., et al. (2020). Manifold GPLVMs for discovering non-Euclidean latent structure in neural data. Advances in Neural Information Processing Systems 33. https://proceedings.neurips.cc/paper/2020/hash/fedc604da8b0f9af74b6cfc0fab2163c-Abstract.html. View in Article Google Scholar
[34]	Elnaggar A., Essam H., Salah-Eldin W., et al. (2023). Ankh: Optimized protein language model unlocks general-purpose modelling. arXiv Preprint arXiv:2301.06568. DOI:10.48550/arXiv.2301.06568 View in Article Google Scholar
[35]	Gowers R. J., Linke M., Barnoud J., et al. (2019). MDAnalysis: A python package for the rapid analysis of molecular dynamics simulations. Proceedings of the 15th Python in Science Conference pp:98–105. https://www.osti.gov/servlets/purl/1565806. View in Article Google Scholar
[36]	Eastman P., Swails J., Chodera J. D., et al. (2017). OpenMM 7: Rapid development of high performance algorithms for molecular dynamics. PLoS Comput. Biol. 13:e1005659. DOI:10.1371/journal.pcbi.1005659 View in Article CrossRef Google Scholar
[37]	Satorras V. G., Hoogeboom E. and Welling M. (2021). E(n) equivariant graph neural networks. Proceedings of the 38th International Conference on Machine Learning 139:9323-9332. https://proceedings.mlr.press/v139/satorras21a.html. View in Article Google Scholar
[38]	Wang Y., Shen Y., Chen S., et al. (2023). Learning harmonic molecular representations on riemannian manifold. The Eleventh International Conference on Learning Representations. https://openreview.net/forum?id=ySCL-NG_I3. View in Article Google Scholar
[39]	Stärk H., Ganea O., Pattanaik L., et al. (2022). EquiBind: Geometric deep learning for drug binding structure prediction. Proceedings of the 39th International Conference on Machine Learning 162:20503-20521. https://proceedings.mlr.press/v162/stark22b. View in Article Google Scholar
[40]	Zhang B. and Sennrich R. (2019). Root mean square layer normalization. Advances in Neural Information Processing Systems 32. https://proceedings.neurips.cc/paper/2019/hash/1e8a19426224ca89e83cef47f1e7f53b-Abstract.html. View in Article Google Scholar
[41]	Vaswani A., Shazeer N., Parmar N., et al. (2017). Attention is all you need. Advances in Neural Information Processing Systems 30. https://proceedings.neurips.cc/paper/2017/hash/3f5ee243547dee91fbd053c1c4a845aa-Abstract.html. View in Article Google Scholar
[42]	Hanin B. and Rolnick D. (2019). Deep ReLU networks have surprisingly few activation patterns. Advances in Neural Information Processing Systems 32. https://proceedings.neurips.cc/paper_files/paper/2019/hash/9766527f2b5d3e95d4a733fcfb77bd7e-Abstract.html. View in Article Google Scholar
[43]	Chen T., Kornblith S., Norouzi M., et al. (2022). A simple framework for contrastive learning of visual representations. Proceedings of the 37th International Conference on Machine Learning 119:1597-1607. https://proceedings.mlr.press/v119/chen20j.html. View in Article Google Scholar
[44]	Sohn K. (2016). Improved deep metric learning with multi-class n-pair loss objective. Advances in Neural Information Processing Systems 29. https://proceedings.neurips.cc/paper/2016/hash/6b180037abbebea991d8b1232f8a8ca9-Abstract.html. View in Article Google Scholar
[45]	Oord A. V. D., Li Y. and Vinyals O. (2018). Representation learning with contrastive predictive coding. arXiv Preprint arXiv:1807.03748. DOI:10.48550/arXiv.1807.03748 View in Article Google Scholar
[46]	Bolme D. S., Beveridge J. R., Draper B. A., et al. (2010). Visual object tracking using adaptive correlation filters. IEEE Computer Society Conference on Computer Vision and Pattern Recognition pp:2544-2550. DOI:10.1109/CVPR.2010.5539960 View in Article Google Scholar
[47]	Skwierawska A. and Pazik A. (2012). Inhibition of impurities formation in the synthesis of N-alkyltheobromines stimulated by microwave irradiation. Cationic and anionic response of membrane electrodes. J. Incl. Phenom. Macrocycl. Chem. 74:145−155. DOI:10.1007/s10847-011-0093-5 View in Article CrossRef Google Scholar
[48]	Stec J., Fomovska A., Afanador G. A., et al. (2013). Modification of triclosan scaffold in search of improved inhibitors for enoyl-acyl carrier protein (ACP) reductase in Toxoplasma gondii. ChemMedChem 8:1138−60. DOI:10.1002/cmdc.201300050 View in Article CrossRef Google Scholar
[49]	Weininger D. (1988). SMILES, a chemical language and information system. 1. introduction to methodology and encoding rules. J. Chem. Inf. Comput. Sci. 28:31–36. DOI:10.1021/ci00057a005 View in Article Google Scholar
[50]	O’Boyle N. and Dalke A. (2018). DeepSMILES: An adaptation of SMILES for use in machine-learning of chemical structures. ChemRxiv. chemrxiv:7097960.v1. DOI:10.26434/chemrxiv.7097960.v1 View in Article Google Scholar
[51]	Liu Y., Jin M., Pan S., et al. (2023). Graph self-supervised learning: A survey. IEEE Transactions on Knowledge and Data Engineering 35:5879−5900. DOI:10.1109/TKDE.2022.3172903 View in Article CrossRef Google Scholar
[52]	Li Y., Liu L., Shen C., et al. (2017). Mining mid-level visual patterns with deep CNN activations. International Journal of Computer Vision 121:344−364. DOI:10.1007/s11263-016-0945-y View in Article CrossRef Google Scholar
[53]	Stretcu O. and Leordeanu M. (2015). Multiple frames matching for object discovery in video. Proceedings of the British Machine Vision Conference pp:186.1-186.12. DOI:10.5244/C.29.186 View in Article Google Scholar
[54]	Rives A., Goyal S., Meier J., et al. (2020). Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences. Proceedings of the National Academy of Sciences of the United States of America 118:e2016239118. DOI:10.1073/pnas.2016239118 View in Article CrossRef Google Scholar
[55]	Devlin J., Chang M. W. Lee, et al. (2018). BERT: Pre-training of deep bidirectional transformers for language understanding. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies 1:4171–4186. DOI:10.18653/v1/N19-1423 View in Article Google Scholar
[56]	Liu Y., Ott M., Goyal N., et al. (2019). RoBERTa: A robustly optimized BERT pretraining approach. arXiv Preprint arXiv:1907.11692. DOI:10.48550/arXiv.1907.11692 View in Article Google Scholar
[57]	Hsu C., Verkuil R., Liu J., et al. (2022). Learning inverse folding from millions of predicted structures. Proceedings of the 39th International Conference on Machine Learning 162:8946-8970. https://proceedings.mlr.press/v162/hsu22a.html. View in Article Google Scholar
[58]	Lin Z., Akin H., Rao R., et al. (2022). Language models of protein sequences at the scale of evolution enable accurate structure prediction. bioRxiv Preprint DOI:10.1101/2022.07.20.500902 View in Article Google Scholar
[59]	Thomson E. C., Rosen L. E., Shepherd J. G., et al. (2021). Circulating SARS-CoV-2 spike N439K variants maintain fitness while evading antibody-mediated immunity. Cell 184:1171−1187. DOI:10.1016/j.cell.2021.01.037 View in Article CrossRef Google Scholar
[60]	Hou Y. J., Chiba S., Halfmann P., et al. (2020). SARS-CoV-2 D614G variant exhibits efficient replication ex vivo and transmission in vivo. Science 370:1464−1468. DOI:10.1126/science.abe8499 View in Article CrossRef Google Scholar
[61]	Weissman D., Alameh M. G., Silva T., et al. (2021). D614G Spike Mutation Increases SARS CoV-2 Susceptibility to Neutralization. Cell Host Microbe. 29:23−31. DOI:10.1016/j.chom.2020.11.012 View in Article CrossRef Google Scholar
[62]	Yurkovetskiy L., Wang X., Pascal K. E., et al. (2020). Structural and functional analysis of the D614G SARS-CoV-2 spike protein variant. Cell 183:739−751. DOI:10.1016/j.cell.2020.09.032 View in Article CrossRef Google Scholar
[63]	Ragoza M., Masuda T. and Koes D. R. (2022). Generating 3d molecules conditional on receptor binding sites with deep generative models. Chem. Sci. 13:2701−2713. DOI:10.1039/d1sc05976a View in Article CrossRef Google Scholar
[64]	Liu M., Luo Y., Uchino K., et al. (2022). Generating 3d molecules for target protein binding. Proceedings of the 39th International Conference on Machine Learning 162:13912-13924. https://proceedings.mlr.press/v162/liu22m.html. View in Article Google Scholar
[65]	Bickerton G. R., Paolini G. V., Besnard J., et al. (2012). Quantifying the chemical beauty of drugs. Nat. Chem. 4:90−98. DOI:10.1038/nchem.1243 View in Article CrossRef Google Scholar
[66]	Ertl P. and Schuffenhauer A. (2009). Estimation of synthetic accessibility score of drug-like molecules based on molecular complexity and fragment contributions. J. Cheminform. 1:8. DOI:10.1186/1758-2946-1-8 View in Article CrossRef Google Scholar
[67]	Wildman S. A. and Crippen G. M. (1999). Prediction of physicochemical parameters by atomic contributions. J. Chem. Inf. Comput. Sci. 39:868−873. DOI:10.1021/ci990307l View in Article CrossRef Google Scholar
[68]	Lipinski C. A., Lombardo F., Dominy B. W., et al. (2001). Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv. Drug Del. Rev. 46:3−26. DOI:10.1016/s0169-409x(00)00129-0 View in Article CrossRef Google Scholar
[69]	Jensen R. K. and Nassas G. (1988). Growth of segment principal moments of inertia between four and twenty years. Med. Sci. Sports. Exerc. 20:594−604. DOI:10.1249/00005768-198812000-00013 View in Article CrossRef Google Scholar
[70]	Becht E., McInnes L., Healy J., et al. (2018). Dimensionality reduction for visualizing single-cell data using UMAP. Nat. Biotechnol. 37:38−44. DOI:10.1038/nbt.4314 View in Article CrossRef Google Scholar
[71]	Wang, R., Fang, X., Lu, Y., et al. (2004). The PDBbind database: Collection of binding affinities for protein-ligand complexes with known three-dimensional structures. J. Med. Chem. 47:2977−2980. DOI:10.1021/jm030580l View in Article CrossRef Google Scholar
[72]	Wang Y., Dong L., Li J., et al. (2017). Pentoxifylline induces apoptosis of HepG2 cells by reducing reactive oxygen species production and activating the MAPK signaling. Life Sci. 183:60−68. DOI:10.1016/j.lfs.2017.05.029 View in Article CrossRef Google Scholar
[73]	Lu J., Yang Y., Guo G., et al. (2017). IKBKE regulates cell proliferation and epithelial-mesenchymal transition of human malignant glioma via the Hippo pathway. Oncotarget 8:49502−49514. DOI:10.18632/oncotarget.17738 View in Article CrossRef Google Scholar
[74]	Hu L. and Zhang Q. (2024). Mechanism of TBK1 activation in cancer cells. Cell Insight 3:100197. DOI:10.1016/j.cellin.2024.100197 View in Article CrossRef Google Scholar
[75]	Ait-Touchente Z., Zine N., Jaffrezic-Renault N., et al. (2023). A Exploring the versatility of microemulsions in cutaneous drug delivery: opportunities and challenges. Nanomaterials 13:1688. DOI:10.3390/nano13101688 View in Article CrossRef Google Scholar
[76]	Zhuo S., Zhang F., Yu J., et al. (2020). pH-sensitive biomaterials for drug delivery. Molecules 25:5649. DOI:10.3390/molecules25235649 View in Article CrossRef Google Scholar
[77]	Gomte S. S., Agnihotri T. G., Khopade S., et al. (2024). Exploring the potential of pH-sensitive polymers in targeted drug delivery. J. Biomater. Sci. Polym. Ed. 35:228−268. DOI:10.1080/09205063.2023.2279792 View in Article CrossRef Google Scholar
[78]	Muttenthaler M., King G. F., Adams D. J., et al. (2021). Trends in peptide drug discovery. Nat. Rev. Drug Discov. 20:309−325. DOI:10.1038/s41573-020-00135-8 View in Article CrossRef Google Scholar
[79]	Huang T., Xu H., Wang H., et al., (2023). Artificial intelligence for medicine: Progress, challenges, and perspectives. Innov. Med. 1:100030. DOI:10.59717/j.xinn-med.2023.100030 View in Article Google Scholar
[80]	Dai J., Xu H., Chen T., et al. (2025). Artificial intelligence for medicine 2025: Navigating the endless frontier. Innov. Med. 3:100120. DOI:10.59717/j.xinn-med.2025.100120 View in Article CrossRef Google Scholar

Cited by

ABOUT THIS ARTICLE

Cite this article:

Xu Y., Fang W., Wang S., et al. (2025). AgainstOTE: A generative pretrain-tune framework for de novo molecular generation against off-target effects. The Innovation Medicine 3:100158. https://doi.org/10.59717/j.xinn-med.2025.100158

Xu Y., Fang W., Wang S., et al. (2025). AgainstOTE: A generative pretrain-tune framework for de novo molecular generation against off-target effects. The Innovation Medicine 3:100158. https://doi.org/10.59717/j.xinn-med.2025.100158

Welcome!

To request copyright permission to republish or share portions of our works, please visit Copyright Clearance Center's (CCC) Marketplace website at marketplace.copyright.com.

Standard PDF

Extended PDF

Figures(8)

Article Metrics

Article views(2716) PDF downloads(2070)

AgainstOTE: A generative pretrain-tune framework for de novo molecular generation against off-target effects

GRAPHICAL ABSTRACT

PUBLIC SUMMARY

ABSTRACT

REFERENCES

ABOUT THIS ARTICLE

Cite this article:

Welcome!

Share

Article Metrics

Relative Articles

Cited by

Catalog

{{newsColumn.name}}

Official Account

{{newsColumn.name}}

Stay connected

{{newsColumn.name}}

AgainstOTE: A generative pretrain-tune framework for de novo molecular generation against off-target effects

GRAPHICAL ABSTRACT

PUBLIC SUMMARY

ABSTRACT

REFERENCES

ABOUT THIS ARTICLE

Cite this article:

Welcome!

Share

Article Metrics

Relative Articles

Cited by

Catalog

Export File

Citation

Format

Content

{{newsColumn.name}}

Official Account

{{newsColumn.name}}

Stay connected

{{newsColumn.name}}