{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,5]],"date-time":"2026-06-05T01:02:16Z","timestamp":1780621336469,"version":"3.54.1"},"publisher-location":"Singapore","reference-count":28,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819500260","type":"print"},{"value":"9789819500277","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025]]},"DOI":"10.1007\/978-981-95-0027-7_36","type":"book-chapter","created":{"date-parts":[[2025,7,16]],"date-time":"2025-07-16T14:15:15Z","timestamp":1752675315000},"page":"419-430","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["DrugGAN-MSM: A Generative Adversarial Approach to Molecular Design Integrating Masked Modeling and Multi-objective Optimization"],"prefix":"10.1007","author":[{"given":"Zihang","family":"Xie","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaolong","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoli","family":"Lin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jing","family":"Hu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,7,17]]},"reference":[{"key":"36_CR1","doi-asserted-by":"crossref","unstructured":"Bian, Y.: From computer-aided drug design to artificial intelligence-driven drug design. Chin. J. Nat., 1\u20138 (2025)","DOI":"10.1016\/B978-0-443-45105-8.00012-3"},{"issue":"1","key":"36_CR2","doi-asserted-by":"publisher","first-page":"49","DOI":"10.3390\/pharmaceutics15010049","volume":"15","author":"Y Chang","year":"2022","unstructured":"Chang, Y., Hawkins, B.A., Du, J.J., et al.: A guide to in silico drug design. Pharmaceutics 15(1), 49 (2022)","journal-title":"Pharmaceutics"},{"key":"36_CR3","doi-asserted-by":"crossref","unstructured":"El Rhabori, S., El Aissouq, A., Daoui, O., et al.: Design of new molecules against cervical cancer using DFT, theoretical spectroscopy, 2D\/3D-QSAR, molecular docking, pharmacophore and ADMET investigations. Heliyon 10(3) (2024)","DOI":"10.1016\/j.heliyon.2024.e24551"},{"key":"36_CR4","doi-asserted-by":"crossref","unstructured":"Liu, H., Lin, X., Hu, J., et al.: A multi-target drug design method based on target protein sequence and feature similarity. In: 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 848\u2013851. IEEE (2024)","DOI":"10.1109\/BIBM62325.2024.10822055"},{"issue":"2","key":"36_CR5","doi-asserted-by":"publisher","first-page":"216","DOI":"10.1038\/s41587-024-02127-0","volume":"42","author":"P Notin","year":"2024","unstructured":"Notin, P., Rollins, N., Gal, Y., et al.: Machine learning for functional protein design. Nat. Biotechnol. 42(2), 216\u2013228 (2024)","journal-title":"Nat. Biotechnol."},{"key":"36_CR6","doi-asserted-by":"publisher","DOI":"10.1016\/j.jobe.2023.107267","volume":"76","author":"S Barbhuiya","year":"2023","unstructured":"Barbhuiya, S., Das, B.B.: Molecular dynamics simulation in concrete research: a systematic review of techniques, models and future directions. J. Build. Eng. 76, 107267 (2023)","journal-title":"J. Build. Eng."},{"key":"36_CR7","doi-asserted-by":"crossref","unstructured":"Gangwal, A., Lavecchia, A.: Unlocking the potential of generative AI in drug discovery. Drug Discov. Today, 103992 (2024)","DOI":"10.1016\/j.drudis.2024.103992"},{"key":"36_CR8","doi-asserted-by":"crossref","unstructured":"An, Q., Yu, L.: A heterogeneous network embedding framework for predicting similarity-based drug-target interactions. Brief. Bioinf. 22(6), bbab275 (2021)","DOI":"10.1093\/bib\/bbab275"},{"issue":"1","key":"36_CR9","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1186\/s13321-025-00965-x","volume":"17","author":"M Oestreich","year":"2025","unstructured":"Oestreich, M., Merdivan, E., Lee, M., et al.: DrugDiff: small molecule diffusion model with flexible guidance towards molecular properties. J. Cheminf. 17(1), 23 (2025)","journal-title":"J. Cheminf."},{"key":"36_CR10","doi-asserted-by":"crossref","unstructured":"Liu, H., Zhang, X., Lin, X., et al.: An efficient drug design method based on drug-target affinity. In: International Conference on Intelligent Computing, pp. 764--775. Springer, Singapore (2023)","DOI":"10.1007\/978-981-99-4749-2_65"},{"issue":"11","key":"36_CR11","doi-asserted-by":"publisher","first-page":"968","DOI":"10.1016\/j.cels.2023.10.002","volume":"14","author":"E Nijkamp","year":"2023","unstructured":"Nijkamp, E., Ruffolo, J.A., Weinstein, E.N., et al.: Progen2: exploring the boundaries of protein language models. Cell Syst. 14(11), 968\u2013978 (2023)","journal-title":"Cell Syst."},{"key":"36_CR12","doi-asserted-by":"crossref","unstructured":"Mardikoraem, M., Wang, Z., Pascual, N., et al.: Generative models for protein sequence modeling: recent advances and future directions. Brief. Bioinf. 24(6), bbad358 (2023)","DOI":"10.1093\/bib\/bbad358"},{"issue":"14","key":"36_CR13","doi-asserted-by":"publisher","first-page":"4277","DOI":"10.1021\/acs.jcim.3c00273","volume":"63","author":"X Shen","year":"2023","unstructured":"Shen, X., Zhang, S., Long, J., et al.: A highly sensitive model based on graph neural networks for enzyme key catalytic residue prediction. J. Chem. Inf. Model. 63(14), 4277\u20134290 (2023)","journal-title":"J. Chem. Inf. Model."},{"key":"36_CR14","doi-asserted-by":"crossref","unstructured":"Wang, M., Zhang, X., Liu, H., et al.: Drug molecule generation method based on fusion of protein sequence features. In: International Conference on Intelligent Computing, pp. 119--130. Springer, Singapore (2024)","DOI":"10.1007\/978-981-97-5692-6_11"},{"issue":"4","key":"36_CR15","doi-asserted-by":"publisher","first-page":"386","DOI":"10.1038\/s42256-023-00636-2","volume":"5","author":"M Mokaya","year":"2023","unstructured":"Mokaya, M., Imrie, F., van Hoorn, W.P., et al.: Testing the limits of SMILES-based de novo molecular generation with curriculum and deep reinforcement learning. Nat. Mach. Intell. 5(4), 386\u2013394 (2023)","journal-title":"Nat. Mach. Intell."},{"key":"36_CR16","doi-asserted-by":"publisher","first-page":"69386","DOI":"10.52202\/079017-2216","volume":"37","author":"X Cheng","year":"2024","unstructured":"Cheng, X., Chen, B., Li, P., et al.: Training compute-optimal protein language models. Adv. Neural. Inf. Process. Syst. 37, 69386\u201369418 (2024)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"issue":"1","key":"36_CR17","doi-asserted-by":"publisher","first-page":"38","DOI":"10.1186\/s13321-023-00702-2","volume":"15","author":"Y Chen","year":"2023","unstructured":"Chen, Y., Wang, Z., Wang, L., et al.: Deep generative model for drug design from protein target sequence. J. Cheminf. 15(1), 38 (2023)","journal-title":"J. Cheminf."},{"key":"36_CR18","doi-asserted-by":"publisher","first-page":"58359","DOI":"10.1109\/ACCESS.2023.3282248","volume":"11","author":"J Fan","year":"2023","unstructured":"Fan, J., Hong, S.K., Lee, Y.: Validity improvement in MolGAN-based molecular generation. IEEE Access 11, 58359\u201358366 (2023)","journal-title":"IEEE Access"},{"issue":"1","key":"36_CR19","doi-asserted-by":"publisher","first-page":"7953","DOI":"10.1038\/s41467-024-51895-5","volume":"15","author":"Y Park","year":"2024","unstructured":"Park, Y., Metzger, B.P.H., Thornton, J.W.: The simplicity of protein sequence-function relationships. Nat. Commun. 15(1), 7953 (2024)","journal-title":"Nat. Commun."},{"issue":"1","key":"36_CR20","doi-asserted-by":"publisher","first-page":"87","DOI":"10.1109\/TPAMI.2022.3152247","volume":"45","author":"K Han","year":"2022","unstructured":"Han, K., Wang, Y., Chen, H., et al.: A survey on vision transformer. IEEE Trans. Pattern Anal. Mach. Intell. 45(1), 87\u2013110 (2022)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"36_CR21","doi-asserted-by":"crossref","unstructured":"Fang, Y., Pan, X., Shen, H.-B.: De Novo drug design by iterative multiobjective deep reinforcement learning with graph-based molecular quality assessment. Bioinformatics 39(4), btad157 (2023)","DOI":"10.1093\/bioinformatics\/btad157"},{"issue":"2","key":"36_CR22","doi-asserted-by":"publisher","first-page":"2046","DOI":"10.1021\/acsomega.2c05607","volume":"8","author":"C Isert","year":"2023","unstructured":"Isert, C., Kromann, J.C., Stiefl, N., et al.: Machine learning for fast, quantum mechanics-based approximation of drug lipophilicity. ACS Omega 8(2), 2046\u20132056 (2023)","journal-title":"ACS Omega"},{"key":"36_CR23","unstructured":"Maleki, M., Zahiri, S.: OWPCP: a deep learning model to predict octanol\u2013water partition coefficient. arXiv preprint arXiv:2410.18118 (2024)"},{"key":"36_CR24","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13321-019-0397-9","volume":"11","author":"O Prykhodko","year":"2019","unstructured":"Prykhodko, O., Johansson, S.V., Kotsias, P.C., et al.: A de novo molecular generation method using latent vector based generative adversarial network. J. Cheminf. 11, 1\u201313 (2019)","journal-title":"J. Cheminf."},{"issue":"D1","key":"36_CR25","doi-asserted-by":"publisher","first-page":"D1180","DOI":"10.1093\/nar\/gkad1004","volume":"52","author":"B Zdrazil","year":"2024","unstructured":"Zdrazil, B., Felix, E., Hunter, F., et al.: The ChEMBL database in 2023: a drug discovery platform spanning multiple bioactivity data types and time periods. Nucleic Acids Res. 52(D1), D1180\u2013D1192 (2024)","journal-title":"Nucleic Acids Res."},{"issue":"1","key":"36_CR26","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1186\/s13321-022-00618-3","volume":"14","author":"BK Isamura","year":"2022","unstructured":"Isamura, B.K., Lobb, K.A.: AMADAR: a python-based package for large-scale prediction of diels-alder transition state geometries and IRC path analysis. J. Cheminf. 14(1), 39 (2022)","journal-title":"J. Cheminf."},{"issue":"1","key":"36_CR27","doi-asserted-by":"publisher","first-page":"13398","DOI":"10.1038\/s41598-023-40160-2","volume":"13","author":"PC Agu","year":"2023","unstructured":"Agu, P.C., Afiukwa, C.A., Orji, O.U., et al.: Molecular docking as a tool for the discovery of molecular targets of nutraceuticals in diseases management. Sci. Rep. 13(1), 13398 (2023)","journal-title":"Sci. Rep."},{"key":"36_CR28","doi-asserted-by":"crossref","unstructured":"Eberhardt, J., Santos-Martins, D., Tillack, A. F., et al.: AutoDock Vina 1.2.0: new docking methods, expanded force field, and python bindings. J. Chem. Inf. Model. 61(8), 3891\u20133898 (2021)","DOI":"10.1021\/acs.jcim.1c00203"}],"container-title":["Lecture Notes in Computer Science","Advanced Intelligent Computing Technology and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-95-0027-7_36","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,5]],"date-time":"2026-06-05T00:21:53Z","timestamp":1780618913000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-95-0027-7_36"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9789819500260","9789819500277"],"references-count":28,"URL":"https:\/\/doi.org\/10.1007\/978-981-95-0027-7_36","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"17 July 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICIC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Intelligent Computing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Ningbo","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 July 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 July 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icic2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.ic-icc.cn\/icg\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}