دورية أكاديمية
A dual diffusion model enables 3D molecule generation and lead optimization based on target pockets.
العنوان: | A dual diffusion model enables 3D molecule generation and lead optimization based on target pockets. |
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المؤلفون: | Huang L; City University of Hong Kong, Hong Kong, SAR, China.; Tencent AI Lab, Shenzhen, China., Xu T; Tencent AI Lab, Shenzhen, China., Yu Y; Tencent AI Lab, Shenzhen, China., Zhao P; Tencent AI Lab, Shenzhen, China., Chen X; Harvard Medical School, Boston, USA., Han J; Regor Therapeutics Group, Shanghai, China., Xie Z; Regor Therapeutics Group, Shanghai, China., Li H; Regor Therapeutics Group, Shanghai, China. hailong.li@qlregor.com., Zhong W; Regor Therapeutics Group, Shanghai, China., Wong KC; City University of Hong Kong, Hong Kong, SAR, China. kc.w@cityu.edu.hk., Zhang H; Tencent AI Lab, Shenzhen, China. htzhang.work@gmail.com. |
المصدر: | Nature communications [Nat Commun] 2024 Mar 26; Vol. 15 (1), pp. 2657. Date of Electronic Publication: 2024 Mar 26. |
نوع المنشور: | Journal Article |
اللغة: | English |
بيانات الدورية: | Publisher: Nature Pub. Group Country of Publication: England NLM ID: 101528555 Publication Model: Electronic Cited Medium: Internet ISSN: 2041-1723 (Electronic) Linking ISSN: 20411723 NLM ISO Abbreviation: Nat Commun Subsets: MEDLINE |
أسماء مطبوعة: | Original Publication: [London] : Nature Pub. Group |
مواضيع طبية MeSH: | Anti-HIV Agents* , Methacrylates*, Benchmarking ; Benzoates ; Chemistry, Physical ; Drug Design |
مستخلص: | Structure-based generative chemistry is essential in computer-aided drug discovery by exploring a vast chemical space to design ligands with high binding affinity for targets. However, traditional in silico methods are limited by computational inefficiency, while machine learning approaches face bottlenecks due to auto-regressive sampling. To address these concerns, we have developed a conditional deep generative model, PMDM, for 3D molecule generation fitting specified targets. PMDM consists of a conditional equivariant diffusion model with both local and global molecular dynamics, enabling PMDM to consider the conditioned protein information to generate molecules efficiently. The comprehensive experiments indicate that PMDM outperforms baseline models across multiple evaluation metrics. To evaluate the applications of PMDM under real drug design scenarios, we conduct lead compound optimization for SARS-CoV-2 main protease (M pro ) and Cyclin-dependent Kinase 2 (CDK2), respectively. The selected lead optimization molecules are synthesized and evaluated for their in-vitro activities against CDK2, displaying improved CDK2 activity. (© 2024. The Author(s).) |
References: | J Chem Inf Comput Sci. 2003 May-Jun;43(3):987-1003. (PMID: 12767158) Chem Biol. 2003 Sep;10(9):787-97. (PMID: 14522049) Mol Pharm. 2019 Oct 7;16(10):4282-4291. (PMID: 31437001) Curr Opin Chem Biol. 2011 Aug;15(4):547-52. (PMID: 21684797) Chem Sci. 2021 Sep 9;12(41):13664-13675. (PMID: 34760151) Adv Drug Deliv Rev. 2001 Mar 1;46(1-3):3-26. (PMID: 11259830) Future Med Chem. 2011 Oct;3(14):1765-86. (PMID: 22004084) J Chem Inf Model. 2020 Mar 23;60(3):1175-1183. (PMID: 31904964) Curr Top Med Chem. 2014;14(16):1923-38. (PMID: 25262799) Chem Sci. 2022 Feb 7;13(9):2701-2713. (PMID: 35356675) J Mol Graph Model. 2008 Sep;27(2):161-9. (PMID: 18485770) J Chem Inf Model. 2020 Sep 28;60(9):4200-4215. (PMID: 32865404) J Cheminform. 2011 Oct 07;3:33. (PMID: 21982300) Chem Rev. 2019 Jun 12;119(11):6595-6612. (PMID: 31059236) Nat Rev Drug Discov. 2004 Nov;3(11):935-49. (PMID: 15520816) Nat Rev Drug Discov. 2007 Mar;6(3):211-9. (PMID: 17290284) J Chem Inf Model. 2012 Oct 22;52(10):2516-25. (PMID: 23009689) Nat Biotechnol. 2017 Nov;35(11):1026-1028. (PMID: 29035372) Trends Pharmacol Sci. 2009 Mar;30(3):138-47. (PMID: 19187977) ACS Cent Sci. 2021 Mar 24;7(3):467-475. (PMID: 33786375) J Chem Inf Model. 2021 Jul 26;61(7):3240-3254. (PMID: 34197105) Nat Commun. 2022 Feb 21;13(1):973. (PMID: 35190542) J Med Chem. 2019 May 9;62(9):4233-4251. (PMID: 30543440) J Cheminform. 2012 Nov 06;4(1):27. (PMID: 23131020) ACS Med Chem Lett. 2023 Feb 08;14(3):297-304. (PMID: 36923916) J Comb Chem. 1999 Jan;1(1):55-68. (PMID: 10746014) Drug Discov Today. 2007 Feb;12(3-4):149-55. (PMID: 17275735) Front Pharmacol. 2020 Dec 18;11:565644. (PMID: 33390943) J Chem Theory Comput. 2017 Jan 10;13(1):42-54. (PMID: 27933808) ACS Cent Sci. 2018 Feb 28;4(2):268-276. (PMID: 29532027) Protein Sci. 2023 Nov;32(11):e4792. (PMID: 37774136) ACS Med Chem Lett. 2022 Oct 06;13(11):1797-1804. (PMID: 36385925) |
معلومات مُعتمدة: | 2021SIRG036 City University of Hong Kong (CityU); CityU 9667265 City University of Hong Kong (CityU); CityU 11203221 KC.W. City University of Hong Kong (CityU); 32170654 KC.W. National Natural Science Foundation of China (National Science Foundation of China); ITB/FBL/9037/22/S KC.W. Innovation and Technology Commission (ITF) |
المشرفين على المادة: | 0 (pyromellitic acid dianhydride-2-hydroxyethyl methacrylate adduct) 0 (Anti-HIV Agents) 0 (Benzoates) 0 (Methacrylates) |
تواريخ الأحداث: | Date Created: 20240327 Date Completed: 20240328 Latest Revision: 20240329 |
رمز التحديث: | 20240329 |
مُعرف محوري في PubMed: | PMC10965937 |
DOI: | 10.1038/s41467-024-46569-1 |
PMID: | 38531837 |
قاعدة البيانات: | MEDLINE |
تدمد: | 2041-1723 |
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DOI: | 10.1038/s41467-024-46569-1 |