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).) |
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| 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 |
Find this article in full text from Springer Verlag. 
Full Text Finder | تدمد: | 2041-1723 |
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| DOI: | 10.1038/s41467-024-46569-1 |