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为针对代谢疾病的特定HDAC11抑制剂设计新的支架,利用深度学习,虚拟查和分子动力学模拟来利用代谢疾病

Jiali Li¹, XiaoDie Chen¹, Rong Liu¹

¹School of Pharmacy and Bioengineering, Chongqing University of Technology, Chongqing 400054, China; Key Laboratory of Screening and activity evaluation of targeted drugs, Chongqing 400054, China.

International journal of biological macromolecules

|February 10, 2024

概括

研究人员利用深度学习来发现新型的基因脱乙酶11 (HDAC11) 抑制剂用于代谢疾病. 通过分子模拟确定并验证了十种有前途的候选药物,提供了新的治疗潜力.

科学领域:

药用化学医学化学
计算生物学计算生物学
药理学药理学是指药理学的学科.

背景情况:

代谢性疾病是一个日益严重的全球健康问题.
基斯脱乙酶11 (HDAC11) 参与了代谢性疾病的发病,是可行的药物标.
深度学习为高效和新型药物设计提供了先进的能力.

研究的目的:

设计和识别用于治疗代谢障碍的HDAC11新型特异性抑制剂.
为了利用深度学习来生成各种各样的潜在HDAC11抑制剂库.
以计算方式选和验证候选分子的治疗潜力.

主要方法:

在现有的HDAC11抑制剂数据上训练深度学习模型,以生成一个新的化合物库 (23,122个分子).
使用ADMET预测,Lipinski & Veber规则,机器分类和分子对接来选化合物.
分子动力学模拟 (RMSD,RMSF,MM/GBSA,自由能景观,PCA) 评估了候选抑制剂的稳定性和特性.

主要成果:

使用深度学习生成了一个23122个分子的库.
经过严格的计算选,十种化合物被确定为有前途的HDAC11抑制剂.
分子动力学模拟证实了十种选定的化合物的稳定性和有利性质.

结论:

十种新型化合物 (Cpd_17556,Cpd_2184,Cpd_8907,Cpd_7771,Cpd_14959,Cpd_7108,Cpd_12383,Cpd_13153,Cpd_14500,Cpd_21811) 显示出可能成为HDAC11抑制剂的可能性.
这些化合物代表了对代谢障碍有前途的候选药物.
进一步的体外试验,体内试验和临床试验是有必要的,以验证它们的治疗疗效.

关键词:

深度学习是一种深度学习.这是一种HDAC11抑制剂.代谢性疾病是一种代谢性疾病.分子动力学模拟的模拟.虚拟选是一个虚拟的选.

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