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使用Active-IT系统进行生物活动的大规模预测

V L Almeida¹, O D H Dos Santos², J C D Lopes³

¹Chemoinformatics Group - NEQUIM, Departamento de Quimica, Instituto de Ciências Exatas, Universidade Federal de Minas Gerais (UFMG), Belo Horizonte, Brazil; Servico de Fitoquimica e Prospeccao Farmaceutica, Fundacao Ezequiel Dias (FUNED), Belo Horizonte, Brazil.

Biomeditsinskaia khimiia

|December 24, 2024

概括

激活IT系统是一个新的基于激素的虚拟查 (LBVS) 工具,通过预测分子活性来加速药物开发. 这种in silico方法有效地选化合物,减少与传统测试相关的时间和成本.

科学领域:

计算化学和化学信息学
药物的发现和开发.
药理学和毒理学药理学和毒理学

背景情况:

传统的药物检测是耗时且昂贵的.
在 silico 方法提供更快,更具成本效益的替代方案.
基于激素的虚拟选 (LBVS) 是一个关键的计算策略.

研究的目的:

引入内部的Active-IT系统,一个基于基的虚拟选 (LBVS) 工具.
预测小型有机分子的生物和药理活动.
评估Active-IT在药物发现管道中的有效性.

主要方法:

开发了Active-IT系统的四个模块:分子描述器生成 (3D-Pharma),机器学习建模 (ExCVBA),生物活性模型数据库和预测模块.
使用了支持矢量机 (SVM) 和天真贝叶斯机器学习方法,使用了PubChem BioAssay数据库中的数据.
采用递归分层分区用于模型构建和Y随机化用于验证,丢弃性能较低的模型.

主要成果:

成功建模了超过3500个生物试验,产生了众多的SVM,天真贝叶斯和随机模型.
使用Active-IT系统评估了来自阿雅华斯卡茶的三种生物活性化合物.
外部验证显示出显著的预测能力,33种化合物中有16种 (48.5%) 显示出值得注意的结果 (p值).

结论:

该Active-IT系统提供了一个强大的和高效的溶液,用于预测制药研究中的分子活性.
该系统的预测能力得到了验证,显示了加速药物发现的前景.
像Active-IT这样的LBVS工具对于优化潜在药物候选者的识别至关重要.

关键词:

3D分子结构是3D分子结构.生物活性的预测预测.基于联结体的虚拟选.机器学习建模机器学习药物学家指纹的指纹递归分层的随机分区.

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