AmIActive (AIA):一个大规模的基于QSAR的目标捕鱼和多药学预测网络工具
Luis Felipe de Morais Melo1, Luciano Pereira de Oliveira Filho2, Uilames de Assis Ferreira2
1Cheminformatics Laboratory, Department of Chemistry, Center for Exact and Natural Sciences (CCEN), Federal University of Paraiba (UFPB), Campus I Lot. Cidade Universitaria, Castelo Branco, João Pessoa, Paraiba, Brazil; Postgraduate Program in Natural and Synthetic Bioactive Products (PgPNSB), Federal University of Paraiba (UFPB), Campus I Lot. Cidade Universitaria, Castelo Branco, João Pessoa, Paraiba, Brazil.
AmIActive (AIA) 是一种新的网络工具,用于预测生物活性,并使用定量结构-活性关系 (QSAR) 模型识别药物标. 它提供了3,239个模型,涵盖了各种生物实体,有助于药物发现研究.
科学领域:
- 计算化学是一种计算化学.
- 化学信息学 化学信息学
- 药理学 药理学是指药理学的学科.
背景情况:
- 定量结构-活性关系 (QSAR) 模型对于预测药物疗效至关重要.
- 获得全面和验证的QSAR模型有助于药物发现和开发.
- 现有的工具可能缺乏广泛的覆盖范围或用户友好的界面来预测生物活动.
研究的目的:
- 介绍AmIActive (AIA),一个新的基于Web的QSAR工具.
- 为生物活动预测和目标捕捞提供一个平台.
- 支持研究人员识别潜在的候选药物及其目标.
主要方法:
- 使用随机森林分类器开发3239个QSAR模型.
- 使用圆形指纹描述器和ChEMBL数据库活动数据的培训模型.
- 为模型可靠性实施明确定义的适用性领域.
主要成果:
- 该AIA系统涵盖了2,277个不同的目标,包括蛋白质,复合体,家族,细胞系,生物和组织.
- 模型在广泛的生物活动数据上进行训练,确保广泛的适用性.
- 该工具提供预测信息,目标细节,验证指标和活动值.
结论:
- AmIActive (AIA) 为基于QSAR的生物活动预测提供了一个用户友好的,免费的资源.
- 全面的模型集和直观的界面简化了目标捕鱼和药物发现工作.
- 国际化学协会促进了对潜在治疗剂的化学空间的有效探索.
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