生物信息学和化学信息学应用的进步
Mohamed A Raslan1, Sara A Raslan1, Eslam M Shehata1
1Drug Research Centre, Cairo P.O. Box 11799, Egypt.
Pharmaceuticals (Basel, Switzerland)
|July 29, 2023
概括
化学信息学和机器学习通过分析大量的化学数据来加速药物发现. 本次审查强调了虚拟图书馆和生物信息学,用于识别有力的候选药物和改善疾病诊断.
科学领域:
- 计算化学和生物信息学
- 药物的发现和开发.
- 分子建模和数据科学
背景情况:
- 化学信息学将物理化学与计算方法 ("in silico技术") 结合起来,以应对化学挑战.
- 机器学习在药物设计中至关重要,因为它可以从大型复合数据库中提取见解.
- 生物信息学的应用包括疾病分类,诊断和识别耐药生物.
研究的目的:
- 审查化学信息学,并提出虚拟化学图书馆,以加强虚拟选.
- 探索生物信息学在疾病诊断和治疗中的作用.
- 介绍药物发现和疾病研究的计算方法的最新进展.
主要方法:
- 使用虚拟化学图书馆和虚拟选来识别新型杀伤性化合物.
- 应用机器学习,组合模型,以及用于疾病诊断的特征选择 (例如,心脏病,COVID-19).
- 调查基因组变异,特定化合物的抗菌活性,并预测药物特性/毒性.
主要成果:
- 虚拟图书馆旨在提高有前途的药物化合物的质量和发现.
- 使用整体模型在疾病诊断中实现了高准确率.
- 证明了基因组变异与疾病之间的相关性,并确定了pyrazole/benzimidazole化合物的抗菌潜力.
结论:
- 化学信息学和机器学习是现代药物发现和开发的重要工具.
- 生物信息学为疾病分类,诊断和理解疾病机制提供了强大的解决方案.
- 计算方法的整合大大提高了药物疗效和安全性的预测.
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