使用人工智能和机器学习进行染色体预测的当前趋势
Yash Raj Singh1, Darshil B Shah1, Mangesh Kulkarni2
1Department of Pharmaceutical Quality Assurance, LJ Institute of Pharmacy, LJ University, Ahmedabad, Gujarat, India.
人工智能 (AI) 和机器学习 (ML) 在染色学中提供更快,更准确的预测. 这些方法,特别是人工神经网络 (ANN),在预测色谱特征和保留时间方面表现优于传统模型.
科学领域:
- 分析化学 分析化学
- 计算化学的计算化学
背景情况:
- 人工智能 (AI) 和机器学习 (ML) 越来越多地被用于它们在各种科学领域的预测能力,准确性和速度.
- 在染色学中,AI和ML对于方法开发特别有价值,为预测染色学特征提供高效和准确的解决方案.
研究的目的:
- 审查用于确定色谱特征的各种AI和ML模型.
- 探索人工神经网络 (ANN) 技术及其在液体染色学中的优势与经典线性模型相比.
- 突出将模糊系统与ANN集成的好处,并将AI / ML与定量结构-保留关系 (QSRR) 结合起来,以提高预测.
主要方法:
- 对AI和ML在染色学中的应用现有文献的审查.
- 对基于人工神经网络 (ANN) 的技术进行分析,用于染色学预测.
- 研究混合方法,包括与ANN和QSRR结合ANN的模糊系统.
主要成果:
- 与经典线性模型相比,与ANN相关的技术显示出更高的准确性和预测色谱特征的潜力.
- 模糊系统与ANN的整合提供了更有效,更准确的染色学预测方法.
- 将AI/ML算法与QSRR结合起来,可以显著提高目标分子保留预测的准确性.
结论:
- 人工智能和机器学习算法,特别是ANN和混合模型,为推进色谱方法开发和预测提供了强大的工具.
- 这些先进的计算方法显示出克服分析化学挑战的巨大潜力,从而实现更精确,更有效的分析.
- 人工智能/ML与QSRR的整合代表了在液体染色学中准确预测保留的有希望的方向.
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