ToxinPred 3.0:一个改进的方法来预测的毒性
Anand Singh Rathore1, Shubham Choudhury1, Akanksha Arora1
1Department of Computational Biology, Indraprastha Institute of Information Technology, Okhla Phase 3, New Delhi, 110020, India.
Computers in biology and medicine
|July 22, 2024
概括
这项研究介绍了ToxinPred3,这是一个改进的计算工具,用于预测治疗性毒性. 先进的混合方法显著提高了准确性和可靠性,通过早期识别潜在有害来帮助药物开发.
科学领域:
- 计算生物学是一种计算生物学.
- 生物信息学是一种生物信息学.
- 药物发现 药物发现
背景情况:
- 毒性是治疗性开发的一个主要障碍,导致临床试验失败.
- 像ToxinPred这样的现有预测方法在覆盖范围或特异性方面存在局限性.
- 准确预测毒性对于推进基于的治疗方法至关重要.
研究的目的:
- 开发一种更准确,更可靠的计算方法来预测毒性.
- 克服以前基于相似性和基于动机的方法的局限性.
- 为科学界提供一个可访问的工具来评估毒性.
主要方法:
- 使用了相似性/对齐性 (BLAST) 和基于动机 (MERCI) 的方法.
- 开发了使用机器学习 (Extra Tree) 和深度学习 (ANN-LSTM) 的无对齐方法.
- 创建了混合模型,结合了基于动机和机器学习的方法,以及大型语言模型 (ESM2-t33).
主要成果:
- 机器学习 (额外的树) 实现了AUROC 0.95,MCC 0.78.
- 深度学习 (ANN-LSTM) 实现了AUROC 0.93 和MCC 0.71.
- 基于混合动力和机器学习模型的混合动力模型在独立数据集上以AUROC 0.98和MCC 0.81实现了最高的性能.
结论:
- 开发的ToxinPred3表现出比现有的毒性预测方法更高的性能.
- 混合方法有效地平衡了灵敏度和特异性,提高了预测可靠性.
- ToxinPred3可以作为独立软件, pip 包和社区使用的网络服务器提供.
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