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VISH-Pred:一组微调的ESM模型,用于蛋白质毒性预测

Raghvendra Mall¹, Ankita Singh¹, Chirag N Patel¹

¹Biotechnology Research Center, Technology Innovation Institute, P.O. Box 9639, Abu Dhabi, United Arab Emirates.

Briefings in bioinformatics

|June 6, 2024

概括

使用人工智能,VISH-Pred准确预测和蛋白质毒性,克服了新疗法的关键挑战. 这种计算工具有助于识别安全的候选药物,并推进基于蛋白质的生物制剂.

科学领域:

* 计算生物学和生物信息学.
* 药物发现和开发.
* 医学中的人工智能.

背景情况:

*和蛋白质疗法为各种疾病提供了有前途的治疗方法.
* 蛋白质毒性是开发有效的基于蛋白质的疗法的主要障碍.
* 准确的in silico方法对于识别有毒和无毒蛋白质至关重要.

研究的目的:

* 开发一个准确的计算框架来预测和蛋白质的毒性.
* 识别无毒蛋白质,以扩大基于蛋白质的生物制剂的潜力.
* 提供一种工具,以毒性为基础,帮助过潜在的治疗候选药物.

主要方法:

* 开发了VISH-Pred,这是一个集体框架,使用精心调整的ESM2变压器模型.
*采用不足样本的技术来解决毒性数据中的类不平衡.
*来自ESM2模型的集成表示与机器学习算法 (LightGBM,XGBoost).
*使用了大量经过实验验证的蛋白质和毒性数据集.

主要成果:

*VISH-Pred在三个独立的盲测试中实现了高性能,马修斯相关系数为0.737,0.716和0.322.
*与其他方法相比,该框架显示出更高的F1分数 (0.759,0.696,0.713),超过10%以上.
*VISH-Pred表现出强大的概括能力,在独立数据集上获得最高准确度和AUC分数.

结论:

* VISH-Pred可以准确地识别有毒和无毒的/蛋白质.
* 该框架为研究人员在辨别蛋白质毒性方面提供了宝贵的资产.
*以Web服务器的形式提供VISH-Pred,可促进基于蛋白质的疗法的高效开发.

关键词:

在ESM2模型中,深度学习是一种深度学习.集合方法集合方法集合方法精细调整精细调整的毒性的毒性蛋白质毒性蛋白质毒性

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