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PT-Finder:一种多模式神经网络方法用于目标识别

Hossam Nada¹, Sungdo Kim¹, Kyeong Lee¹

¹BK21 FOUR Team and Integrated Research Institute for Drug Development, College of Pharmacy, Dongguk University-Seoul, Goyang, 10326, Republic of Korea.

Computers in biology and medicine

|April 18, 2024

概括

机器学习通过预测生物活性化合物点来加速药物发现. 一个名为PT-Finder的新工具使用神经网络准确识别蛋白质目标,从而加快工作流程.

科学领域:

计算化学是一种计算化学.
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:

高效的生物活性化合物目标识别是至关重要的,但具有挑战性和昂贵.
机器学习为预测化合物-蛋白质相互作用提供了一个有希望的方法.
目前的方法通常依赖于连接体的结构相似性,限制范围.

研究的目的:

开发一种机器学习模型,用于预测化学化合物的蛋白质标.
为快速准确的目标识别创建一个用户友好的应用程序.
为了加速药物发现和重新利用管道.

主要方法:

使用蛋白质序列和活性抑制剂构建了一个多模态神经网络.
该模型在一个全面的蛋白质和连接体库上进行了训练.
使用精度和MPRAUC指标验证了性能.

主要成果:

开发的模型实现了82%的准确性和0.80.8的MPRAUC.
基于训练模型创建了一个离线应用程序PT-Finder.
PT-Finder可以在几秒钟内预测数百种化合物的目标.

结论:

PT-Finder为蛋白质标识提供了一个快速,准确和用户友好的解决方案.
该工具通过快速预测,显著加快药物发现工作流程.
自由访问的源代码和应用程序促进了更广泛的采用和研究.

关键词:

药物发现药物发现多输入神经网络多输入神经网络目标识别目标识别

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