ElixirSeeker:利用注意力驱动的分子指纹融合发现抗衰老化合物的机器学习框架

Yan Pan, Hongxia Cai, Fang Ye, Wentao Xu, Zhihang Huang, Jingyuan Zhu, Yiwen Gong, Yutong Li, Anastasia Ngozi Ezemaduka, Shan Gao, Shunqi Liu, Guojun Li, Hao Li, Jing Yang, Junyu Ning, Bo Xian
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引用次数: 0

摘要

尽管人们对抗衰老药物开发的兴趣与日俱增,但高成本和低成功率构成了巨大挑战。我们介绍了 ElixirSeeker,这是一个新的机器学习框架,旨在利用注意力驱动的分子指纹融合,帮助加快潜在抗衰老化合物的发现。我们的方法整合了由不同算法生成的分子指纹,并利用 XGBoost 选择最佳指纹长度。随后,我们为分子指纹分配权重,并采用核主成分分析法(KPCA)来降低维度,从而整合不同的注意力驱动方法。我们使用 DrugAge 数据库对算法进行了训练。我们的综合分析表明,64 位 Attention-ElixirFP 保持了较高的预测准确率和 F1 分数,同时将计算成本降至最低。利用 ElixirSeeker 筛选外部化合物数据库,我们发现了许多有前景的候选抗衰老药物。我们测试了排名前 6 位的化合物,发现其中 4 种化合物能延长秀丽隐杆线虫的寿命,包括 Polyphyllin Ⅵ、Medrysone、Thymoquinone 和 Medrysone。这项研究表明,注意力驱动的指纹融合能最大限度地学习分子活性特征,为高通量机器学习发现抗衰老分子提供了一种新方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
ElixirSeeker: A Machine Learning Framework Utilizing Attention-Driven Fusion of Molecular Fingerprints for the Discovery of Anti-Aging Compounds
Despite the growing interest in anti-aging drug development, high cost and low success rate pose a significant challenge. We present ElixirSeeker, a new machine-learning framework designed to help speed up the discovery of potential anti-aging compounds by utilizing the attention-driven fusion of molecular fingerprints. Our approach integrates molecular fingerprints generated by different algorithms and utilizes XGBoost to select optimal fingerprint lengths. Subsequently, we assign weights to the molecular fingerprints and employ Kernel Principal Component Analysis (KPCA) to reduce dimensionality, integrating different attention-driven methods. We trained the algorithm using DrugAge database. Our comprehensive analyses demonstrate that 64-bit Attention-ElixirFP maintains high predictive accuracy and F1 score while minimizing computational cost. Using ElixirSeeker to screen external compound databases, we identified a number of promising candidate anti-aging drugs. We tested top 6 hits and found that 4 of these compounds extend the lifespan of Caenorhabditis elegans, including Polyphyllin Ⅵ, Medrysone, Thymoquinone and Medrysone. This study illustrates that attention-driven fusion of fingerprints maximizes the learning of molecular activity features, providing a novel approach for high-throughput machine learning discovery of anti-aging molecules.
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