将遗传算法与深度学习整合用于新型酪氨酸激酶抑制剂的生成和生物活性预测

Ricardo Romero
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引用次数: 0

摘要

人工智能与生物信息学的交叉使药物发现取得了重大进展,特别是通过应用机器学习模型。在这项研究中,我们提出了一种使用遗传算法和深度学习模型的组合方法,以解决药物发现的两个关键方面:新型酪氨酸激酶抑制剂的生成及其生物活性的预测。生成模型利用遗传算法创造出具有优化的 ADMET(吸收、分布、代谢、排泄和毒性)和药物相似性的新小分子。同时,还采用了深度学习模型来预测这些生成的分子对酪氨酸激酶的生物活性,酪氨酸激酶是参与各种细胞过程和癌症进展的关键酶家族。通过整合这些先进的计算方法,我们展示了一个强大的框架,用于加速潜在酪氨酸激酶抑制剂的生成和鉴定,从而为更高效、更有效的早期药物发现过程做出贡献。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Integration of Genetic Algorithms and Deep Learning for the Generation and Bioactivity Prediction of Novel Tyrosine Kinase Inhibitors
The intersection of artificial intelligence and bioinformatics has enabled significant advancements in drug discovery, particularly through the application of machine learning models. In this study, we present a combined approach using genetic algorithms and deep learning models to address two critical aspects of drug discovery: the generation of novel tyrosine kinase inhibitors and the prediction of their bioactivity. The generative model leverages genetic algorithms to create new small molecules with optimized ADMET (absorption, distribution, metabolism, excretion, and toxicity) and drug-likeness properties. Concurrently, a deep learning model is employed to predict the bioactivity of these generated molecules against tyrosine kinases, a key enzyme family involved in various cellular processes and cancer progression. By integrating these advanced computational methods, we demonstrate a powerful framework for accelerating the generation and identification of potential tyrosine kinase inhibitors, contributing to more efficient and effective early-stage drug discovery processes.
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