药物相似性预测的当前趋势和挑战:它们是否具有普遍性和可解释性?

Wenyu Zhu, Yanxing Wang, Yan Niu, Liangren Zhang, Zhenming Liu
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引用次数: 0

摘要

重要性:化合物的药物相似性是对其在临床试验中取得成功的潜力的全面评估,并且通过过滤不利性质和不良开发潜力的化合物来节省研究支出是必不可少的。为此,一种鲁棒的药物相似性预测方法必不可少。各种方法,包括判别规则、统计模型和机器学习模型,已经开发出基于物理化学性质和结构特征来预测药物相似性。值得注意的是,新型深度学习技术的最新进展显著提高了药物相似性预测,特别是在分类性能方面。在这篇综述中,我们讨论了药物相似性预测的发展前景,重点介绍了采用新型深度学习技术的方法,并强调了药物相似性预测目前面临的挑战,特别是在泛化和可解释性方面。此外,我们还探索了潜在的补救措施,并概述了未来研究的有希望的途径。结论:尽管存在一般化和可解释性方面的障碍,但新的深度学习技术在药物相似性预测方面具有巨大的潜力,值得进一步研究。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Current Trends and Challenges in Drug-likeness Prediction: Are They Generalizable and Interpretable?
Importance : Drug-likeness of a compound is an overall assessment of its potential to succeed in clinical trials, and is essential for economizing research expenditures by filtering compounds with unfavorable properties and poor development potential. To this end, a robust drug-likeness prediction method is indispensable. Various approaches, including discriminative rules, statistical models, and machine learning models, have been developed to predict drug-likeness based on physiochemical properties and structural features. Notably, recent advancements in novel deep learning techniques have significantly advanced drug-likeness prediction, especially in classification performance. Highlights : In this review, we addressed the evolving landscape of drug-likeness prediction, with emphasis on methods employing novel deep learning techniques, and highlighted the current challenges in drug-likeness prediction, specifically regarding the aspects of generalization and interpretability. Moreover, we explored potential remedies and outlined promising avenues for future research. Conclusion : Despite the hurdles of generalization and interpretability, novel deep learning techniques have great potential in drug-likeness prediction and are worthy of further research efforts.
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