Advancing Peptide-Based Cancer Therapy with AI: In-Depth Analysis of State-of-the-Art AI Models

IF 5.6 2区化学 Q1 CHEMISTRY, MEDICINAL

Journal of Chemical Information and Modeling Pub Date : 2024-06-14 DOI:10.1021/acs.jcim.4c00295

Sadik Bhattarai, Hilal Tayara* and Kil To Chong*,

引用次数: 0

Abstract

Anticancer peptides (ACPs) play a vital role in selectively targeting and eliminating cancer cells. Evaluating and comparing predictions from various machine learning (ML) and deep learning (DL) techniques is challenging but crucial for anticancer drug research. We conducted a comprehensive analysis of 15 ML and 10 DL models, including the models released after 2022, and found that support vector machines (SVMs) with feature combination and selection significantly enhance overall performance. DL models, especially convolutional neural networks (CNNs) with light gradient boosting machine (LGBM) based feature selection approaches, demonstrate improved characterization. Assessment using a new test data set (ACP10) identifies ACPred, MLACP 2.0, AI4ACP, mACPred, and AntiCP2.0_AAC as successive optimal predictors, showcasing robust performance. Our review underscores current prediction tool limitations and advocates for an omnidirectional ACP prediction framework to propel ongoing research.

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利用人工智能推进基于肽的癌症疗法：最新人工智能模型的深入分析。

抗癌肽（ACPs）在选择性靶向和消除癌细胞方面发挥着至关重要的作用。评估和比较各种机器学习（ML）和深度学习（DL）技术的预测结果是一项挑战，但对抗癌药物研究至关重要。我们对 15 种 ML 模型和 10 种 DL 模型（包括 2022 年之后发布的模型）进行了全面分析，发现支持向量机（SVM）通过特征组合和选择可显著提高整体性能。DL模型，尤其是采用基于轻梯度提升机（LGBM）的特征选择方法的卷积神经网络（CNNs），在特征描述方面也有所改进。使用新的测试数据集（ACP10）进行的评估确定了 ACPred、MLACP 2.0、AI4ACP、mACPred 和 AntiCP2.0_AAC 为连续的最佳预测器，展示了强大的性能。我们的综述强调了当前预测工具的局限性，并主张建立一个全方位的 ACP 预测框架，以推动正在进行的研究。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of Chemical Information and Modeling 化学-化学综合

CiteScore

9.80

自引率

10.70%

发文量

529

审稿时长

1.4 months

期刊介绍： The Journal of Chemical Information and Modeling publishes papers reporting new methodology and/or important applications in the fields of chemical informatics and molecular modeling. Specific topics include the representation and computer-based searching of chemical databases, molecular modeling, computer-aided molecular design of new materials, catalysts, or ligands, development of new computational methods or efficient algorithms for chemical software, and biopharmaceutical chemistry including analyses of biological activity and other issues related to drug discovery. Astute chemists, computer scientists, and information specialists look to this monthly’s insightful research studies, programming innovations, and software reviews to keep current with advances in this integral, multidisciplinary field. As a subscriber you’ll stay abreast of database search systems, use of graph theory in chemical problems, substructure search systems, pattern recognition and clustering, analysis of chemical and physical data, molecular modeling, graphics and natural language interfaces, bibliometric and citation analysis, and synthesis design and reactions databases.