Machine Learning in Enhancing Protein Binding Sites Predictions - What Has Changed Since Then?

IF 1.6 4区 医学 Q4 BIOCHEMICAL RESEARCH METHODS
Oluwayimika E Ibitoye, Mahmoud Soliman
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引用次数: 0

Abstract

Accurate identification of protein binding sites is pivotal for understanding molecular interactions and facilitating drug discovery efforts. However, the dynamic nature of proteinligand interactions presents a formidable challenge, necessitating innovative approaches to bridge the gap between theoretical predictions and experimental realities. This review explores the challenges and recent advancements in protein binding site prediction. Specifically, we highlight the integration of molecular dynamics simulations, machine learning, and deep learning techniques to capture the dynamic and complex nature of protein-ligand interactions. Additionally, we discuss the importance of integrating experimental data, such as structural information and biochemical assays, to enhance prediction accuracy and reliability. By navigating the intersection of classical and the onset of machine learning and deep learning approaches, we aim to provide insights into current state-of-the-art techniques and chart a course for future protein binding site prediction advancements. Ultimately, these efforts could unravel the mysteries of protein-ligand interactions and accelerate drug discovery endeavors.

机器学习在增强蛋白质结合位点预测中的应用--此后发生了哪些变化?
准确鉴定蛋白质结合位点对于理解分子相互作用和促进药物发现工作至关重要。然而,蛋白质配体相互作用的动态性质带来了严峻的挑战,需要创新的方法来弥合理论预测与实验现实之间的差距。本综述探讨了蛋白质结合位点预测所面临的挑战和最新进展。具体来说,我们强调了分子动力学模拟、机器学习和深度学习技术的整合,以捕捉蛋白质配体相互作用的动态和复杂性质。此外,我们还讨论了整合实验数据(如结构信息和生化检测)以提高预测准确性和可靠性的重要性。通过探索经典方法与机器学习和深度学习方法的交叉点,我们旨在深入了解当前最先进的技术,并为未来蛋白质结合位点预测的发展指明方向。最终,这些努力将揭开蛋白质-配体相互作用的神秘面纱,加速药物发现的进程。
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来源期刊
CiteScore
3.10
自引率
5.60%
发文量
327
审稿时长
7.5 months
期刊介绍: Combinatorial Chemistry & High Throughput Screening (CCHTS) publishes full length original research articles and reviews/mini-reviews dealing with various topics related to chemical biology (High Throughput Screening, Combinatorial Chemistry, Chemoinformatics, Laboratory Automation and Compound management) in advancing drug discovery research. Original research articles and reviews in the following areas are of special interest to the readers of this journal: Target identification and validation Assay design, development, miniaturization and comparison High throughput/high content/in silico screening and associated technologies Label-free detection technologies and applications Stem cell technologies Biomarkers ADMET/PK/PD methodologies and screening Probe discovery and development, hit to lead optimization Combinatorial chemistry (e.g. small molecules, peptide, nucleic acid or phage display libraries) Chemical library design and chemical diversity Chemo/bio-informatics, data mining Compound management Pharmacognosy Natural Products Research (Chemistry, Biology and Pharmacology of Natural Products) Natural Product Analytical Studies Bipharmaceutical studies of Natural products Drug repurposing Data management and statistical analysis Laboratory automation, robotics, microfluidics, signal detection technologies Current & Future Institutional Research Profile Technology transfer, legal and licensing issues Patents.
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