National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR 72079, USA.
National Center for Advancing Translational Sciences, National Institutes of Health, Bethesda, MD 20892, USA.
Biomolecules. 2024 Jan 5;14(1):72. doi: 10.3390/biom14010072.
Molecular recognition is fundamental in biology, underpinning intricate processes through specific protein-ligand interactions. This understanding is pivotal in drug discovery, yet traditional experimental methods face limitations in exploring the vast chemical space. Computational approaches, notably quantitative structure-activity/property relationship analysis, have gained prominence. Molecular fingerprints encode molecular structures and serve as property profiles, which are essential in drug discovery. While two-dimensional (2D) fingerprints are commonly used, three-dimensional (3D) structural interaction fingerprints offer enhanced structural features specific to target proteins. Machine learning models trained on interaction fingerprints enable precise binding prediction. Recent focus has shifted to structure-based predictive modeling, with machine-learning scoring functions excelling due to feature engineering guided by key interactions. Notably, 3D interaction fingerprints are gaining ground due to their robustness. Various structural interaction fingerprints have been developed and used in drug discovery, each with unique capabilities. This review recapitulates the developed structural interaction fingerprints and provides two case studies to illustrate the power of interaction fingerprint-driven machine learning. The first elucidates structure-activity relationships in β2 adrenoceptor ligands, demonstrating the ability to differentiate agonists and antagonists. The second employs a retrosynthesis-based pre-trained molecular representation to predict protein-ligand dissociation rates, offering insights into binding kinetics. Despite remarkable progress, challenges persist in interpreting complex machine learning models built on 3D fingerprints, emphasizing the need for strategies to make predictions interpretable. Binding site plasticity and induced fit effects pose additional complexities. Interaction fingerprints are promising but require continued research to harness their full potential.
分子识别在生物学中至关重要，它通过特定的蛋白质-配体相互作用支撑着复杂的过程。这种理解在药物发现中至关重要，但传统的实验方法在探索广阔的化学空间方面存在局限性。计算方法，特别是定量构效/性质关系分析，已经变得越来越重要。分子指纹编码分子结构并作为性质概况，这在药物发现中是必不可少的。虽然二维（2D）指纹通常被使用，但三维（3D）结构相互作用指纹提供了针对靶蛋白的增强的结构特征。基于相互作用指纹训练的机器学习模型能够进行精确的结合预测。最近的研究重点已经转移到基于结构的预测建模上，机器学习评分函数由于关键相互作用指导的特征工程而表现出色。值得注意的是，由于其稳健性，3D 相互作用指纹正在获得关注。已经开发并在药物发现中使用了各种结构相互作用指纹，每种指纹都具有独特的功能。这篇综述回顾了已开发的结构相互作用指纹，并提供了两个案例研究来说明基于相互作用指纹的机器学习的强大功能。第一个案例研究阐明了β2 肾上腺素受体配体的结构-活性关系，展示了区分激动剂和拮抗剂的能力。第二个案例研究采用基于逆合成的预训练分子表示来预测蛋白质-配体解离率，提供了对结合动力学的深入了解。尽管取得了显著的进展，但在解释基于 3D 指纹构建的复杂机器学习模型方面仍然存在挑战，强调了需要策略来使预测具有可解释性。结合位点的可塑性和诱导契合效应带来了额外的复杂性。相互作用指纹具有很大的潜力，但需要进一步的研究来充分发挥它们的潜力。
