Geometric graph learning with extended atom-types features for protein-ligand binding affinity prediction
Geometric graph learning with extended atom-types features for protein-ligand binding affinity prediction
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DOI：
10.1016/j.compbiomed.2023.107250
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发表时间：
2023-07-27
影响因子：
7.7
通讯作者：
Nguyen，Duc Duy
中科院分区：
文献类型：
--
作者：
Rana，Md Masud;Nguyen，Duc Duy
理解和准确预测蛋白质-配体结合亲和力在药物设计和发现过程中至关重要。目前，基于机器学习的方法由于其效率和准确性以及蛋白质-配体复合物的结构和结合亲和力数据的日益可用性而作为预测结合亲和力的手段越来越受欢迎。在生物分子研究中，图论已经被广泛应用，因为图可以被用来以自然的方式对分子或分子复合物进行建模。在目前的工作中，我们升级基于图的学习者的蛋白质配体相互作用的研究，通过整合广泛的原子类型，如SYBYL和扩展连接交互功能（ECIF）到多尺度加权彩色图（MWCG）。通过与梯度提升决策树（GBDT）机器学习算法配对，我们的方法产生了两种不同的方法，即sysloggggl-score和ecif GGL-Score。我们的两个模型都使用药物设计领域常用的三个基准数据集（即CASF-2007，CASF-2013和CASF-2016）对其评分能力进行了广泛验证。我们最好的模型symptomGGL-Score的性能与其他国家的最先进的模型在结合亲和力预测每个基准进行了比较。虽然我们的两个模型都达到了最先进的结果，但SYBYL原子类型模型syslogggl-score在所有基准测试中都远远优于其他方法。最后，性能最好的SYBYL原子型模型进行评估的两个测试集是独立的CASF基准。
Understanding and accurately predicting protein-ligand binding affinity are essential in the drug design and discovery process. At present, machine learning-based methodologies are gaining popularity as a means of predicting binding affinity due to their efficiency and accuracy, as well as the increasing availability of structural and binding affinity data for protein-ligand complexes. In biomolecular studies, graph theory has been widely applied since graphs can be used to model molecules or molecular complexes in a natural manner. In the present work, we upgrade the graph-based learners for the study of protein-ligand interactions by integrating extensive atom types such as SYBYL and extended connectivity interactive features (ECIF) into multiscale weighted colored graphs (MWCG). By pairing with the gradient boosting decision tree (GBDT) machine learning algorithm, our approach results in two different methods, namely sybyl GGL-Score and ecif GGL-Score. Both of our models are extensively validated in their scoring power using three commonly used benchmark datasets in the drug design area, namely CASF-2007, CASF-2013, and CASF-2016. The performance of our best model sybyl GGL-Score is compared with other state-of-the-art models in the binding affinity prediction for each benchmark. While both of our models achieve state-of-the-art results, the SYBYL atom-type model sybyl GGL-Score outperforms other methods by a wide margin in all benchmarks. Finally, the best-performing SYBYL atom-type model is evaluated on two test sets that are independent of CASF benchmarks.