Abelson kinase (c-Abl) is a non-receptor tyrosine kinase involved in several biological processes essential for cell differentiation, migration, proliferation, and survival. This enzyme's activation might be an alternative strategy for treating diseases such as neutropenia induced by chemotherapy, prostate, and breast cancer. Recently, a series of compounds that promote the activation of c-Abl has been identified, opening a promising ground for c-Abl drug development. Structure-based drug design (SBDD) and ligand-based drug design (LBDD) methodologies have significantly impacted recent drug development initiatives. Here, we combined SBDD and LBDD approaches to characterize critical chemical properties and interactions of identified c-Abl's activators. We used molecular docking simulations combined with tree-based machine learning models—decision tree, AdaBoost, and random forest to understand the c-Abl activators' structural features required for binding to myristoyl pocket, and consequently, to promote enzyme and cellular activation. We obtained predictive and robust models with Matthews correlation coefficient values higher than 0.4 for all endpoints and identified characteristics that led to constructing a structure–activity relationship model (SAR).

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中文翻译：

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使用基于树的机器学习模型和分子对接对 ABL 激酶激活的分子见解

Abelson 激酶 (c-Abl) 是一种非受体酪氨酸激酶，参与细胞分化、迁移、增殖和存活所必需的几个生物学过程。这种酶的激活可能是治疗由化疗引起的中性粒细胞减少症、前列腺癌和乳腺癌等疾病的替代策略。最近，一系列促进 c-Abl 活化的化合物已被鉴定出来，为 c-Abl 药物开发开辟了广阔的前景。基于结构的药物设计 (SBDD) 和基于配体的药物设计 (LBDD) 方法对最近的药物开发计划产生了重大影响。在这里，我们结合 SBDD 和 LBDD 方法来表征已识别的 c-Abl 激活剂的关键化学性质和相互作用。我们使用分子对接模拟与基于树的机器学习模型（决策树、AdaBoost 和随机森林）相结合，以了解 c-Abl 激活剂与肉豆蔻酰口袋结合所需的结构特征，从而促进酶和细胞活化。我们获得了所有端点的 Matthews 相关系数值高于 0.4 的预测性和稳健模型，并确定了导致构建结构-活性关系模型 (SAR) 的特征。

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