Prediction and Optimization of NaV1.7 Sodium Channel Inhibitors Based on Machine Learning and Simulated Annealing.,Journal of Chemical Information and Modeling

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Prediction and Optimization of NaV1.7 Sodium Channel Inhibitors Based on Machine Learning and Simulated Annealing.
Journal of Chemical Information and Modeling ( IF 5.6 ) Pub Date : 2020-05-18 , DOI: 10.1021/acs.jcim.9b01180
Weikaixin Kong ₁ , Xinyu Tu ₁ , Weiran Huang ₁ , Yang Yang ₂ , Zhengwei Xie ₃ , Zhuo Huang _{1,

4}

Affiliation

Although the Na_V1.7 sodium channel is a promising drug target for pain, traditional screening strategies for discovery of Na_V1.7 inhibitors are very painstaking and time-consuming. Herein, we aimed to build machine learning models for screening and design of potent and effective Na_V1.7 sodium channel inhibitors. We customized the imbalanced data set from ChEMBL and BindingDB to train and filter the best classification model. Then, the whole-cell voltage-clamp was employed to validate the inhibitors. We assembled a molecular group optimization method by combining the Grammar Variational Autoencoder, classification model, and simulated annealing. We found that the RF-CDK model (random forest + CDK fingerprint) performs best in the imbalanced data set. Of the three compounds that may have inhibitory effects, nortriptyline has been experimentally verified. In the molecule optimization process, 40 molecules located in the applicability domain of RF-CDK were used as a starting point, among which 34 molecules evolved to molecules with greater molecular scores (MS). The molecule with the highest MS was derived from CHEMBL2325245. The model and method we developed for Na_V1.7 inhibitors are also applicable to other targets.

中文翻译：

基于机器学习和模拟退火的NaV1.7钠通道抑制剂的预测和优化。

尽管Na _V 1.7钠通道是治疗疼痛的有希望的药物靶标，但发现Na _V 1.7抑制剂的传统筛选策略非常费时费力。在此，我们旨在建立机器学习模型，以筛选和设计有效有效的Na _V1.7钠通道抑制剂。我们从ChEMBL和BindingDB中定制了不平衡数据集，以训练和过滤最佳分类模型。然后，使用全细胞电压钳来验证抑制剂。我们通过组合语法变分自动编码器，分类模型和模拟退火组装了分子组优化方法。我们发现RF-CDK模型（随机森林+ CDK指纹）在不平衡数据集中表现最佳。在可能具有抑制作用的三种化合物中，去甲替林已通过实验验证。在分子优化过程中，以RF-CDK的适用范围内的40个分子为起点，其中34个分子进化为分子评分（MS）更高的分子。MS最高的分子来自CHEMBL2325245。_V 1.7抑制剂也适用于其他靶标。

更新日期：2020-06-23

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