Prediction and Optimization of Nav1.7 Sodium Channel Inhibitors Based on Machine Learning and Simulated Annealing
Prediction and Optimization of Nav1.7 Sodium Channel Inhibitors Based on Machine Learning and Simulated Annealing
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基于机器学习和模拟退火的Na(v)1.7钠通道抑制剂的预测和优化
DOI：
10.1021/acs.jcim.9b01180
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发表时间：
2020-06-22
影响因子：
5.6
通讯作者：
Huang, Zhuo
中科院分区：
文献类型：
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作者：
Kong, Weikaixin;Tu, Xinyu;Huang, Zhuo
虽然Na（v）1.7钠通道是一个很有前途的疼痛药物靶点，但发现Na（v）1.7抑制剂的传统筛选策略非常费力和耗时。在此，我们的目的是建立机器学习模型，用于筛选和设计强效和有效的Na（v）1.7钠通道抑制剂。我们定制了来自ChEMBL和BindingDB的不平衡数据集，以训练和过滤最佳分类模型。采用全细胞电压钳技术对抑制剂进行了验证。我们组装了一个分子组优化方法，结合语法变分自动编码器，分类模型，和模拟退火。我们发现RF-CDK模型（随机森林+ CDK指纹）在不平衡数据集上表现最好。在可能具有抑制作用的三种化合物中，去甲替林已得到实验验证。在分子优化过程中，以位于RF-CDK适用域的40个分子为起点，其中34个分子进化为具有更大分子评分（MS）的分子。MS最高的分子来自CHEMBL 232524 S。我们为Na（v）1.7抑制剂开发的模型和方法也适用于其他靶标。
Although the Na(v)1.7 sodium channel is a promising drug target for pain, traditional screening strategies for discovery of Na(v)1.7 inhibitors are very painstaking and time-consuming. Herein, we aimed to build machine learning models for screening and design of potent and effective Na(v)1.7 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 CHEMBL232524S. The model and method we developed for Na(v)1.7 inhibitors are also applicable to other targets.