Laboratory of Fundamental and Applied Research of Quality and Technology of Food Production, Moscow State University of Food Production , Moscow, Russian Federation.
Postgraduate Program in Natural and Synthetic Bioactive Products, Federal University of Paraíba , João Pessoa, Brazil.
SAR QSAR Environ Res. 2020 Nov;31(11):815-836. doi: 10.1080/1062936X.2020.1818617. Epub 2020 Sep 24.
Liver cancers are one of the leading fatal diseases among malignant neoplasms. Current chemotherapeutic treatments used to fight these illnesses have become less efficient in terms of both efficacy and safety. Therefore, there is a great need of search for new anti-liver cancer agents and this can be accelerated by using computer-aided drug discovery approaches. In this work, we report the development of the first cell-based multi-target model based on quantitative structure-activity relationships (CBMT-QSAR) for the design and prediction of chemicals as anticancer agents against 17 liver cancer cell lines. While having a good quality and predictive power (accuracy higher than 80%) in the training and test sets, respectively, the CBMT-QSAR model was employed as a tool to directly extract suitable fragments from the physicochemical and structural interpretations of the molecular descriptors. Some of these desirable fragments were assembled, leading to the virtual design of eight molecules with drug-like properties, with six of them being predicted as versatile anticancer agents against the 17 liver cancer cell lines reported here.
肝癌是恶性肿瘤中导致死亡的主要疾病之一。目前用于治疗这些疾病的化疗方法在疗效和安全性方面都变得不那么有效。因此，非常需要寻找新的抗肝癌药物，而这可以通过使用计算机辅助药物发现方法来加速。在这项工作中，我们报告了第一个基于定量构效关系（CBMT-QSAR）的基于细胞的多靶模型的开发，用于设计和预测作为抗癌剂的化学品，以对抗 17 种肝癌细胞系。该 CBMT-QSAR 模型在训练集和测试集上分别具有良好的质量和预测能力（准确性高于 80%），可作为工具直接从分子描述符的物理化学和结构解释中提取合适的片段。将其中一些理想的片段组装在一起，导致虚拟设计了 8 种具有类药性的分子，其中 6 种被预测为针对这里报道的 17 种肝癌细胞系的多用途抗癌剂。
