Multi-objective Genetic Algorithm for De Novo Drug Design (MoGADdrug)
Multi-objective Genetic Algorithm for De Novo Drug Design (MoGADdrug)
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DOI：
10.2174/1573409916666200620194143
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发表时间：
2021-01-01
影响因子：
1.7
通讯作者：
Coumar, Mohane S.
中科院分区：
文献类型：
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作者：
Devi, R. Vasundhara;Sathya, S. Siva;Coumar, Mohane S.
背景：本文提出了一种用于De novo drug design （MoGADdrug）的多目标遗传算法，用于设计与某些参比分子相似的新型类药物分子。所开发的算法接受从已批准的药物中提取的片段集，并在片段库中可用，并根据指定的规则将它们组合起来，通过计算机方法发现新药。方法：在此过程中，使用遗传算法将片段编码为变长染色体的基因，并在各代中应用各种遗传算子。采用加权和方法同时优化新药与参考分子的结构相似性和药物相似性。结果：选取利多卡因、呋喃嘧啶衍生物、伊马替尼、阿托伐他汀和格列吡嗪5种参比分子对算法进行性能评价。结论：利用锌、PubChem数据库和对接调查对新设计的分子进行了分析。
Background: A multi-objective genetic algorithm for De novo drug design (MoGADdrug) has been proposed in this paper for the design of novel drug-like molecules similar to some reference molecules. The algorithm developed accepts a set of fragments extracted from approved drugs and available in fragment libraries and combines them according to specified rules to discover new drugs through the in-silico method. Methods: For this process, a genetic algorithm has been used, which encodes the fragments as genes of variable length chromosomes and applies various genetic operators throughout the generations. A weighted sum approach is used to simultaneously optimize the structural similarity of the new drug to a reference molecule as well as its drug-likeness property. Results: Five reference molecules namely Lidocaine, Furano-pyrimidine derivative, Imatinib, Atorvastatin and Glipizide have been chosen for the performance evaluation of the algorithm. Conclusion: Also, the newly designed molecules were analyzed using ZINC, PubChem databases and docking investigations.