Department of Computer Science, Pondicherry University, Pondicherry, India.
Centre for Bioinformatics, Pondicherry University, Pondicherry, India.
Curr Comput Aided Drug Des. 2021;17(3):445-457. doi: 10.2174/1573409916666200620194143.
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.
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.
Five reference molecules namely Lidocaine, Furano-pyrimidine derivative, Imatinib, Atorvastatin and Glipizide have been chosen for the performance evaluation of the algorithm.
Also, the newly designed molecules were analyzed using ZINC, PubChem databases and docking investigations.
本文提出了一种用于从头药物设计的多目标遗传算法（MoGADdrug），旨在设计类似于某些参考分子的新型类药性分子。该算法接受一组从已批准药物中提取并可在片段库中获得的片段，并根据指定规则将它们组合在一起，通过计算机模拟方法发现新药。
为此过程，使用了遗传算法，将片段编码为可变长度染色体的基因，并在整个世代中应用各种遗传操作符。采用加权和方法同时优化新药与参考分子的结构相似性以及其类药性。
选择了五个参考分子，即利多卡因、呋喃嘧啶衍生物、伊马替尼、阿托伐他汀和格列吡嗪，用于算法的性能评估。
此外，还使用 ZINC、PubChem 数据库和对接研究分析了新设计的分子。
