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.

背景：本文提出了一种用于从头药物设计（MoGADdrug）的多目标遗传算法，用于设计与某些参考分子相似的新型药物样分子。开发的算法接受一组从批准的药物中提取并在片段库中可用的片段，并根据指定的规则将它们组合起来，通过 in-silico 方法发现新药物。

方法：在这个过程中，使用了遗传算法，将片段编码为可变长度染色体的基因，并在各代中应用各种遗传算子。加权总和方法用于同时优化新药与参考分子的结构相似性及其药物相似性。

结果：选择了五种参考分子，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。

结论：此外，使用 ZINC、PubChem 数据库和对接研究分析了新设计的分子。