Computer aided drug design is a promising approach to reduce the tremendous costs, i.e. time and resources, for developing new medicinal drugs. It finds application in aiding the traversal of the vast chemical space of potentially useful compounds. In this paper, we deploy multi-objective evolutionary algorithms, namely NSGA-II, NSGA-III, and MOEA/D, for this purpose. At the same time, we used the SELFIES string representation method. In addition to the QED and SA score, we optimize compounds using the GuacaMol benchmark multi-objective task sets. Our results indicate that all three algorithms show converging behavior and successfully optimize the defined criteria whilst differing mainly in the number of potential solutions found. We observe that novel and promising candidates for synthesis are discovered among obtained compounds in the Pareto-sets.

翻译：计算机辅助药物设计是降低新药开发巨大成本（即时间和资源）的一种有前景的方法。它有助于遍历可能具有实用价值的化合物的广阔化学空间。本文为此部署了多目标进化算法，即NSGA-II、NSGA-III和MOEA/D，同时采用SELFIES字符串表示方法。除QED和SA评分外，我们利用GuacaMol基准多目标任务集对化合物进行优化。结果表明，三种算法均呈现收敛行为，成功优化了既定标准，主要差异体现在找到的潜在解数量上。我们观察到，在获得的帕累托前沿化合物中发现了新颖且有前景的合成候选分子。