Optimized Drug Design using Multi-Objective Evolutionary Algorithms with SELFIES
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2024
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866914778898759680 |
|---|---|
| author | Hömberg, Tomoya Mostaghim, Sanaz Hiwa, Satoru Hiroyasu, Tomoyuki |
| author_facet | Hömberg, Tomoya Mostaghim, Sanaz Hiwa, Satoru Hiroyasu, Tomoyuki |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_00401 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Optimized Drug Design using Multi-Objective Evolutionary Algorithms with SELFIES Hömberg, Tomoya Mostaghim, Sanaz Hiwa, Satoru Hiroyasu, Tomoyuki Neural and Evolutionary Computing 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. |
| title | Optimized Drug Design using Multi-Objective Evolutionary Algorithms with SELFIES |
| topic | Neural and Evolutionary Computing |
| url | https://arxiv.org/abs/2405.00401 |