MolSearch: Search-based Multi-objective Molecular Generation and Property Optimization

Leveraging computational methods to generate small molecules with desired properties has been an active research area in the drug discovery field. Towards real-world applications, however, efficient generation of molecules that satisfy property requirements simultaneously remains a key challenge. In...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Proceedings / International Conference on Knowledge Discovery and Data Mining Ročník 2022; s. 4724
Hlavní autoři: Sun, Mengying, Wang, Huijun, Xing, Jing, Chen, Bin, Meng, Han, Zhou, Jiayu
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States 01.08.2022
Témata:
Molecular Generation and Optimization
Design Moves
Computing methodologies → Machine learning algorithms
Monte Carlo Tree Search
Applied computing → Bioinformatics
ISSN:2154-817X
On-line přístup:Zjistit podrobnosti o přístupu
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:Leveraging computational methods to generate small molecules with desired properties has been an active research area in the drug discovery field. Towards real-world applications, however, efficient generation of molecules that satisfy property requirements simultaneously remains a key challenge. In this paper, we tackle this challenge using a search-based approach and propose a simple yet effective framework called MolSearch for multi-objective molecular generation (optimization). We show that given proper design and sufficient information, search-based methods can achieve performance comparable or even better than deep learning methods while being computationally efficient. Such efficiency enables massive exploration of chemical space given constrained computational resources. In particular, MolSearch starts with existing molecules and uses a two-stage search strategy to gradually modify them into new ones, based on transformation rules derived systematically and exhaustively from large compound libraries. We evaluate MolSearch in multiple benchmark generation settings and demonstrate its effectiveness and efficiency.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:2154-817X
DOI:10.1145/3534678.3542676