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New methods for drug synergy prediction: A mini-review.
Abbasi, Fatemeh; Rousu, Juho.
Affiliation
  • Abbasi F; Laboratory of Bioinformatics and Drug Design (LBD), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran.
  • Rousu J; Department of Computer Science, Aalto University, Espoo, Finland. Electronic address: juho.rousu@aalto.fi.
Curr Opin Struct Biol ; 86: 102827, 2024 06.
Article in En | MEDLINE | ID: mdl-38705070
ABSTRACT
In this mini-review, we explore the new prediction methods for drug combination synergy relying on high-throughput combinatorial screens. The fast progress of the field is witnessed in the more than thirty original machine learning methods published since 2021, a clear majority of them based on deep learning techniques. We aim to put these articles under a unifying lens by highlighting the core technologies, the data sources, the input data types and synergy scores used in the methods, as well as the prediction scenarios and evaluation protocols that the articles deal with. Our finding is that the best methods accurately solve the synergy prediction scenarios involving known drugs or cell lines while the scenarios involving new drugs or cell lines still fall short of an accurate prediction level.
Subject(s)

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Drug Synergism Limits: Humans Language: En Journal: Curr Opin Struct Biol Journal subject: BIOLOGIA MOLECULAR Year: 2024 Document type: Article Affiliation country: Iran

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Drug Synergism Limits: Humans Language: En Journal: Curr Opin Struct Biol Journal subject: BIOLOGIA MOLECULAR Year: 2024 Document type: Article Affiliation country: Iran