Perturbation-Theory Machine Learning for Multi-Target Drug Discovery in Modern Anticancer Research DOI Creative Commons
Valeria V. Kleandrova, M. Natália D. S. Cordeiro, Alejandro Speck‐Planche

et al.

Current Issues in Molecular Biology, Journal Year: 2025, Volume and Issue: 47(5), P. 301 - 301

Published: April 25, 2025

Cancers constitute a group of biological complex diseases, which are associated with great prevalence and mortality. These medical conditions very difficult to tackle due their multi-factorial nature, includes ability evade the immune system become resistant current anticancer agents. There is pressing need search for novel agents multi-target modes action and/or multi-cell inhibition versatility, can translate into more efficacious safer chemotherapeutic treatments. Computational methods paramount importance accelerate drug discovery in cancer research but most them have several disadvantages such as use limited structural information through homogeneous datasets chemicals, prediction activity against single target, lack interpretability. This mini-review discusses emergence, development, application perturbation-theory machine learning (PTML) cutting-edge approach capable overcoming aforementioned limitations context small molecule discovery. Here, we analyze promising investigations on PTML modeling spanning over decade enable versatile We highlight potential while envisaging future applications modeling.

Language: Английский

Perturbation-Theory Machine Learning for Multi-Target Drug Discovery in Modern Anticancer Research DOI Creative Commons
Valeria V. Kleandrova, M. Natália D. S. Cordeiro, Alejandro Speck‐Planche

et al.

Current Issues in Molecular Biology, Journal Year: 2025, Volume and Issue: 47(5), P. 301 - 301

Published: April 25, 2025

Cancers constitute a group of biological complex diseases, which are associated with great prevalence and mortality. These medical conditions very difficult to tackle due their multi-factorial nature, includes ability evade the immune system become resistant current anticancer agents. There is pressing need search for novel agents multi-target modes action and/or multi-cell inhibition versatility, can translate into more efficacious safer chemotherapeutic treatments. Computational methods paramount importance accelerate drug discovery in cancer research but most them have several disadvantages such as use limited structural information through homogeneous datasets chemicals, prediction activity against single target, lack interpretability. This mini-review discusses emergence, development, application perturbation-theory machine learning (PTML) cutting-edge approach capable overcoming aforementioned limitations context small molecule discovery. Here, we analyze promising investigations on PTML modeling spanning over decade enable versatile We highlight potential while envisaging future applications modeling.

Language: Английский

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