Advancing Peptide-Based Cancer Therapy with AI: In-Depth Analysis of State-of-the-Art AI Models.
J Chem Inf Model
; 64(13): 4941-4957, 2024 Jul 08.
Article
em En
| MEDLINE
| ID: mdl-38874445
ABSTRACT
Anticancer peptides (ACPs) play a vital role in selectively targeting and eliminating cancer cells. Evaluating and comparing predictions from various machine learning (ML) and deep learning (DL) techniques is challenging but crucial for anticancer drug research. We conducted a comprehensive analysis of 15 ML and 10 DL models, including the models released after 2022, and found that support vector machines (SVMs) with feature combination and selection significantly enhance overall performance. DL models, especially convolutional neural networks (CNNs) with light gradient boosting machine (LGBM) based feature selection approaches, demonstrate improved characterization. Assessment using a new test data set (ACP10) identifies ACPred, MLACP 2.0, AI4ACP, mACPred, and AntiCP2.0_AAC as successive optimal predictors, showcasing robust performance. Our review underscores current prediction tool limitations and advocates for an omnidirectional ACP prediction framework to propel ongoing research.
Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Peptídeos
/
Neoplasias
/
Antineoplásicos
Limite:
Humans
Idioma:
En
Revista:
J Chem Inf Model
/
J. chem. inf. model
/
Journal of chemical information and modeling
Assunto da revista:
INFORMATICA MEDICA
/
QUIMICA
Ano de publicação:
2024
Tipo de documento:
Article
País de afiliação:
Coréia do Sul
País de publicação:
Estados Unidos