LGBM-ACp: an ensemble model for anticancer peptide prediction and in silico screening with potential drug targets

Garai, Swarnava; Thomas, Juanit; Dey, Palash; Das, Dhrubajyoti

doi:10.1007/s11030-023-10602-0

Cited by 2 publications

(2 citation statements)

References 52 publications

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“…Their model utilized two composition features and two binary features for predicting anticancer peptides (ACPs). Validation on a distinct data set of nonmutagenic, nontoxic, and noninhibitory P-459 peptides showcased the robust performance . Molecular docking of predicted peptides demonstrated strong binding affinity with 25 anticancer drug targets, notably phosphoenolpyruvate carboxykinase.…”

Section: Ai Models For Acpsmentioning

confidence: 94%

“…The proposed model has the potential to revolutionize the identification of ACPs in healthcare and cancer treatment. 102 In 2024, Garai et al introduced a machine learning framework employing light gradient boosting called LGBM-ACp as a classifier. Their model utilized two composition features and two binary features for predicting anticancer peptides (ACPs).…”

Section: T H Imentioning

confidence: 99%

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Advancing Peptide-Based Cancer Therapy with AI: In-Depth Analysis of State-of-the-Art AI Models

Bhattarai,

Tayara,

Chong

2024

J. Chem. Inf. Model.

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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.

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Section: Ai Models For Acpsmentioning

confidence: 94%

Section: T H Imentioning

confidence: 99%