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Cell-penetrating peptides


AntAngioCOOL

Tool
Anti-angiogenic peptides Machine learning R package Feature selection Physicochemical features

AntAngioCOOL is an R-based machine learning package for predicting anti-angiogenic peptides (AAPs), enabling efficient identification via robust feature engineering and model selection. Built on a non-redundant dataset of 135 AAPs (positives) and 135 non-AAPs (negatives), the tool extracts over 17,000 features and selects 2000+ informative ones to train three models (high sensitivity, specificity, accuracy).

AntiAngioPred

Webserver
Anti-angiogenic peptides Machine learning Residue preference Motif analysis Tumor therapy

AntiAngioPred is a webserver for predicting anti-angiogenic peptides, developed based on literature-collected peptides with systematic analysis of residue preferences and positional features: Cysteine (Cys), Proline (Pro) etc., are enriched, with N-terminal bias for Ser/Pro/Trp/Cys and C-terminal enrichment of Cys/Gly/Arg. Prominent motifs like "CG-G", "TC" are identified. 

AntiCP 2.0

Webserver
Anticancer peptides Machine learning Motif analysis Webserver

AntiCP 2.0 is a computational model for predicting and designing anticancer peptides (ACPs), analyzing residue composition (preferring A, F, K, L, W) and positional preferences (N-terminal favoring A, F, K; C-terminal favoring L, K), combined with motifs like LAKLA and AKLAK.

AntiDMPpred

Webserver
Antidiabetic peptides Random Forest Feature selection Oral drugs Bioinformatics

AntiDMPpred is a Random Forest-based webserver for predicting antidiabetic peptides (ADPs), specifically designed for oral peptide drug development. The model constructs feature spaces by integrating four sequence descriptors (amino acid composition, physicochemical properties, dipeptide composition, autocorrelation functions), and employs a cross-evaluation strategy of feature scoring and machine learning algorithms to select non-redundant features.

AntiFlamPred

Tool
Anti-inflammatory peptides Deep learning Feature extraction Machine learning Bootstrap aggregation

AntiFlamPred is a high-precision predictor for anti-inflammatory peptides (AIPs), constructed by integrating sequence encoding features and deep learning algorithms, optimized via techniques like bootstrap aggregation.