Anticancer Protein or Peptide Tools

This page provides a comprehensive list of computational tools and databases developed for the analysis and prediction of anticancer properties of peptides and proteins. These tools utilize machine learning models and bioinformatics techniques to support research in peptide-based cancer therapy and drug design.

Name	Year	Description	PubMed ID
AntiCP2	2021	In silico model developed for predicting and designing anticancer peptides (ACPs) using an ETree Classifier.	32770192
Z.Hajisharifi et al.	2014	Predicts anticancer peptides using classifiers based on Chou's pseudo-amino acid composition (PseAAC) and local alignment kernel.	24035842
ACPP	2014	Computational method using support vector machine and protein relatedness measure feature vector to predict ACPs.	Link
iACP	2016	Sequence-based predictor developed using g-gap dipeptide components to identify anticancer peptides.	26942877
Feng-Min Li et al.	2016	Predicts anticancer peptides using amino acid composition (AAC), average chemical shifts (acACS), and reduced amino acid composition (RAAC).	27670968
F. Khan et al.	2017	Utilizes peptide sequence descriptors such as Split amino acid composition, dipeptide composition, and Pseudo amino acid composition for anticancer prediction.	Link
iACP-GAEnsC	2017	Uses amphiphilic Pseudo amino acid composition, g-gap dipeptide composition, and Reduced amino acid alphabet composition for ACP prediction.	28655440
Lei Xu et al.	2017	Predicts ACPs using 400D features, incorporating g-gap dipeptide features and feature pruning via maximum relevance-maximum distance method.	29534013
ACPred	2017	Predicts ACPs using machine learning models (support vector machine and random forest) and various peptide feature classes.	31121946
MLACP	2017	Developed using support vector machine- and random forest-based machine-learning methods for predicting ACPs. Features include amino acid composition, dipeptide composition, atomic composition, and physicochemical properties.	29100375
ACPred‐FL67	2018	Employs a feature representation learning model that extracts and selects informative features from sequence-based descriptors, using a two-step feature selection technique for effective prediction of ACPs.	29868903
mACPpred	2019	Combines sequence-based features and probabilistic feature vectors using a two-step protocol and applies support vector machines to develop an effective ACP prediction model.	31013619
ACP-DL	2019	Integrates binary profile features and k-mer sparse matrix of the reduced amino acid alphabet. Uses a deep LSTM model to identify anticancer peptides.	31173946
ACPred-FUSE	2019	Explores class and probabilistic information embedded in ACPs using a feature representation learning model and fuses multiview features for robust prediction.	31729528
MLACP2	2022	Utilizes a wide range of feature encodings and develops models using multiple classifiers. Combines predicted scores using a convolutional neural network (CNN).	36051870
mACPpred2	2024	Incorporates conventional feature descriptors and NLP-based embeddings. Uses a stacked deep learning approach with 1D CNN blocks and hybrid features for ACP prediction.	39237191
CpACpP	2021	Applies RF, SVM, and XGBoost algorithms to predict active Cp-ACPs using an experimentally validated dataset. Combines cell-penetrating peptide (CPP) and ACP subpredictors.	34368571
LGBM-ACp	2024	Uses the Light Gradient Boosting Machine classifier with compositional and binary profile features to predict ACPs.	36637711
A-CaMP	2020	Facilitates rapid fingerprinting of anticancer and antimicrobial peptides.	31870207
CancerGram	2020	Utilizes n-grams and random forests for accurate prediction of anticancer peptides.	33142753
iACP-FSCM	2021	Employs a flexible scoring card method (FSCM) with global and local sequence information for ACP prediction.	33542286
ENNAACT	2021	A deep neural network classifier for sequence-based anticancer peptide activity prediction.	33254015
iACP-DRLF	2021	Combines Light Gradient Boosting Machine and deep learning-based sequence embeddings to enhance ACP prediction.	33529337
ACHP	2021	Describes peptide drugs using amino acid interaction networks and structural features with SVM, KNN, and RF classifiers.	Link
EnACP	2020	Identifies ACPs using ensemble learning and multidimensional feature representations such as sequence composition and physicochemical properties.	32903636
StackACPred	2022	Integrates sequence-based features with SVM-RFE and CBR algorithms to build a stacking-based ensemble model for ACP prediction.	[Link]
iDACP	2021	Uses a two-step machine learning approach with hybrid features to classify ACP subtypes and improve prediction accuracy.	34193950
ACPred-FL	2018	Anti-Cancer peptide Predictor with Feature representation Learning (ACPred-FL) for accurate prediction of ACPs based on sequence information.	29868903
AACFlow	2024	An end-to-end model based on attention-augmented convolutional neural networks and flow-attention mechanism for identification of anticancer peptides.	38452348
AntiMF	2022	A deep learning framework for predicting anticancer peptides based on multi-view feature extraction.	36100141
ACPs-ASSF	2023	An Augmented Sample Selection Framework for Prediction of Anticancer Peptides.	37764455
ACP-PDAFF	2024	Pretrained model and dual-channel attentional feature fusion for anticancer peptides prediction.	38996756
ME-ACP	2022	Multi-view neural networks with ensemble model for identification of anticancer peptides.	35358753
ACP-MLC	2023	A two-level prediction engine for identification of anticancer peptides and multi-label classification of their functional types.	37058760
PLMACPred	2024	Prediction of anticancer peptides based on protein language model and wavelet denoising transformation.	39043738
ACP-BC	2023	A model for accurate identification of anticancer peptides based on fusion features of bidirectional long short-term memory and chemically derived information.	37895128
xDeep-AcPEP	2021	Deep Learning Method for Anticancer Peptide Activity Prediction Based on Convolutional Neural Network and Multitask Learning.	34327990
ACP-check	2022	An anticancer peptide prediction model based on bidirectional long short-term memory and multi-features fusion strategy.	35868046
ACPPfel	2024	Explainable deep ensemble learning for anticancer peptides prediction based on feature optimization.	38487252
MA-PEP	2024	A novel anticancer peptide prediction framework with multimodal feature fusion based on attention mechanism.	38532681
Li & Wang's et al.	2020	Prediction of Anticancer Peptides Using a Low-Dimensional Feature Model.	32903381
PEPred-Suite	2019	Improved and robust prediction of therapeutic peptides using adaptive feature representation learning.	30994882
PTPD	2019	Predicting therapeutic peptides by deep learning and word2vec.	31492094
TargetACP	2018	Intelligent computational method for discrimination of anticancer peptides by incorporating sequential and evolutionary profiles information.	Link
ACP-ESM2	2025	Enhancing Anticancer Peptide Prediction with Pre-trained Protein Language Models.	Link
iACP-SEI	2025	An Anticancer Peptide Identification Method Incorporating Sequence Evolutionary Information.	Link
ACP-CLB	2025	An Anticancer Peptide Prediction Model Based on Multichannel Discriminative Processing and Integration of Large Pretrained Protein Language Models.	39969847