ArticleComputational and structural biotechnology journal2022
MLACP 2.0: An updated machine learning tool for anticancer peptide prediction.
Article in Computational and structural biotechnology journal, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers.
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Who cites it
39 citing papers in PubMed, 75 citations in OpenAlex.
- Mechanism-Informed Machine Learning Enables Discovery of Oncolytic Peptides for Cancer Immunotherapy.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Venom-Derived Enzyme Inhibitors as Anticancer Agents: Structure-Activity Relationships, Molecular Targets and Mechanistic Insights.Molecules (Basel, Switzerland) · 2026Review
- DeepACPred: an integrated multistage framework for anticancer peptide discovery and activity prediction.Briefings in bioinformatics · 2026Article
- Design and classify innovative antimicrobial and dual- and multi-functional peptides using generative artificial intelligence.Scientific reports · 2026Article
- Enhancing anticancer peptide discovery: A fusion-centric framework with conditional diffusion for prediction and generation.PLoS computational biology · 2026Article
- Deep capsule neural network for identifying anticancer peptides using sequence to image transformation-based local embedded features.BMC biology · 2026Article
- Aegis: a transformer-based deep learning framework for the accurate identification of anticancer peptides.BMC biology · 2026Article
- ANIA: an inception-attention network for predicting minimum inhibitory concentration of antimicrobial peptides.Briefings in bioinformatics · 2026Article
- Animal Venoms as Peptide Libraries for the Discovery of Antiglioblastoma Agents.Biochemistry research international · 2026Review
- ProtT5-MSCRNet: a multi-scale convolutional and channel-recalibrated deep learning framework for anticancer peptide prediction.Frontiers in artificial intelligence · 2026Article
- Comparative analysis of tissue-specific anticancer peptide prediction models: ACP-Boost framework.Frontiers in molecular biosciences · 2026Article
- High-Value Transformation of Rice-Derived Bioactive Peptides for Multiscenario Applications: From AI-Aided Design to Molecular Modification.International journal of food science · 2026Review
- iALP: Identification of Allergenic Proteins Based on Large Language Model and Gate Linear Unit.Interdisciplinary sciences, computational life sciences · 2025Article
- Artificial intelligence-driven anticancer peptide discovery.iMetaOmics · 2025Review
- AttBiLSTM_DE: enhancing anticancer peptide prediction using word embedding and an optimized attention-based BiLSTM framework.Scientific reports · 2025Article
- A scalable reinforcement learning approach for screening large peptide libraries for bioactive peptide discovery.Nature communications · 2025Article
- xBitterT5: an explainable transformer-based framework with multimodal inputs for identifying bitter-taste peptides.Journal of cheminformatics · 2025Article
- HyPepTox-Fuse: An interpretable hybrid framework for accurate peptide toxicity prediction fusing protein language model-based embeddings with conventional descriptors.Journal of pharmaceutical analysis · 2025Article
- CancerPPD2: an updated repository of anticancer peptides and proteins.Database : the journal of biological databases and curation · 2025Article
- iMFP-LG: Identify Novel Multi-functional Peptides Using Protein Language Models and Graph-based Deep Learning.Genomics, proteomics & bioinformatics · 2025Article
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Anticancer peptides are emerging anticancer drug that offers fewer side effects and is more effective than chemotherapy and targeted therapy. Predicting anticancer peptides from sequence information is one of the most challenging tasks in immunoinformatics. In the past ten years, machine learning-based approaches have been proposed for identifying ACP activity from peptide sequences. These methods include our previous method MLACP (developed in 2017) which made a significant impact on anticancer research. MLACP tool has been widely used by the research community, however, its robustness must be improved significantly for its continued practical application. In this study, the first large non-redundant training and independent datasets were constructed for ACP research. Using the training dataset, the study explored a wide range of feature encodings and developed their respective models using seven different conventional classifiers. Subsequently, a subset of encoding-based models was selected for each classifier based on their performance, whose predicted scores were concatenated and trained through a convolutional neural network (CNN), whose corresponding predictor is named MLACP 2.0. The evaluation of MLACP 2.0 with a very diverse independent dataset showed excellent performance and significantly outperformed the recent ACP prediction tools. Additionally, MLACP 2.0 exhibits superior performance during cross-validation and independent assessment when compared to CNN-based embedding models and conventional single models. Consequently, we anticipate that our proposed MLACP 2.0 will facilitate the design of hypothesis-driven experiments by making it easier to discover novel ACPs. The MLACP 2.0 is freely available at https://balalab-skku.org/mlacp2.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.