ArticlePeerJ2019
TOXIFY: a deep learning approach to classify animal venom proteins.
Article in PeerJ, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed.
- NToxSEM: Enhancing prediction of neurotoxic peptides and neurotoxins using a stacked ensemble-based multimodal framework.Protein science : a publication of the Protein Society · 2026Article
- Venom variation and the future of antivenom design: Integrating population venomics, evolutionary toxinology, and precision therapeutics.Toxicon: X · 2026Review
- Quantitative analysis of growth and diversification in venom data using database metrics.Database : the journal of biological databases and curation · 2026Article
- Lab to codes: multimodel advanced AI-driven pipelines to drive antimicrobial peptide design for the post-antibiotic era.Frontiers in cellular and infection microbiology · 2026Review
- ProToxin, a Predictor of Protein Toxicity.Toxins · 2025Article
- Exo-Tox: Identifying Exotoxins from secreted bacterial proteins.BioData mining · 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
- A large language model for predicting neurotoxic peptides and neurotoxins.Protein science : a publication of the Protein Society · 2025Article
- Review
- ToxDL 2.0: Protein toxicity prediction using a pretrained language model and graph neural networks.Computational and structural biotechnology journal · 2025Article
- Article
- Structure-aware deep learning model for peptide toxicity prediction.Protein science : a publication of the Protein Society · 2024Article
- VISH-Pred: an ensemble of fine-tuned ESM models for protein toxicity prediction.Briefings in bioinformatics · 2024Article
- MultiToxPred 1.0: a novel comprehensive tool for predicting 27 classes of protein toxins using an ensemble machine learning approach.BMC bioinformatics · 2024Article
- DeTox: a pipeline for the detection of toxins in venomous organisms.Briefings in bioinformatics · 2024Article
- Article
- Article
- CSM-Toxin: A Web-Server for Predicting Protein Toxicity.Pharmaceutics · 2023Article
- Omics Data and Data Representations for Deep Learning-Based Predictive Modeling.International journal of molecular sciences · 2022Review
- Reliability of the In Silico Prediction Approach to In Vitro Evaluation of Bacterial Toxicity.Sensors (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In the era of Next-Generation Sequencing and shotgun proteomics, the sequences of animal toxigenic proteins are being generated at rates exceeding the pace of traditional means for empirical toxicity verification. To facilitate the automation of toxin identification from protein sequences, we trained Recurrent Neural Networks with Gated Recurrent Units on publicly available datasets. The resulting models are available via the novel software package TOXIFY, allowing users to infer the probability of a given protein sequence being a venom protein. TOXIFY is more than 20X faster and uses over an order of magnitude less memory than previously published methods. Additionally, TOXIFY is more accurate, precise, and sensitive at classifying venom proteins.
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Registered trials
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.