ReviewBriefings in bioinformatics2024
Peptide-based drug discovery through artificial intelligence: towards an autonomous design of therapeutic peptides.
Review in Briefings in bioinformatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 65 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
65 citing papers in PubMed.
- Artificial intelligence-assisted design of self-assembling peptide hydrogels for neural regeneration: Principles and opportunities.Bioactive materials · 2027Review
- Re-engineering insulin for oral delivery: structural modifications, advanced formulation strategies, and future directions.Drug delivery · 2026Review
- AI for bioactive materials: From material design to biological applications.Bioactive materials · 2026Review
- Peptidomics-guided 3D structural-mechanistic investigation of bovine colostrum peptides reveals multi-targetedFood chemistry: X · 2026Article
- Harnessing snake venom cardiotoxins for antimicrobial peptide discovery.npj drug discovery · 2026Article
- GDA-Pred: Generative AI-Driven Data Augmentation for Improved Prediction of IL-6 and IL-13-Inducing Peptides.International journal of molecular sciences · 2026Article
- Design, Synthesis and Biological Evaluation of Novel SN38 Based Albumin-Binding Peptide-Drug Conjugates in Pancreatic Ductal Adenocarcinoma.Pharmaceutics · 2026Article
- Deciphering Allergen Peptides for Dermatological and Cosmetic Applications with Explainable Artificial Intelligence.Journal of proteome research · 2026Article
- GeoPep: A Geometry-Aware Masked Language Model for Protein-Peptide Binding Site Prediction.Journal of chemical information and modeling · 2026Article
- Recent Progress in Artificial Intelligence in Biosensor Development: From Bioprobe Design to Fabrication and Signal Analysis.Biosensors · 2026Review
- Complex Networks in Bioactive Peptide Research: A Methodological Review.Biomolecules · 2026Review
- Generative AI-drivenAntibody therapeutics · 2026Article
- Research Progress on Intelligent Prediction, Debittering Technologies, and Multi-Dimensional Evaluation for Bitter Peptides.Foods (Basel, Switzerland) · 2026Review
- Contemporary data-driven innovations in peptide-based therapeutic design.Briefings in bioinformatics · 2026Review
- Molecular fingerprints are strong models for peptide function prediction.Bioinformatics (Oxford, England) · 2026Article
- AI-Driven Discovery and Design of Antimicrobial Peptides: Progress, Challenges, and Opportunities.Probiotics and antimicrobial proteins · 2026Review
- Design Principles, Synthetic Strategies, and Biomedical Applications of Peptide-Polymer Conjugates.Polymer science & technology (Washington, D.C.) · 2026Review
- Integrative Peptide Drug Development: Chemical Engineering, AI-Driven Design, and Cell-Penetrating Peptides.Pharmaceutics · 2026Review
- Target discovery and drug design in the era of artificial intelligence.Medicinal chemistry research : an international journal for rapid communications on design and mechanisms of action of biologically active agents · 2026Review
- Simulation-guided design of peptide-metal coordination interfaces for next-generation metallo-immunotherapy.Nano convergence · 2026Review
5 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
Abstract
With their diverse biological activities, peptides are promising candidates for therapeutic applications, showing antimicrobial, antitumour and hormonal signalling capabilities. Despite their advantages, therapeutic peptides face challenges such as short half-life, limited oral bioavailability and susceptibility to plasma degradation. The rise of computational tools and artificial intelligence (AI) in peptide research has spurred the development of advanced methodologies and databases that are pivotal in the exploration of these complex macromolecules. This perspective delves into integrating AI in peptide development, encompassing classifier methods, predictive systems and the avant-garde design facilitated by deep-generative models like generative adversarial networks and variational autoencoders. There are still challenges, such as the need for processing optimization and careful validation of predictive models. This work outlines traditional strategies for machine learning model construction and training techniques and proposes a comprehensive AI-assisted peptide design and validation pipeline. The evolving landscape of peptide design using AI is emphasized, showcasing the practicality of these methods in expediting the development and discovery of novel peptides within the context of peptide-based drug discovery.
Indexed as
Identifiers
What Socratic holds
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 Socratic graph.