ReviewChemical communications (Cambridge, England)2026
Peptide-based drug design using generative AI.
Review in Chemical communications (Cambridge, England), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 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
13 citing papers in PubMed.
- Peptide Therapeutics for Solid Tumors: Functional Classes, AI-Enhanced Discovery and Clinical Advances.Journal of peptide science : an official publication of the European Peptide Society · 2026Review
- Leveraging Advanced AI Frameworks for Dual PPAR α/γ Agonist Discovery in Alzheimer's Disease.ACS chemical neuroscience · 2026Review
- Cyclic Peptides as Modulators of Protein-Protein Interactions: A Survival Guide from Discovery Platforms to AI-Driven Design.International journal of molecular sciences · 2026Review
- A Comprehensive Review of Antimicrobial Peptides and Smart Biomaterials in Chronic Wound Therapy: Overcoming Biofilms, Resistance, and Translational Barriers.International journal of molecular sciences · 2026Review
- Functional Engineering of Bioactive Peptides: Chemical Modifications and Synthetic Biology Approaches.International journal of molecular sciences · 2026Review
- Targeting the Undruggable: Deep Learning-Driven Design of Peptide Therapeutics in Cancer.Pharmaceuticals (Basel, Switzerland) · 2026Review
- Contemporary data-driven innovations in peptide-based therapeutic design.Briefings in bioinformatics · 2026Review
- Integrative Peptide Drug Development: Chemical Engineering, AI-Driven Design, and Cell-Penetrating Peptides.Pharmaceutics · 2026Review
- Peptide Arrays as Tools for Unraveling Tumor Microenvironments and Drug Discovery in Oncology.Cells · 2026Review
- Advances in antimicrobial peptides: promising cancer treatments and vaccines.Frontiers in medicine · 2026Review
- Peptide-Based Therapeutics for Alzheimer's Disease: Medicinal Chemistry, AI-Guided Computational Design, and Blood-Brain Barrier Delivery.Drug design, development and therapy · 2026Review
- Recent advances in multimodal foundation model-enabled peptide screening and optimization for smart biomaterials and functional tissue engineering.Frontiers in bioengineering and biotechnology · 2026Review
- Artificial Intelligence for Osteoporosis Diagnosis, Risk Prediction and Therapy: Current Advances, Clinical Challenges, and Future Perspectives.Clinical interventions in aging · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
Peptide-based therapeutics have emerged as a significant treatment strategy, offering high specificity and tunable pharmacokinetics. Recent advances in Artificial Intelligence (AI) have shifted the focus towards structure prediction, generative design, and interaction modelling, significantly accelerating drug design and discovery. Deep learning architectures, including graph neural networks, transformers, and diffusion models, have facilitated the generation of novel sequences for the target of interest, although predicting the solubility, immunogenicity, and toxicity of these sequences remains a challenge. Innovations in peptide chemistry, such as cyclization, stapling, non-canonical amino acids, and nanoparticle formulations, help overcome the hurdles of bioavailability and permeation. These chemical approaches, combined with developments in autonomous peptide synthesis and high-throughput screening, have considerably reduced discovery timelines from years to months. Clinically, this progress is apparent in the growing number of approved peptide drugs for metabolic disorders, oncology, and medical imaging. Here, we review recent progress in peptide-based drug design using AI, focusing on generative architectures and interactions. We then examine AI-driven screening and delivery optimization for these peptide-based discoveries. Finally, we discuss the current limitations, practical challenges, and future direction with particular emphasis on data quality and autonomous 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.