ArticleChemical science2025
PepINVENT: generative peptide design beyond natural amino acids.
Article in Chemical science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 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
16 citing papers in PubMed.
- Artificial Intelligence-Driven Antimicrobial Peptide Discovery: Prediction, Generation, Mining and Optimization.Probiotics and antimicrobial proteins · 2026Review
- Advancing Biochemical Molecule Registration, Representation and Search for New Drug Modalities.Journal of chemical information and modeling · 2026Article
- Artificial intelligence in biologic drug discovery: A review of methodological evolution and therapeutic applications.Acta pharmaceutica Sinica. B · 2026Review
- Lactoferrin-Derived Peptides in Cancer Therapy: Structural Features, Mechanistic Insights and Clinical Translation Prospects.International journal of molecular sciences · 2026Review
- Contemporary data-driven innovations in peptide-based therapeutic design.Briefings in bioinformatics · 2026Review
- Simulation-guided design of peptide-metal coordination interfaces for next-generation metallo-immunotherapy.Nano convergence · 2026Review
- PepFoundry: A Pipeline for Building Machine-Learning Ready Representations of Nonstandard Peptides Containing Cycles, Non-natural Residues, Polymer Units, and More.Journal of chemical information and modeling · 2026Article
- Exploring and expanding the chemical multiverse of peptides.Chemical science · 2026Review
- Peptide Arrays as Tools for Unraveling Tumor Microenvironments and Drug Discovery in Oncology.Cells · 2026Review
- Peptide-based drug design using generative AI.Chemical communications (Cambridge, England) · 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
- Tumor Targeting with Peptide-Drug Conjugates: Showcasing Key Progress and Hurdles.Drug design, development and therapy · 2026Review
- A scalable reinforcement learning approach for screening large peptide libraries for bioactive peptide discovery.Nature communications · 2025Article
- How to build machine learning models able to extrapolate from standard to modified peptides.Journal of cheminformatics · 2025Article
- Article
- Harnessing peptide hormones for postharvest preservation of horticultural produce.Frontiers in plant science · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Peptides play a crucial role in drug design and discovery whether as a therapeutic modality or a delivery agent. Non-natural amino acids (NNAAs) have been used to enhance the peptide properties such as binding affinity, plasma stability and permeability. Incorporating novel NNAAs facilitates the design of more effective peptides with improved properties. The generative models used in the field have focused on navigating the peptide sequence space. The sequence space is formed by combinations of a predefined set of amino acids. However, there is still a need for a tool to explore the peptide landscape beyond this enumerated space to unlock and effectively incorporate the
Identifiers
What OpenQuestion 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 OpenQuestion graph.