ArticleDigital discovery2024
ProtAgents: protein discovery
Article in Digital discovery, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 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
30 citing papers in PubMed.
- AI for Accelerated Materials Discovery: From Generative Design to Autonomous Realization.Chemical reviews · 2026Article
- Artificial intelligence for food innovation.Nature food · 2026Review
- Benchmarking and behavioral characterization of LLM agents for protein design.bioRxiv : the preprint server for biology · 2026Article
- MutexaGPT: An Intuition-to-Design Translator for Physics-based Enzyme Engineering.Research square · 2026Article
- Review
- Agentic AI and the rise of in silico team science in biomedical research.Nature biotechnology · 2026Review
- Controllable Protein Design by Prefix-Tuning Protein Language Models.Journal of chemical information and modeling · 2026Article
- Sustainable Materials Design With Multi-Modal Artificial Intelligence.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Automatic Generation of a Mechanical Properties Question-Answering Data Set for Language Model Benchmarking: A Comparative Study of BERT, XLNet, and LLaMA Models.Journal of chemical information and modeling · 2026Article
- MDCrow: automating molecular dynamics workflows with large language models.Machine learning: science and technology · 2026Article
- Large language model agents for biological intelligence across genomics, proteomics, spatial biology, and biomedicine.Briefings in bioinformatics · 2026Review
- Peptide-based drug design using generative AI.Chemical communications (Cambridge, England) · 2026Review
- LLM-driven design of physics-constrained constitutive models: Two agents are better than one.Machine learning for computational science and engineering · 2026Article
- A Process-Centric Survey of AI for Scientific Discovery Through the EXHYTE Framework.Research square · 2025Article
- LIDDIA: Language-based Intelligent Drug Discovery Agent.Proceedings of the Conference on Empirical Methods in Natural Language Processing. Conference on Empirical Methods in Natural Language Processing · 2025Article
- The rise and potential opportunities of large language model agents in bioinformatics and biomedicine.Briefings in bioinformatics · 2025Review
- Large language models for drug discovery and development.Patterns (New York, N.Y.) · 2025Review
- Article
- Risks of AI scientists: prioritizing safeguarding over autonomy.Nature communications · 2025Review
- Streamline automated biomedical discoveries with agentic bioinformatics.Briefings in bioinformatics · 2025Review
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
Designing
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.