ArticleJournal of chemical information and modeling2026
PyRMD Studio: A Unified Suite for Next-Generation, AI-Powered Virtual Screening.
Article in Journal of chemical information and modeling, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- AI-Enforced Ultra-Large Virtual Screening Discovers Potent CD28 Binders.Journal of chemical information and modeling · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial Intelligence (AI) has become a cornerstone of modern drug discovery. Yet, its widespread adoption is often hindered by the steep learning curve of command-line tools and the prevalence of overfitting due to improper data splitting. Here, we introduce PyRMD Studio, a major update to our open-source virtual screening (VS) software, designed to address these challenges through enhanced accessibility, scientific rigor, and computational efficiency. This new version features a comprehensive graphical user interface (GUI) compatible with both Linux and Windows, democratizing access to sophisticated AI workflows and allowing nonexpert users to perform both ligand-based and structure-based VS without coding expertise. To ensure reliable performance estimation, PyRMD Studio implements a rigorous validation strategy based on Butina clustering, which separates structural clusters between training and test sets to mitigate data leakage and prevent overoptimistic benchmarking. Furthermore, extensive code optimizations and vectorization have resulted in an approximate 3.3-fold increase in screening speed compared to the previous release. By combining a user-friendly environment with robust validation protocols, PyRMD Studio provides a streamlined and reliable toolkit for high-throughput virtual screening campaigns. PyRMD Studio is freely available at https://github.com/sandrocosconati/PyRMD-Studio.
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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.