ArticleFrontiers in medicine2026
Physics-consistent equation discovery for drug solubility in supercritical CO₂ through symbolic regression and domain generalization.
Article in Frontiers in medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Predicting drug solubility in supercritical CO₂ is challenging due to limited data, and strong inter-compound variability conditions that hinder generalization to unseen drugs. Although conventional machine learning models often achieve high accuracy, they rely on spurious correlations and lack interpretability, limiting practical use. This study proposes a unified framework combining symbolic regression with domain generalization techniques, including Invariant Risk Minimization (IRM) and Domain-Adversarial Neural Networks (DANN), to enable equation discovery. The framework enables the discovery of invariant, physically consistent equations that generalize beyond the training distribution. Symbolic regression extracts closed-form relationships, while IRM and DANN enforce invariance across domains within a multi-objective optimization setting. The optimized models achieved validation R
Indexed as
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.