SC3: New Multi-Solvent Solubility Benchmark Reveals Significant Gap Between Current Models and Experimental Limits
Researchers have introduced SC3, a new multi-solvent solubility benchmark built on over 101,000 measurements, finding that the best current models perform five times worse than the true noise floor of experimental data. The study argues that prior benchmarks overstated model progress by using flawed curation, misleading metrics, and an inflated estimate of irreducible experimental error. This matters because solubility prediction is critical to drug discovery and materials science, and the findings suggest the field has been measuring itself against an artificially lenient standard.
A preprint posted to arXiv introduces SC3, a rigorously curated multi-solvent solubility benchmark derived from BigSolDB v2.1, encompassing 101,535 measurements across 1,327 solutes and 206 solvents. The authors argue that existing benchmarks have created a misleading picture of model maturity by using inconsistent curation policies, count-weighted RMSE metrics that obscure poor performance on rare solvents, and a widely cited aleatoric (irreducible noise) ceiling of 0.6–0.8 log S that reflects worst-case inter-laboratory disagreement rather than typical expected error. SC3 recalibrates this floor to approximately 0.106 log S—roughly six times tighter—and introduces tiered consensus data (Gold/Silver/Bronze), per-point uncertainty estimates, leakage-checked data splits, and new metrics including PS-RMSE and Z-RMSE. A 31-model benchmark spanning six model families found that even the best-performing model sits at five times the recalibrated aleatoric limit, a gap that no deep learning alternative tested was able to close. Follow-on analyses covering data scaling, transfer from quantum-chemistry solvation energies, and feature attribution suggest that calibrated per-point uncertainty can serve as reusable diagnostic infrastructure beyond simple point prediction. The work positions SC3 as a community resource intended to drive more honest and targeted progress in computational solubility modeling.
What's missing
The paper is a preprint and has not yet undergone peer review, so its recalibrated aleatoric floor estimate and benchmark methodology have not been independently validated.
What different sources said
- arXiv cs.LGCenter
SC3: The Multi-Solvent Solubility Challenge and Benchmark
Related
Gut Bacteria Enzyme Found to Break Down Heat-Processed Food Compounds, Producing Novel Biogenic Amines
Researchers have discovered that an enzyme in common gut bacteria can degrade N-epsilon-carboxymethyllysine (CML), a compound formed during thermal food processing, producing previously unknown biogenic amines. The enzyme, ornithine decarboxylase SpeC from enterobacteria, acts on CML and related modified lysine derivatives through a low-level 'underground' catalytic activity. This finding suggests a previously unrecognized communication axis between thermally processed dietary compounds and gut microbial physiology, with potential implications for host health.
Full-Length Gene Sequencing Reveals Two Distinct Bacterial Communities in Black-Legged Ticks Expanding Into Canada
Researchers used Oxford Nanopore full-length 16S rRNA gene sequencing to characterize the microbiome of Ixodes scapularis black-legged ticks collected in Nova Scotia, Canada, distinguishing between tick-adapted bacteria and environmentally acquired bacteria. The study comes as I. scapularis — the primary vector of Lyme disease — is rapidly expanding northward into Canada due to climate change. The findings suggest that environmentally derived bacteria in tick microbiomes are not mere contamination, which has implications for how tick microbiome data is collected and interpreted across surveillance studies.
Study Identifies Metabolic Link Between Cell Envelope Stress and Biofilm Formation in Bacteria
Researchers have discovered that the metabolite acetyl-CoA directly inhibits enzymes that degrade the bacterial signaling molecule c-di-GMP, connecting cell envelope biosynthesis stress to biofilm formation in Pseudomonas aeruginosa. The study found that sub-inhibitory concentrations of antibiotics targeting early peptidoglycan biosynthesis — but not other antibiotic classes — elevate c-di-GMP levels by reducing phosphodiesterase activity, with acetyl-CoA competing for the enzyme active site. Because the relevant enzyme domain is broadly conserved across bacterial species, this checkpoint mechanism may be widespread and could have implications for understanding antibiotic-induced biofilm responses.