arXiv:2606.07656v1 Announce Type: cross Abstract: Solubility prediction is a standard benchmark in computational chemistry, yet multi-solvent models which reportedly approach the experimental-noise ceiling (i.e. the aleatoric limit) are not yet reliable enough to be deployed. We argue that this gap is partly artefactual: published benchmarks differ in curation policies, evaluate on count-weighted RMSE that hides failure on tail-heavy solvent distributions, and treat the widely cited 0.6-0.8 log S inter-laboratory figure as the aleatoric ceiling even though it reflects worst-case, not expected,
The publication of SC3 at this time addresses the unreliability of current multi-solvent solubility models and proposes a new benchmark to improve their accuracy and deployability in computational chemistry.
This benchmark provides critical improvements for drug discovery and materials science by enhancing the accuracy of solubility predictions, a fundamental requirement for molecular design and development.
The proposed SC3 benchmark will standardize the evaluation of solubility prediction models, leading to more robust and reliable computational chemistry tools.
- · Computational Chemists
- · Pharmaceutical Industry
- · Materials Science
- · AI/ML researchers in chemistry
- · Developers of unreliable multi-solvent models
- · Research groups using flawed benchmarks
More precise solubility predictions will accelerate the design and development cycle for new drugs and specialized materials.
Improved predictive models could significantly reduce the costs and time associated with experimental validation in chemical research.
The enhanced efficiency in molecular design may lead to the discovery of novel compounds with transformative applications in various industries.
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Read at arXiv cs.LG