arXiv:2605.24742v1 Announce Type: new Abstract: Obtaining consistent explanations for machine learning on molecular graphs requires predictions and attributions to be aligned with chemical identity. However, chemically equivalent drawings of the same molecule can induce different molecular representations, leading to inconsistent predictions and explanations. Here, we introduce InChIfied Invariants, a class of node, edge, and graph features based on the International Chemical Identifier (InChI) and designed to be invariant under transformations that preserve chemical identity. Using one millio
The increasing reliance on machine learning in molecular science necessitates more robust and reliable methods for interpretation, making advancements in consistent explanations critical.
This development allows for more accurate and consistent AI-driven drug discovery, material science, and chemical engineering by ensuring ML models understand molecules correctly.
The introduction of InChIfied Invariants provides a standardized, chemically-aware approach to molecular graph representations, overcoming inconsistencies that plague current ML explanations.
- · Pharmaceutical companies
- · Material science R&D
- · AI in chemistry platforms
- · Chemical engineers
- · Companies relying on inconsistent or biased chemical ML models
- · Traditional, slower chemical analysis methods
More efficient and reliable machine learning models for molecular design and property prediction will emerge.
Accelerated discovery of new drugs, catalysts, and advanced materials will drive innovation in related industries.
The reduced cost and increased speed of molecular R&D could democratize access to advanced chemical development capabilities.
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Read at arXiv cs.LG