Title: Learning Molecular Chirality via Chiral Determinant Kernels
Primary area: applications to physical sciences (physics, chemistry, biology, etc.)
Keywords: chirality, molecular representation learning, axial chirality

Abstract: Chirality is a fundamental molecular property that governs stereospecific behavior in chemistry and biology. Capturing chirality in machine learning models remains challenging due to the geometric complexity of stereochemical relationships and the limitations of traditional molecular representations that often lack explicit stereochemical encoding. Existing approaches to chiral molecular representation primarily focus on central chirality, relying on handcrafted stereochemical tags or limited 3D encodings, and thus fail to generalize to more complex forms, such as axial chirality. In this work, we introduce \textbf{ChiDeK} (\textbf{Chi}ral \textbf{De}terminant \textbf{K}ernels), a framework that systematically integrates stereogenic information into molecular representation learning. We propose the chiral determinant kernel to encode the SE(3)-invariant chirality matrix and employ cross-attention to integrate stereochemical information from local chiral centers into the global molecular representation. This design enables explicit modeling of chiral-related features within a unified architecture. To support the evaluation of axial chirality, we construct a new benchmark for electronic circular dichroism (ECD) prediction. To the best of our knowledge, ChiDeK is the first architecture capable of jointly encoding central and axial chirality. Across three tasks, including R/S configuration classification, enantiomer ranking, and ECD spectrum prediction, ChiDeK achieves substantial improvements over state-of-the-art baselines, most notably yielding over 18\% higher accuracy on axially chiral molecules.