DMMRL: Disentangled Multi-Modal Representation Learning via Variational Autoencoders for Molecular Property Prediction
Molecular property prediction constitutes a cornerstone of drug discovery and materials science, necessitating models capable of disentangling complex structure-property relationships across diverse molecular modalities. Existing approaches frequently exhibit entangled representations--conflating structural, chemical, and functional factors--thereby limiting interpretability and transferability. Furthermore, conventional methods inadequately exploit complementary information from graphs, sequenc
Record details
Published: 22 March 2026
Source: arXiv
Category: Research
Topics: Safety & alignment · Healthcare · Biotech
Retrieved: 14 July 2026
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ethics.ai (22 March 2026), “DMMRL: Disentangled Multi-Modal Representation Learning via Variational Autoencoders for Molecular Property Prediction,” evidence record 6903, https://ethics.ai/record/6903 (originally published by arXiv).
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