SenCos-GEM: SENet-Calibrated and Law-of-Cosines-Constrained geometry-enhanced molecular representation for property prediction
Read the original at arxiv.org→arXiv:2607.20551v1 Announce Type: new Abstract: Effective molecular representation learning is crucial for accurate molecular property prediction. Recently, numerous self-supervised learning (SSL) approaches...
Original headline: "SenCos-GEM: SENet-Calibrated and Law-of-Cosines-Constrained Geometry-Enhanced Molecular Representation for Property Prediction"
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- Jul 24, 04:00 UTC arXiv cs.LG lead source SenCos-GEM: SENet-Calibrated and Law-of-Cosines-Constrained Geometry-Enhanced Molecular Representation for Property Prediction