Visualization and prototype preference analyses indicate that PACL separates structurally similar molecules with different functional properties more clearly in task-aligned representation spaces, supporting adaptive substructure contrastive learning and task-specific semantic alignment as an effective strategy for multi-task molecular property prediction in data-limited settings.
CAMF (Chirality- and Activity-cliff-aware Multimodal Framework), a task-adaptive framework that models SSPs through selective integration of complementary molecular evidence, reveals that modality relevance is strongly task-dependent, with descriptors and 3D geometry becoming especially important in non-smooth property...
Shaolong Lin, Si-Long Zhai, Shi-Hang Wang et al.· Chemical Science· 0 citations
Molecular property prediction is a key task in AI-driven drug discovery, yet the prevalence and impact of label imbalances in molecular property regression remain poorly understood. Through a systematic benchmark of widely used molecular property data sets, we show that target values are often highly imbalanced and tha...
Y. Sun, Yu Shi, Alana Deng et al.· Journal of Chemical Informat...· 0 citations
This work introduces Wide Encoded Extended Connectivity Fingerprints with Substructure Rotary Graph-distance Encoding (SuRGE), a tokenized hierarchical Morgan representation in which graph-distance-dependent rotations are applied at the input and within transformer self-attention.
Molecular property prediction is central to drug development and materials discovery, but experiments are costly and labeled data are scarce. Context-aware methods use auxiliary assay labels to support few-shot prediction, and recent work supervises property relations with label agreement. However, label agreement is s...
Lin-Yu Li, Zhi Jin, Yuan-Peng He et al.· 0 citations
This work introduces ActiveFusion, a framework for integrating heterogeneous molecular representations within active learning workflows for molecular property prediction and offers practical guidance for designing active learning pipelines that balance prediction accuracy with chemical space exploration.
Nelson Evbarunegbe, Shiyun Wa, Luke Taylor et al.· Journal of Chemical Informat...· 0 citations
Accurate prediction of cancer cell responses to chemical compounds remains a fundamental challenge in precision oncology and drug discovery. Although several drug response prediction (DRP) models have been proposed, most were evaluated using random splits of drug-cell line pairs, allowing the same drugs to appear in bo...
Ji-Sang Lee, Taelim Yeom, June-Woo Kim et al.· IEEE journal of biomedical a...· 0 citations
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