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Overall, scLDM provides a robust and biologically consistent strategy for in silico perturbation screening, and exhibits strong interpretability, as the learned perturbation embeddings show high functional alignment with known biological mechanisms.
LARK is introduced, a unified framework for learning atom–molecule–reaction knowledge from chemical transformations that learns chemically informative representations of substructures involved in molecular transformations and establishes chemical transformations as a source of structured supervision for representation...
DynaTCR is presented, a dynamic graph ensemble learning framework for TEB prediction that achieves the highest AUC among all compared methods, demonstrating its robustness and effectiveness for TEB prediction and candidate prioritization.
VAERNAGen, a novel variational autoencoder-based framework for de novo generation of RNA family sequences, outperformed the current state-of-the-art grammar-based method and is established as a state-of-the-art method for de novo generation of RNA family sequences.
The proposed UniPept is a novel model for peptide property prediction that integrates atomic-level and residue-level features to generate robust peptide representations and outperforms competing approaches in interaction and binding affinity prediction tasks.
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