Highlight: FeaSynth integrates an Expert Encoding Scheme with 145 annotated reaction templates to resolve critical chemical conflicts and significantly outperform SOTA baselines in practical synthesizability. We also establishes a standardized Reaction Pathway Feasibility (RPF) Score to rigorously quantify route viability and ensure only the most robust, experimentally valid pathways are selected.
Highlight: scNext is a generative foundation model that transforms static single-cell data into predictive temporal sequences, enabling the forecasting of cellular evolution, developmental potential, and long-term therapeutic responses.
Highlight: Cost-Effective Prediction of Macrocyclic Peptide Permeability via Dynamic Conformational Analysis We introduce a computationally efficient molecular dynamics framework that accurately predicts macrocyclic peptide permeability by quantifying environment-dependent conformational adaptability and specific shifts in polar surface area .
Highlight: This study presents ImmunoSeq, an interpretable and applicable method for immunogenicity prediction. ImmunoSeq demonstrated superior ADA correlation and humanness classification accuracy compared to deep learning models, while accurately predicts ADA reductions in humanization, enabling sufficient sequence optimization for humanness.
Highlight: UniMoMo is the first all-atom geometric latent diffusion framework capable of designing peptides, antibodies, and small molecules within a unified generative model, demonstrating superior performance and cross-domain knowledge transfer in de novo binder design.
Highlight: This study establishes a comprehensive benchmarking framework for in silico gene perturbation, systematically evaluating ten AI methods across four biological scenarios to standardize assessment and advance computational drug discovery.