AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
Ligand-protein binding prediction remains a central challenge, yet the contribution of ligand-side information to performance is unclear and ligand-only models as interpretable screening tools are established and used as a baseline for assessing the added value of protein representations.
LGBind is introduced, a structure-guided deep learning framework for generalized, ligand-conditioned binding site identification and demonstrates profound robustness and generalization in predicting novel ligand-target interactions, and demonstrates remarkable versatility.
Protein--ligand binding affinity prediction is fundamental to computational drug discovery, yet modern AI-driven models are limited by pervasive heterogeneity in their training data: bioactivity values are aggregated across diverse assay types and experimental conditions without accounting for protocol-level difference...
It is demonstrated that attention-based protein language models can accurately identify nucleic acid-binding proteins directly from sequence data and reveal biologically meaningful sequence determinants of binding, establishing an interpretable and scalable framework for proteome-wide characterization of protein–nuclei...
Machine learning accelerates GPCR ligand discovery but often lacks interpretability and struggles to generalise to structurally novel chemical space. We present an integrated framework combining ensemble learning, SHAP interpretability, and complementary structure-based analyses to prioritise putative candidates for th...
DESTIN (dual structure-guided estimation of substance-target interaction networks), an ML framework for CPI prediction on a genome-wide scale from the three-dimensional structures of compounds and proteins exhibited good predictive performance, even for structurally complex and pharmacologically important membrane prot...
Testing the ability of common large language models to consider design principles to generate de novo proteins that bind metals and lipophilic small molecules without copying existing sequences highlights the utility of LLMs in making protein design more comprehensible and accessible to users without sophisticated desi...
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