AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
It is concluded that molecular dynamics has an important place in improving the physicality of existing protein structure prediction paradigms, leading to the development of the Subspace Relaxation Operator (SRO).
This framework provides a clearer understanding of how methodological shifts have shaped the capabilities, limitations, and practical roles of recent models.
The (un)folding rates of natural proteins determine their native stability and functional homeostasis, making them important targets for protein engineering and design. From a prediction standpoint, the rates have been a long‐standing puzzle. We have known for decades that folding rates empirically correlate with prope...
Vilya-2 is the structure-prediction oracle that de novo peptide design pipelines require--establishing the all-atom approach as a general foundation for the design and evaluation of de novo peptide therapeutics.
This mini review traces the evolution of AI-driven methods in protein research, from early residue-contact prediction using coevolutionary information to transformative breakthroughs, the rise of protein language models (PLMs), and the emerging era of generative design and functional modeling.
It is argued that structure prediction should be reformulated as a state-space inference problem: recovering not one conformation's coordinates but accessible states, their energetic and kinetic relationships, context dependence, and responses to perturbations.
WaterFlow is introduced, a flow-matching-based generator model and confidence model for predicting the positions of ordered water molecules in protein structures that outperforms the existing state of the art at every precision level and quantifies the tradeoff between data quantity and data quality.
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