AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
This chapter provides an updated overview of the ProBiS tools, which identify binding sites, predict ligand interactions, and analyze conserved water molecules, and enhances the annotation of AlphaFold2-modeled human proteome structures.
COACH-D 2.0 is introduced, a substantially enhanced template-based method for predicting protein-ligand binding sites and features three key advances: integration of multimeric templates from Q-BioLiP into the authors' in-house library, a new multimeric structure processing module enabling binding site prediction for p...
FlexAutoDock is an automated cloud-based molecular docking platform that provides a unified environment for protein-ligand docking and large-scale virtual screening, providing researchers with an accessible computational resource for accelerating early-stage drug discovery.
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.
This study presents an efficient computational approach to identifying novel kinase inhibitors from a database of ligands of the nucleotide-binding protein family, and the protocol could be applied to other protein target families.
A redesigned RCSB Protein Data Bank RCSB.org Advanced Search capability that supports intuitive discovery of 3D structures through a unified interface that integrates annotation‐, sequence‐, and 3D structure‐based searches, and embeds an interactive 3D viewer.
Recent developments in QM-based molecular docking and HTD are reviewed, focusing on cases where QM is used explicitly in docking workflows.
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