AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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1,416 papers
A comparative model explaining when and why subventral and dorsal glands exchange, retain, or alternate their functions across contrasting parasitic lifestyles across contrasting parasitic lifestyles is explained.
This work proposes LumiCharge, a novel atomic charge prediction framework that incorporates high-order spherical harmonics convolutions and explicitly models multibody interactions, and demonstrates exceptional extrapolation capability and robustness across molecules of varying sizes, effectively overcoming the limitat...
LT16 outperformed existing antiandrogens by fully antagonizing clinical AR mutations and effectively suppressing castration- and enzalutamide-resistant LNCaP cells proliferation in vitro and in vivo, positioning it as a promising and innovative therapeutic for advanced PCa.
PepBAN is introduced, a deep learning framework for modeling PepPI predictions that effectively learns the pattern of pairwise local interactions, enables the identification of key residues participating in the peptide-protein interactions, and offers an intuitive approach to interpret the underlying mechanisms of PepP...
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This study proposes SynGFN, which models molecular design as a cascade of simulated chemical reactions, enabling the assembly of molecules from synthesizable building blocks, as a bridge linking molecular design and synthesis, accelerating exploration and producing diverse, synthesizable, high-performance molecules.
This work introduces an advanced equivariant graph attention neural network specifically engineered to model long-range atomic electrostatic interactions with high precision, and improves the model's accuracy, generalization, and robustness in complex scenarios.
PepPCBench enables a robust evaluation of PFNN-based methods and supports their continued development for peptide-protein structure prediction, and highlights the influence of peptide length, conformational flexibility, and training set similarity on prediction accuracy.
CovalentLab is introduced, an interactive computational platform that integrates ligand-based and warhead-based strategies into a unified workflow for the rational design of covalent ligands and enables the prediction and ranking of nine classes of covalent-binding residues in proteins according to their reactivity.
This work proposes a novel strategy that incorporates two models: a fragment-based model (Frag-MD) and a character-based model (MD), both integrating a conditional transformer, recurrent neural networks, and reinforcement learning that holds the potential to establish a new paradigm for discovering novel PSs applicable...
RAPiDock is presented, an all-atom diffusion model that predicts peptide–protein binding patterns across 92 amino acid types, enabling high-throughput virtual screening for advancing therapeutic peptide design and serve as a powerful tool for high-throughput virtual screening with structural precision.
Abstract Cyclic peptides represent a highly promising class of biopharmaceutical scaffolds. The screening of cyclic peptides against protein targets can be greatly facilitated using computational approaches, especially molecular docking. However, it remains a crucial challenge to accurately predict protein–cyclic pepti...
A unified benchmarking framework is established that enables systematic evaluation of docking methods across diverse tasks and provides critical insights into the strengths and limitations of current docking strategies, thereby informing future developments in protein-protein docking research.
GPT-Lab
· gpt-lab.eu
Oct 8, 2026
Exploring how generative AI could make machine vision more accessible to businesses. The post GenEye in a Box: Making Machine Vision Something You Can Just Ask For appeared first on GPT-Lab.
A Blog post by Ai2 on Hugging Face
A Blog post by ServiceNow-AI on Hugging Face
“InstructMesh” can generate designs for everyday objects that are easy to edit and fabricate for both experts and newcomers to 3D modeling.
Proof of concept for watermarking AI-generated proteins while preserving biological function.
Algorithms & Theory
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