AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
We have 10 of 51 papers
We haven’t gathered this author’s papers yet. Follow them and we’ll fetch their work.
Not the right person? Other researchers publish under this name.
The resources and modeling advances supporting AI virtual cells' value for mechanism-of-action analysis, efficacy, safety, resistance, and combination studies are reviewed, and evidence requirements for pharmacological use are defined.
Structure-sensitive properties (SSPs), including activity cliffs and chirality-dependent properties, challenge molecular machine learning because small structural perturbations can cause abrupt property changes and invalidate smooth structure–property assumptions. Here, we present CAMF (Chirality- and Activity-cliff-aw...
This work proposes MF‐Net, a unified hierarchical multiscale fusion framework that integrates sequence‐, atomic‐, and fragment‐level representations to model drug–target interactions across complementary scales and demonstrates strong early enrichment across multiple virtual screening datasets.
MAERM has the potential to reduce the experimental cost of measuring enzymes’ catalytic scope, facilitate enzyme design, and ultimately accelerate the design-build-test-learn cycle in enzyme engineering.
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.
This work aims to discuss the features and the generative performance of different types of molecular generative models for the PROTAC design task and help researchers to better apply these models in practical cases.
TPS-Flow is presented, a physics-guided flow-based generative framework for end point-conditioned conformational path sampling between predefined protein states (not equilibrium ensembles), thereby bridging atomistic simulation and deep generative modeling of protein transition paths.
An integrated virtual screening strategy based on molecular fingerprint similarity, pharmacophore models, molecular docking, and molecular dynamics simulation is proposed and three promising lead compounds targeting RIPK3 for AD treatment are offered.
CoBind is presented, a multitask deep learning framework that jointly predicts RNA–compound interactions and nucleotide-level binding-site probabilities within a unified architecture and provides complementary nucleotide-level binding-site localization, supporting a site-aware view of RNA–ligand recognition under distr...
The Comprehensive VS Platform with AI Engine (CVSP-AIE) for drug discovery from compound libraries integrates three AI models: KarmaDock, a fast docking model that directly updates atomic coordinates; CarsiDock, an accurate docking model that predicts protein-ligand distances and reconstructs binding poses; and RTMScor...
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.