AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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2,041 papers
The AI-guided development of a first-in-class proteolysis-targeting chimera (PROTAC) designed to selectively degrade the CLIP1-LTK fusion protein is reported, providing a promising therapeutic strategy for overcoming acquired resistance in kinase-driven cancers.
ERAM aligns pre-trained molecular representations from Protein Language Model with the knowledge of enzyme catalysis by modeling enzymatic reactions as multi-relational data, and demonstrates its potential as a versatile and effective tool for enzyme catalysis research.
This study introduces LaMGen, an LLM-powered framework that leverages large-scale protein-ligand data and rotation-aware molecular encoding to rapidly produce chemically plausible multi-target candidates, achieving strong zero-shot generalization, superior molecular quality, and robust performance across dual- and trip...
An integrated computational workflow that combines enhanced sampling techniques and machine learning collective variables to identify druggable conformations of the AR-NTD and elucidate the binding mechanism of its modulator, EPI-002 is introduced.
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This paper proposes a novel approach to protein structure prediction leveraging principles from quantum mechanics, specifically entanglement and superposition. Traditional methods for protein folding, reliant on classical computational techniques, frequently struggle with accuracy and efficiency, particularly when deal...
Frontotemporal lobar degeneration (FTLD) is a common cause of early-onset dementias marked by progressive declines in behavior, cognition, and/or movement. FTLD neuropathologies, including TDP-43 proteinopathies and primary tauopathies, do not have reliable fluid biomarkers for in-vivo diagnosis nor biomarkers that dir...
The simulation of complex biological systems, such as protein folding and gene regulation, presents significant challenges due to the inherent complexity and often intractable nature of these systems. Traditional computational methods struggle to capture the nuanced dynamics of biological processes, limiting our abilit...
The field of topology has witnessed significant advancements in understanding and manipulating complex network structures, from protein folding to genome sequencing. This research introduces a novel approach to topology optimization – the "Adaptive Topology Optimization Algorithm" – that leverages a self-adaptive topol...
By combining conditional pre-trained language model fine-tuning with dynamic structural evaluation, the framework advances beyond static sequence-based predictions to address the structural and functional dimensions of enzyme behavior, key aspects for both biomedical and industrial applications.
It is described that downregulation of WNK1 through RNA interference in HEK293 cells led to a two-fold decrease in cell-surface GLUT1 abundance, concomitant with a 40% decrease in glucose uptake.
This release contains the version of the FCKcat code associated with the manuscript: Overcoming systematic data biases enables accurate prediction of enzyme kcat fold-changes for computational protein design Authors: Yvan Rousset, Alexander Kroll, and Martin J. Lercher The data and trained model files required to repro...
Bayesian Geometric Chaos with Adaptive Constraint Propagation represents a novel approach to modeling complex systems, particularly those exhibiting intricate dynamics and high-dimensional parameter spaces. This paper explores the integration of a variational Bayesian framework, incorporating adaptive constraint propag...
Proof of concept for watermarking AI-generated proteins while preserving biological function.
A new machine-learning framework aims to improve the success rate of computational protein design while moving away from results that reproduce sequences found in nature.
Scientists are using AlphaFold to strengthen a photosynthesis enzyme for resilient, heat-tolerant crops.
Explore how AlphaFold has accelerated science and fueled a global wave of biological discovery.
AlphaFold has revealed the structure of a key protein behind heart disease
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