Artificial intelligence-driven drug development against autoimmune diseases
P Moingeon - Trends in pharmacological sciences, 2023 - cell.com
Artificial intelligence (AI)-based predictive models are being used to foster a precision
medicine approach to treat complex chronic diseases such as autoimmune and …
medicine approach to treat complex chronic diseases such as autoimmune and …
Enzyme catalyzes ester bond synthesis and hydrolysis: The key step for sustainable usage of plastics
J Lai, H Huang, M Lin, Y Xu, X Li, B Sun - Frontiers in Microbiology, 2023 - frontiersin.org
Petro-plastic wastes cause serious environmental contamination that require effective
solutions. Developing alternatives to petro-plastics and exploring feasible degrading …
solutions. Developing alternatives to petro-plastics and exploring feasible degrading …
Quantification of absolute transcription factor binding affinities in the native chromatin context using BANC-seq
Transcription factor binding across the genome is regulated by DNA sequence and
chromatin features. However, it is not yet possible to quantify the impact of chromatin context …
chromatin features. However, it is not yet possible to quantify the impact of chromatin context …
Data‐Driven Protein Engineering for Improving Catalytic Activity and Selectivity
YF Ao, M Dörr, MJ Menke, S Born, E Heuson… - …, 2024 - Wiley Online Library
Protein engineering is essential for altering the substrate scope, catalytic activity and
selectivity of enzymes for applications in biocatalysis. However, traditional approaches, such …
selectivity of enzymes for applications in biocatalysis. However, traditional approaches, such …
Exploring the role of microbial proteins in controlling environmental pollutants based on molecular simulation
J Wu, J Lv, L Zhao, R Zhao, T Gao, Q Xu, D Liu… - Science of the Total …, 2023 - Elsevier
Molecular simulation has been widely used to study microbial proteins' structural
composition and dynamic properties, such as volatility, flexibility, and stability at the …
composition and dynamic properties, such as volatility, flexibility, and stability at the …
High-throughput profiling of sequence recognition by tyrosine kinases and SH2 domains using bacterial peptide display
Tyrosine kinases and SH2 (phosphotyrosine recognition) domains have binding specificities
that depend on the amino acid sequence surrounding the target (phospho) tyrosine residue …
that depend on the amino acid sequence surrounding the target (phospho) tyrosine residue …
HIt Discovery using docking ENriched by GEnerative Modeling (HIDDEN GEM): A novel computational workflow for accelerated virtual screening of ultra‐large …
KI Popov, J Wellnitz, T Maxfield… - Molecular …, 2024 - Wiley Online Library
Recent rapid expansion of make‐on‐demand, purchasable, chemical libraries comprising
dozens of billions or even trillions of molecules has challenged the efficient application of …
dozens of billions or even trillions of molecules has challenged the efficient application of …
Disentangling representations in restricted boltzmann machines without adversaries
A goal of unsupervised machine learning is to build representations of complex high-
dimensional data, with simple relations to their properties. Such disentangled …
dimensional data, with simple relations to their properties. Such disentangled …
TransVAE-DTA: Transformer and variational autoencoder network for drug-target binding affinity prediction
C Zhou, Z Li, J Song, W Xiang - Computer Methods and Programs in …, 2024 - Elsevier
Background and objective Recent studies have emphasized the significance of
computational in silico drug-target binding affinity (DTA) prediction in the field of drug …
computational in silico drug-target binding affinity (DTA) prediction in the field of drug …
A systematic survey in geometric deep learning for structure-based drug design
Structure-based drug design (SBDD) utilizes the three-dimensional geometry of proteins to
identify potential drug candidates. Traditional methods, grounded in physicochemical …
identify potential drug candidates. Traditional methods, grounded in physicochemical …