Results 181 to 190 of about 228,098 (300)

Decoding Tattoo and Permanent Makeup Pigments: Linking Physicochemical Properties to Absorption, Distribution, Metabolism, and Elimination Profiles Using Quantitative Structure–Activity Relationship (QSAR)‐Based New Approach Methodologies (NAMs)

Advanced Intelligent Discovery, EarlyView.
This study applies QSAR‐based new approach methodologies to 90 synthetic tattoo and permanent makeup pigments, revealing systemic links between their physicochemical properties and absorption, distribution, metabolism, and elimination profiles. The correlation‐driven analysis using SwissADME, ChemBCPP, and principal component analysis uncovers insights
Girija Bansod, Ajay Vikram Singh, Preeti Bhardwaj, Tulika Rai, Sweta Vijay Nakhale, Amruta Shelar, Rajendra Patil, Peter Laux, Andreas Luch, Christopher J. Osgood, Michael W. Stacey   +10 more
wiley   +1 more source

In Silico Structure Prediction of Human Fatty Acid Synthase–Dehydratase: A Plausible Model for Understanding Active Site Interactions

Bioinformatics and Biology Insights, 2016
Arun John, Vetrivel Umashankar, A. Samdani, Manoharan Sangeetha, Subramanian Krishnakumar, Perinkulam Ravi Deepa   +5 more
doaj  

Rational engineering of allosteric protein switches by in silico prediction of domain insertion sites. [PDF]

Nat Methods
Wolf B, Shehu P, Brenker L, von Bachmann AL, Kroell AS, Southern N, Holderbach S, Eigenmann J, Aschenbrenner S, Mathony J, Niopek D.   +10 more
europepmc   +1 more source

Advanced Experiment Design Strategies for Drug Development

Advanced Intelligent Discovery, EarlyView.
Wang et al. analyze 592 drug development studies published between 2020 and 2024 that applied design of experiments methodologies. The review surveys both classical and emerging approaches—including Bayesian optimization and active learning—and identifies a critical gap between advanced experimental strategies and their practical adoption in ...
Fanjin Wang, Anthony Harker, Mohan Edirisinghe, Maryam Parhizkar   +3 more
wiley   +1 more source

In Silico Prediction of Potential pTLR7/pSTING Dual-Targeting Ligands via Virtual Screening and Molecular Dynamics Simulation. [PDF]

Int J Mol Sci
Liu C, Qin Z, Bai L, Xu X, Ge W, Li Z, Li J.   +6 more
europepmc   +1 more source

A Generalized Framework for Data‐Efficient and Extrapolative Materials Discovery for Gas Separation

Advanced Intelligent Discovery, EarlyView.
This study introduces an iterative supervised machine learning framework for metal‐organic framework (MOF) discovery. The approach identifies over 97% of the best performing candidates while using less than 10% of available data. It generalizes across diverse MOF databases and gas separation scenarios.
Varad Daoo, Jayant K. Singh
wiley   +1 more source

Thiocarbamoylimidates as Precursors of New 5-Amino-1,2,4-Triazole and Oxadiazole Derivatives In Silico Prediction of Absorption, Distribution, Metabolism, and Excretion Parameters. [PDF]

Chempluschem
Gatri O, Roisnel T, Cordier M, Dorcet V, Efrit ML, Fischmeister C.   +5 more
europepmc   +1 more source

A Computational Approach for Biomimetic Design of Liver‐On‐A‐Chip

Advanced Intelligent Discovery, EarlyView.
A biomimetic liver‐on‐a‐chip with antiparallel perfusion was designed using COMSOL‐guided simulation to replicate hepatic acinus transport. Full‐scale modelling revealed diffusion‐dominated, length‐dependent nutrient gradients and low‐shear conditions.
Zhenxu Yang, Qiankun Yin, Qingxin Liu, Zhejun Xu, Ye Zhang, Christopher Vega‐Sánchez, Xiaochen Liu, Daniele Vigolo, Jiao Jiao Li, Ken‐Tye Yong   +9 more
wiley   +1 more source

In Silico Prediction of Tetrastatin-Derived Peptide Interactions with αvβ3 and α5β1 Integrins. [PDF]

Pharmaceuticals (Basel)
Paturel V, Baud S, Schneider C, Brassart-Pasco S.   +3 more
europepmc   +1 more source

Accelerating Biosensor Discovery: A Computationally‐Driven Pipeline for Microplastics Monitoring

Advanced Intelligent Discovery, EarlyView.
A computationally guided pipeline unites molecular simulation, synthetic biology, electrochemical engineering, and machine learning to accelerate biosensor discovery. A Bacillus anthracis carbohydrate‐binding module is used to develop a high‐performance micro‐ and nanoplastics sensor with greatly reduced error and variability.
Gabriel X. Pereira, Marcos de C. Leite, Pedro H. M. Zanineli, Juliana N. Y. Costa, Débora M. Cunha, Sophia P. Serra, Pedro H. Sophia, Flávio M. Shimizu, Yasmin W. Moura, Célio C. Oliveira, Gabriel R. Schleder, Gabriela F. Persinoti, Leandro Merces, Renato S. Lima   +13 more
wiley   +1 more source