Results 101 to 110 of about 71,218 (232)

Organic Materials of Tomorrow: Horizons of Artificial Intelligence

Advanced Materials, EarlyView.
This review examines machine learning techniques accelerating the discovery of organic semiconductors by linking molecular structure to properties. Key methods include graph neural networks, generative models, and active learning. Applications to organic photovoltaics demonstrate practical impact.
Harold Mena, J. Terence Blaskovits, Kun‐Han Lin, Denis Andrienko   +3 more
wiley   +1 more source

Resolving Heterogeneity of Targeted Lipid Nanoparticles Through Solution‐Based Biophysical Analyses

Advanced Materials, EarlyView.
AF4‐UV‐DLS‐MALS‐SAXS resolves previously inaccessible targeted lipid nanoparticle (tLNP) subpopulations that differ in size, shape, and composition. Correlation of subpopulation‐resolved biophysical properties with in vivo RNA delivery reveals that targeted placental transfection is associated with distinct tLNP subpopulations rather than ensemble ...
Hannah C. Geisler, Hannah C. Safford, Ajay S. Thatte, Marshall S. Padilla, Elisa Battistini, Hannah M. Yamagata, Violet M. Ullman, Alex Chan, Benjamin E. Nachod, Anushka Agrawal, Maxwell B. Watkins, Jesse B. Hopkins, Andrew Tsourkas, Kushol Gupta, Michael J. Mitchell   +14 more
wiley   +1 more source

Optimized differential evolution and hybrid deep learning for superior drug-target binding affinity prediction

Alexandria Engineering Journal
Investigating Drug-Target Interactions (DTI) is crucial for drug repositioning and discovery tasks. However, discovering DTIs through experimental approaches is time-consuming and requires substantial financial resources.
Aryan Bhatia, Moolchand Sharma, Eatedal Alabdulkreem, Nuha Alruwais, Muhammad Kashif Saeed, Abdulsamad Ebrahim Yahya   +5 more
doaj   +1 more source

Boolean Logic‐Based Control Over Recombinant Protein Biomaterial Degradability and Therapeutic Delivery

Advanced Materials, EarlyView.
Fully recombinant protein‐based biomaterials execute complex Boolean logic for user‐programmable material degradation and concomitant therapeutic cargo release. Biologics, such as growth factors, can be incorporated within the crosslinkers as “drugamers”, while encapsulated cells can be released according to nested YES/OR/AND‐type logical operations ...
Murial L. Ross, Ryan Gharios, Shivani Kottantharayil, Cole A. DeForest   +3 more
wiley   +1 more source

DTA Atlas: A massive-scale drug repurposing database

Artificial Intelligence in the Life Sciences
The drug development process is costly and time-consuming. Repurposing existing approved drugs, an efficient and cost-effective strategy, involves assessing numerous drug-protein pairs to uncover new interactions.
Madina Sultanova, Elizaveta Vinogradova, Alisher Amantay, Ferdinand Molnár, Siamac Fazli   +4 more
doaj   +1 more source

Reprogramming the Immunosuppressive Microenvironment in Triple‐Negative Breast Cancer via FAP‐Targeted Nanoprobes for Multimodal Imaging and Photothermal Therapy

Advanced Materials, EarlyView.
Targeting cancer‐associated fibroblasts (CAFs) with fibroblast activation protein (FAP)‐directed nanoprobes for multimodal imaging and photothermal remodeling of the immunosuppressive microenvironment to enhance immunotherapy in triple‐negative breast cancer (TNBC).
Ling Zhan, Yanhong Chen, Jingjing Hu, Chunting Wang, Huanhuan Liu, Defan Yao, Dengbin Wang   +6 more
wiley   +1 more source

Recent trends in machine learning and deep learning-based prediction of G-protein coupled receptor-ligand binding affinities

Frontiers in Bioinformatics
Accurately predicting protein-ligand binding affinity is key in drug discovery. Machine Learning and Deep Learning methods used in the drug discovery process have advanced the prediction of drug–target binding affinities, particularly for G protein ...
Joshua Stephenson, Konda Reddy Karnati
doaj   +1 more source

Optimization of drug-target affinity prediction methods through feature processing schemes. [PDF]

Bioinformatics, 2023
Ru X, Zou Q, Lin C.
europepmc   +1 more source

CASTER-DTA: equivariant graph neural networks for predicting drug–target affinity

Briefings in Bioinformatics
Abstract Accurately determining the binding affinity of a ligand with a protein is important for drug design, development, and screening. With the advent of accessible protein structure prediction methods such as AlphaFold, predicted protein 3D structures are readily available; however, scalable methods for predicting binding affinity
Rachit Kumar, Joseph D. Romano, Marylyn D. Ritchie   +2 more
openaire   +3 more sources

Recent Advances in Ferrite‐Based Materials for Biomedical Applications: A Comprehensive Review

Advanced Materials, EarlyView.
Ferrite nanoplatforms are presented as tunable biomedical materials in which synthesis control, cation engineering, defect/morphology regulation, and surface functionalization govern structure–property–bioactivity relationships. These design strategies enable multifunctional applications including MRI contrast, magnetic hyperthermia, targeted drug ...
Pramod D. Mhase, Varsha C. Pujari, Akash V. Fulari, Sunil M. Patange, Sean Li, Danyang Wang, Sagar E. Shirsath   +6 more
wiley   +1 more source