Results 121 to 130 of about 6,076,909 (260)

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

Polymeric Sorbents as Energy‐Efficient Alternative to Cryogenic Distillation for Light Hydrocarbon Purification

Advanced Materials, EarlyView.
The transition from cryogenic distillation to polymeric sorbents for light hydrocarbon purification is crucial for energy and environmental sustainability. The polymeric sorbents are engineered to separate gas mixtures based on their specific properties.
Kelechi Festus, Ankit Mondal, Suman Mandal, Jazmine Aiya D. Marquez, Arnab Ghosh, Aishi Sikdar, Urme Podder, Ahana Sarkar, Qingsheng Wang, Hong‐Cai Zhou   +9 more
wiley   +1 more source

A dual-branch graph neural network architecture for drug-target binding affinity prediction

Scientific Reports
Graph Neural Networks have emerged as a powerful paradigm for artificial intelligence driven drug discovery, offering molecular representation learning that surpasses many conventional approaches.
Khushnood Abbas, Chen Hao, Shi Dong, Wengang Zhou, Yong Tang, Shayla Islam, Mohammad Kamrul Hasan, Siti Norul Huda Sheikh Abdullah, Ruizhe Hou   +8 more
doaj   +1 more source

Sustainable and Multifunctional Natural Macromolecular Polymers for Aqueous Zn Metal Batteries

Advanced Materials, EarlyView.
We comprehensively review the structure‐function relationships and regulatory mechanism of natural macromolecular polymers in stabilizing zinc anodes at the microscopic and mesoscopic scales. The interactions among polymer structures, optimization strategies, and regulatory mechanisms are discussed systematically summarizing recent related research ...
Yunuo Shi, Bo Jiang, Jingcheng Zhang, Xiaodong Shao, Peixun Xiong, Lutong Shan, Shengyang Huang, Mengfang Liang, Mengyao Chang, Fangyan Liu, Wenwu Li, Baoyu Xia, Wei Ji, Ho Seok Park   +13 more
wiley   +1 more source

Polyphenol‐Inspired Materials for Agricultural Applications

Advanced Materials, EarlyView.
This review outlines the use of polyphenol‐inspired materials for sustainable agriculture, highlighting their molecular design, interfacial assembly, structure–property relationships, and prospects toward precision agriculture, climate resilience, ecosystem protection, and circular bioeconomy strategies.
Haofu Liu, Omid Mazaheri, Zhixing Lin, Tianzheng Wang, Jingqu Chen, Yuang Gu, Xia Liang, Deli Chen, Frank Caruso   +8 more
wiley   +1 more source

ResDTA: Predicting Drug-Target Binding Affinity Using Residual Skip Connections

, 2023
The discovery of novel drug target (DT) interactions is an important step in the drug development process. The majority of computer techniques for predicting DT interactions have focused on binary classification, with the goal of determining whether or ...
Ghosh, Partho, Haque, Md. Aynal
core  

Elastomeric 3D‐Printed Microenvironments Enable Nanonewton Force Measurements in Healthy and Diseased Human Pluripotent Stem Cell‐Derived Neuroepithelial Cells

Advanced Materials, EarlyView.
We present elastomeric three‐dimensional (3D) microstructures fabricated via two‐photon polymerization (2PP) and post‐processed through wet etching, for quantifying nanonewton (nN)‐scale forces applied by healthy and diseased neural cells. The mechanically characterized free‐standing beam architectures enable measurement of traction forces of ...
Pieter F. J. van Altena, Lucia Castillo Ransanz, Ruben H. Guis, Giulia Bergamaschi, Nanne J. Paauw, Urs Staufer, Vivi M. Heine, Angelo Accardo   +7 more
wiley   +1 more source

Binding Affinity Prediction with 3D Machine Learning: Training Data and Challenging External Testing [PDF]

Protein-ligand binding affinity prediction is one of the major challenges in computational assisted drug discovery. An active area of research uses machine learning (ML) models trained on 3D structures of protein ligand complexes to predict binding modes,
Gary , Tresadern, Lili, Cao, Jose Carlos, Gómez-Tamayo, Mazen, Ahmad   +3 more
core   +2 more sources

PRGNet: a Parallel Residual Graph Network for enhanced drug-target binding affinity prediction

BMC Genomics
Predicting drug-target binding affinity (DTA) remains a cornerstone of structure-based drug discovery but is still constrained by fundamental methodological trade-offs.
Jing Liu, Aamir Mehmood, Haiqi Liu, Yishu Liu, Dongqing Wei, Daixi Li   +5 more
doaj   +1 more source

Side‐Chain‐Induced Interlayer Slipping in Metalloporphyrin COFs Enables Microenvironment and Spin‐State Regulation for Photocatalytic CO2 Reduction

Advanced Materials, EarlyView.
Alkoxy chain length drives a continuous transition from eclipsed AA to a serrated, slipped AA* configuration over Co‐porphyrin COFs. A moderate slip both contracts the pore channel to enrich CO2 and suppress hydrogen evolution, and enhances collective high‐spin of Co sites, which delivers a record CO production rate with high selectivity.
Jie He, Qingxuan Chen, Minxian Zhang, Wenhao Zhao, Aoni Xu, Jinqiang Zhang, Hongqi Sun, Shaobin Wang, Xiaoguang Duan   +8 more
wiley   +1 more source