Results 81 to 90 of about 160,727 (166)

Development of models for predicting Torsade de Pointes cardiac arrhythmias using perceptron neural networks

, 2017
Blockage of some ion channels and in particular, the hERG cardiac potassium channel delays cardiac repolarization and can induce arrhythmia. In some cases it leads to a potentially life-threatening arrhythmia known as Torsade de Pointes (TdP).
Buzatu, Dan, Harris, Stephen, Sharifi, Mohsen, Wilkes, Jon   +3 more
core   +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

Learnable protein representations in computational biology for predicting drug-target affinity

Journal of Cheminformatics
In this review, we discuss the various different types of learnable protein representations that have been used in computational biology, with a particular focus on representations that have been used in the paradigm of predicting drug-target affinity ...
Rachit Kumar, Joseph Romano, Marylyn Ritchie   +2 more
doaj   +1 more source

A topological approach for protein classification

, 2015
Protein function and dynamics are closely related to its sequence and structure. However prediction of protein function and dynamics from its sequence and structure is still a fundamental challenge in molecular biology.
Cang, Zixuan, Mu, Lin, Opron, Kristopher, Wei, Guo-Wei, Wu, Kedi, Xia, Kelin   +5 more
core   +1 more source

MM-DRPNet: A multimodal dynamic radial partitioning network for enhanced protein–ligand binding affinity prediction

Computational and Structural Biotechnology Journal
Accurate prediction of drug-target binding affinity remains a fundamental challenge in contemporary drug discovery. Despite significant advances in computational methods for protein-ligand binding affinity prediction, current approaches still face ...
Dayan Liu, Tao Song, Shudong Wang
doaj   +1 more source

MFAE: Multilevel Feature Aggregation Enhanced Drug‐Target Affinity Prediction for Drug Repurposing Against Colorectal Cancer

Advanced Intelligent Systems
Colorectal cancer (CRC), a leading cause of cancer‐related deaths globally, demands innovative therapeutic strategies to improve patient outcomes. Drug repurposing, identifying new uses for existing drugs, provides a cost‐effective solution. To this end,
Guanxing Chen, Haohuai He, Calvin Yu-Chian Chen   +2 more
doaj   +1 more source

InceptionDTA: Predicting drug-target binding affinity with biological context features and inception networks

Heliyon
Predicting drug-target binding affinity via in silico methods is crucial in drug discovery. Traditional machine learning relies on manually engineered features from limited data, leading to suboptimal performance.
Mahmood Kalemati, Mojtaba Zamani Emani, Somayyeh Koohi   +2 more
doaj   +1 more source

Binding Affinity Prediction for Pancreatic Ductal Adenocarcinoma Using Drug-Target Descriptors and Artificial Intelligence

IEEE Access
Pancreatic ductal adenocarcinoma (PDAC) is the most common and aggressive form of pancreatic cancer, accounting for 90% of all pancreatic malignancies.
Pragya, A. Amalin Prince, Jac Fredo Agastinose Ronickom   +2 more
doaj   +1 more source

A comparison of embedding aggregation strategies in drug–target interaction prediction

BMC Bioinformatics
The prediction of interactions between novel drugs and biological targets is a vital step in the early stage of the drug discovery pipeline. Many deep learning approaches have been proposed over the last decade, with a substantial fraction of them ...
Dimitrios Iliadis, Bernard De Baets, Tapio Pahikkala, Willem Waegeman   +3 more
doaj   +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