AIMC Topic: Drug Development

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Inferring Drug-Target Interactions Based on Random Walk and Convolutional Neural Network.

IEEE/ACM transactions on computational biology and bioinformatics Aug 8, 2022
Computational strategies for identifying new drug-target interactions (DTIs) can guide the process of drug discovery, reduce the cost and time of drug development, and thus promote drug development. Most recently proposed methods predict DTIs via int...
Neural Networks, Computer Drug Development Proteins Drug Discovery Algorithms
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CSatDTA: Prediction of Drug-Target Binding Affinity Using Convolution Model with Self-Attention.

International journal of molecular sciences Jul 30, 2022
Drug discovery, which aids to identify potential novel treatments, entails a broad range of fields of science, including chemistry, pharmacology, and biology. In the early stages of drug development, predicting drug-target affinity is crucial. The pr...
Neural Networks, Computer Drug Development Drug Discovery
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A novel machine learning model based on sparse structure learning with adaptive graph regularization for predicting drug side effects.

Journal of biomedical informatics Jul 14, 2022
Drug side effects are closely related to the success and failure of drug development. Here we present a novel machine learning method for side effect prediction. The proposed method treats side effect prediction as a multi-label learning problem and ...
Drug-Related Side Effects and Adverse Reactions Machine Learning Drug Development Research Design Algorithms Humans
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Sequence-based drug-target affinity prediction using weighted graph neural networks.

BMC genomics Jun 17, 2022
BACKGROUND: Affinity prediction between molecule and protein is an important step of virtual screening, which is usually called drug-target affinity (DTA) prediction. Its accuracy directly influences the progress of drug development. Sequence-based d...
Sequence Alignment Amino Acid Sequence Neural Networks, Computer Proteins Drug Development
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Landscape Analysis of the Application of Artificial Intelligence and Machine Learning in Regulatory Submissions for Drug Development From 2016 to 2021.

Clinical pharmacology and therapeutics Jun 16, 2022
Drug Development Artificial Intelligence Algorithms Machine Learning Humans
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Artificial Intelligence and Machine Learning for Lead-to-Candidate Decision-Making and Beyond.

Annual review of pharmacology and toxicology Jun 9, 2022
The use of artificial intelligence (AI) and machine learning (ML) in pharmaceutical research and development has to date focused on research: target identification; docking-, fragment-, and motif-based generation of compound libraries; modeling of sy...
Humans Artificial Intelligence High-Throughput Screening Assays Drug Development Machine Learning
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idse-HE: Hybrid embedding graph neural network for drug side effects prediction.

Journal of biomedical informatics May 28, 2022
In drug development, unexpected side effects are the main reason for the failure of candidate drug trials. Discovering potential side effects of drugsin silicocan improve the success rate of drug screening. However, most previous works extracted and ...
Drug Development Knowledge Drug-Related Side Effects and Adverse Reactions Neural Networks, Computer Software Humans
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Data considerations for predictive modeling applied to the discovery of bioactive natural products.

Drug discovery today May 14, 2022
Natural products (NPs) constitute a large reserve of bioactive compounds useful for drug development. Recent advances in high-throughput technologies facilitate functional analysis of therapeutic effects and NP-based drug discovery. However, the larg...
Biological Products Drug Discovery Drug Development Artificial Intelligence
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DeepNC: a framework for drug-target interaction prediction with graph neural networks.

PeerJ May 11, 2022
The exploration of drug-target interactions (DTI) is an essential stage in the drug development pipeline. Thanks to the assistance of computational models, notably in the deep learning approach, scientists have been able to shorten the time spent on ...
Drug Development Neural Networks, Computer Hypersensitivity Humans Algorithms Benchmarking
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Learning Multi-Scale Heterogeneous Representations and Global Topology for Drug-Target Interaction Prediction.

IEEE journal of biomedical and health informatics Apr 14, 2022
Identification of interactions between drugs and target proteins plays a critical role not only in drug discovery but also in drug repositioning. Deep integration of inter-connections and intra-similarities between heterogeneous multi-source data abo...
Neural Networks, Computer Algorithms Humans Proteins Drug Development Drug Repositioning
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