AIMC Topic: Drug Development

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Predicting Drug-Target Affinity Based on Recurrent Neural Networks and Graph Convolutional Neural Networks.

Combinatorial chemistry & high throughput screening Jan 1, 2022
BACKGROUND: Drug development requires a lot of money and time, and the outcome of the challenge is unknown. So, there is an urgent need for researchers to find a new approach that can reduce costs. Therefore, the identification of drug-target interac...
Molecular Structure Drug Interactions Amino Acid Sequence Neural Networks, Computer Drug Development
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DTI-Voodoo: machine learning over interaction networks and ontology-based background knowledge predicts drug-target interactions.

Bioinformatics (Oxford, England) Dec 11, 2021
MOTIVATION: In silico drug-target interaction (DTI) prediction is important for drug discovery and drug repurposing. Approaches to predict DTIs can proceed indirectly, top-down, using phenotypic effects of drugs to identify potential drug targets, or...
Drug Development Proteins Machine Learning Software Neural Networks, Computer
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[Protein modeling and design based on deep learning].

Sheng wu gong cheng xue bao = Chinese journal of biotechnology Nov 25, 2021
The accumulation of protein sequence and structure data allows researchers to obtain large amount of descriptive information, simultaneously it poses an urgent need for researchers to extract information from existing data efficiently and apply it to...
Deep Learning Drug Development Proteins Amino Acid Sequence
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Learning to SMILES: BAN-based strategies to improve latent representation learning from molecules.

Briefings in bioinformatics Nov 5, 2021
Computational methods have become indispensable tools to accelerate the drug discovery process and alleviate the excessive dependence on time-consuming and labor-intensive experiments. Traditional feature-engineering approaches heavily rely on expert...
Research Design Drug Discovery Software Algorithms Cheminformatics Deep Learning Drug Development
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Utilizing graph machine learning within drug discovery and development.

Briefings in bioinformatics Nov 5, 2021
Graph machine learning (GML) is receiving growing interest within the pharmaceutical and biotechnology industries for its ability to model biomolecular structures, the functional relationships between them, and integrate multi-omic datasets - amongst...
Neural Networks, Computer Machine Learning Models, Molecular Molecular Structure Computer Graphics Drug Development Drug Repositioning Algorithms Drug Discovery
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Integrating multi-scale neighbouring topologies and cross-modal similarities for drug-protein interaction prediction.

Briefings in bioinformatics Sep 2, 2021
MOTIVATION: Identifying the proteins that interact with drugs can reduce the cost and time of drug development. Existing computerized methods focus on integrating drug-related and protein-related data from multiple sources to predict candidate drug-t...
Reproducibility of Results Protein Binding Computational Biology Models, Theoretical Machine Learning Algorithms Humans Proteins Pharmaceutical Preparations Drug Development Support Vector Machine
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An end-to-end heterogeneous graph representation learning-based framework for drug-target interaction prediction.

Briefings in bioinformatics Sep 2, 2021
Accurately identifying potential drug-target interactions (DTIs) is a key step in drug discovery. Although many related experimental studies have been carried out for identifying DTIs in the past few decades, the biological experiment-based DTI ident...
Pharmaceutical Preparations Drug-Related Side Effects and Adverse Reactions Drug Development Proteins Humans Machine Learning Computer Simulation Drug Discovery Software
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Drug-drug interaction prediction with Wasserstein Adversarial Autoencoder-based knowledge graph embeddings.

Briefings in bioinformatics Jul 20, 2021
An interaction between pharmacological agents can trigger unexpected adverse events. Capturing richer and more comprehensive information about drug-drug interactions (DDIs) is one of the key tasks in public health and drug development. Recently, seve...
Pattern Recognition, Automated Drug Development Drug Interactions Neural Networks, Computer
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DeepPurpose: a deep learning library for drug-target interaction prediction.

Bioinformatics (Oxford, England) Apr 1, 2021
SUMMARY: Accurate prediction of drug-target interactions (DTI) is crucial for drug discovery. Recently, deep learning (DL) models for show promising performance for DTI prediction. However, these models can be difficult to use for both computer scien...
Drug Development Proteins Drug Discovery Deep Learning Pharmaceutical Preparations
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Can machine learning consistently improve the scoring power of classical scoring functions? Insights into the role of machine learning in scoring functions.

Briefings in bioinformatics Jan 18, 2021
How to accurately estimate protein-ligand binding affinity remains a key challenge in computer-aided drug design (CADD). In many cases, it has been shown that the binding affinities predicted by classical scoring functions (SFs) cannot correlate well...
Proteomics Proteome Drug Development Ligands Animals Protein Binding Humans Machine Learning
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