AIMC Topic: Drug Design

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De Novo Molecular Design with Chemical Language Models.

Methods in molecular biology (Clifton, N.J.) Jan 1, 2022
Artificial intelligence (AI) offers new possibilities for hit and lead finding in medicinal chemistry. Several instances of AI have been used for prospective de novo drug design. Among these, chemical language models have been shown to perform well i...
Models, Chemical Language Drug Design Artificial Intelligence Prospective Studies Neural Networks, Computer
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Has Artificial Intelligence Impacted Drug Discovery?

Methods in molecular biology (Clifton, N.J.) Jan 1, 2022
Artificial intelligence (AI) tools find increasing application in drug discovery supporting every stage of the Design-Make-Test-Analyse (DMTA) cycle. The main focus of this chapter is the application in molecular generation with the aid of deep neura...
Neural Networks, Computer Drug Discovery Artificial Intelligence Drug Design
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Applications of Artificial Intelligence in Drug Design: Opportunities and Challenges.

Methods in molecular biology (Clifton, N.J.) Jan 1, 2022
Artificial intelligence (AI) has undergone rapid development in recent years and has been successfully applied to real-world problems such as drug design. In this chapter, we review recent applications of AI to problems in drug design including virtu...
Artificial Intelligence Drug Design
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Mol2Context-vec: learning molecular representation from context awareness for drug discovery.

Briefings in bioinformatics Nov 5, 2021
With the rapid development of proteomics and the rapid increase of target molecules for drug action, computer-aided drug design (CADD) has become a basic task in drug discovery. One of the key challenges in CADD is molecular representation. High-qual...
Deep Learning Structure-Activity Relationship Quantum Theory Cheminformatics Models, Molecular Drug Design Drug Discovery Humans Algorithms
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Molecular design in drug discovery: a comprehensive review of deep generative models.

Briefings in bioinformatics Nov 5, 2021
Deep generative models have been an upsurge in the deep learning community since they were proposed. These models are designed for generating new synthetic data including images, videos and texts by fitting the data approximate distributions. In the ...
Deep Learning Models, Molecular Drug Design Drug Discovery
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De novo generation of dual-target ligands using adversarial training and reinforcement learning.

Briefings in bioinformatics Nov 5, 2021
Artificial intelligence, such as deep generative methods, represents a promising solution to de novo design of molecules with the desired properties. However, generating new molecules with biological activities toward two specific targets remains an ...
Biomarkers Drug Design Drug Discovery Artificial Intelligence Algorithms Chemical Phenomena Databases, Pharmaceutical Structure-Activity Relationship Ligands Deep Learning Proteins
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Protein-ligand binding affinity prediction model based on graph attention network.

Mathematical biosciences and engineering : MBE Oct 25, 2021
Estimating the binding affinity between proteins and drugs is very important in the application of structure-based drug design. Currently, applying machine learning to build the protein-ligand binding affinity prediction model, which is helpful to im...
Machine Learning Drug Design Proteins Ligands Protein Binding
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Hyperbolic relational graph convolution networks plus: a simple but highly efficient QSAR-modeling method.

Briefings in bioinformatics Sep 2, 2021
Accurate predictions of druggability and bioactivities of compounds are desirable to reduce the high cost and time of drug discovery. After more than five decades of continuing developments, quantitative structure-activity relationship (QSAR) methods...
Computer Graphics Models, Chemical Quantitative Structure-Activity Relationship Drug Design Algorithms Organic Chemicals Neural Networks, Computer Drug Discovery Computer Simulation Computational Biology Artificial Intelligence Molecular Structure
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Persistent spectral hypergraph based machine learning (PSH-ML) for protein-ligand binding affinity prediction.

Briefings in bioinformatics Sep 2, 2021
Molecular descriptors are essential to not only quantitative structure activity/property relationship (QSAR/QSPR) models, but also machine learning based chemical and biological data analysis. In this paper, we propose persistent spectral hypergraph ...
Molecular Structure Pharmaceutical Preparations Quantitative Structure-Activity Relationship Binding, Competitive Ligands Databases, Protein Models, Molecular Humans Computational Biology Proteins Drug Design Drug Discovery Machine Learning Protein Binding Algorithms
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Hypergraph-based persistent cohomology (HPC) for molecular representations in drug design.

Briefings in bioinformatics Sep 2, 2021
Artificial intelligence (AI) based drug design has demonstrated great potential to fundamentally change the pharmaceutical industries. Currently, a key issue in AI-based drug design is efficient transferable molecular descriptors or fingerprints. Her...
Databases, Protein Machine Learning Models, Chemical Drug Design
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