AIMC Topic: Drug Design

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Predictive Modelling in pharmacokinetics: from in-silico simulations to personalized medicine.

Expert opinion on drug metabolism & toxicology Mar 20, 2024
INTRODUCTION: Pharmacokinetic parameters assessment is a critical aspect of drug discovery and development, yet challenges persist due to limited training data. Despite advancements in machine learning and in-silico predictions, scarcity of data hamp...
Pharmacogenetics Drug Development Drug Design Drug Discovery Models, Biological Pharmacokinetics Models, Molecular Pharmaceutical Preparations Machine Learning Computer Simulation Precision Medicine Reproducibility of Results Humans Artificial Intelligence
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PROTACable Is an Integrative Computational Pipeline of 3-D Modeling and Deep Learning To Automate the De Novo Design of PROTACs.

Journal of chemical information and modeling Mar 19, 2024
Proteolysis-targeting chimeras (PROTACs) that engage two biological targets at once are a promising technology in degrading clinically relevant protein targets. Since factors that influence the biological activities of PROTACs are more complex than t...
Proteolysis Proteolysis Targeting Chimera Drug Design Models, Molecular Deep Learning Ubiquitin-Protein Ligases
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The AI-driven Drug Design (AIDD) platform: an interactive multi-parameter optimization system integrating molecular evolution with physiologically based pharmacokinetic simulations.

Journal of computer-aided molecular design Mar 19, 2024
Computer-aided drug design has advanced rapidly in recent years, and multiple instances of in silico designed molecules advancing to the clinic have demonstrated the contribution of this field to medicine. Properly designed and implemented platforms ...
Artificial Intelligence Algorithms Drug Design Evolution, Molecular
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Explainable artificial intelligence in the design of selective carbonic anhydrase I-II inhibitors via molecular fingerprinting.

Journal of computational chemistry Mar 15, 2024
Inhibiting the enzymes carbonic anhydrase I (CA I) and carbonic anhydrase II (CA II) presents a potential avenue for addressing nervous system ailments such as glaucoma and Alzheimer's disease. Our study explored harnessing explainable artificial int...
Carbonic Anhydrase I Drug Design Molecular Structure Carbonic Anhydrase II Carbonic Anhydrase Inhibitors Humans Artificial Intelligence
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Deciphering the Lexicon of Protein Targets: A Review on Multifaceted Drug Discovery in the Era of Artificial Intelligence.

Molecular pharmaceutics Mar 11, 2024
Understanding protein sequence and structure is essential for understanding protein-protein interactions (PPIs), which are essential for many biological processes and diseases. Targeting protein binding hot spots, which regulate signaling and growth,...
Quantitative Structure-Activity Relationship Drug Design Artificial Intelligence Drug Discovery Machine Learning
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GraphsformerCPI: Graph Transformer for Compound-Protein Interaction Prediction.

Interdisciplinary sciences, computational life sciences Mar 8, 2024
Accurately predicting compound-protein interactions (CPI) is a critical task in computer-aided drug design. In recent years, the exponential growth of compound activity and biomedical data has highlighted the need for efficient and interpretable pred...
Animals Drug Design Humans Algorithms Proteins Caenorhabditis elegans Deep Learning Protein Binding
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Transfer learning empowers accurate pharmacokinetics prediction of small samples.

Drug discovery today Mar 8, 2024
Accurate assessment of pharmacokinetic (PK) properties is crucial for selecting optimal candidates and avoiding downstream failures. Transfer learning is an innovative machine learning approach enabling high-throughput prediction with limited data. R...
Machine Learning Drug Design Pharmacokinetics
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Target-specific novel molecules with their recipe: Incorporating synthesizability in the design process.

Journal of molecular graphics & modelling Feb 28, 2024
Application of Artificial intelligence (AI) in drug discovery has led to several success stories in recent times. While traditional methods mostly relied upon screening large chemical libraries for early-stage drug-design, de novo design can help ide...
Proteins Small Molecule Libraries Drug Design Artificial Intelligence Drug Discovery
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Machine Learning Accelerates De Novo Design of Antimicrobial Peptides.

Interdisciplinary sciences, computational life sciences Feb 28, 2024
Efficient and precise design of antimicrobial peptides (AMPs) is of great importance in the field of AMP development. Computing provides opportunities for peptide de novo design. In the present investigation, a new machine learning-based AMP predicti...
Antimicrobial Peptides Microbial Sensitivity Tests Machine Learning Amino Acid Sequence Drug Design
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Adapting Deep Learning QSPR Models to Specific Drug Discovery Projects.

Molecular pharmaceutics Feb 19, 2024
Medicinal chemistry and drug design efforts can be assisted by machine learning (ML) models that relate the molecular structure to compound properties. Such quantitative structure-property relationship models are generally trained on large data sets ...
Machine Learning Deep Learning Quantitative Structure-Activity Relationship Drug Design Drug Discovery
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