AIMC Topic: Drug Design

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Rational design of potent phosphopeptide binders to endocrine Snk PBD domain by integrating machine learning optimization, molecular dynamics simulation, binding energetics rescoring, and in vitro affinity assay.

European biophysics journal : EBJ Nov 29, 2024
Human Snk is an evolutionarily conserved serine/threonine kinase essential for the maintenance of endocrine stability. The protein consists of a N-terminal catalytic domain and a C-terminal polo-box domain (PBD) that determines subcellular localizati...
Humans Machine Learning Drug Design Protein Binding Protein Serine-Threonine Kinases Molecular Dynamics Simulation Amino Acid Sequence Protein Domains Thermodynamics Phosphopeptides
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Integrating machine learning and multitargeted drug design to combat antimicrobial resistance: a systematic review.

Journal of drug targeting Nov 26, 2024
Antimicrobial resistance (AMR) is a critical global health challenge, undermining the efficacy of antimicrobial drugs against microorganisms like bacteria, fungi and viruses. Multidrug resistance (MDR) arises when microorganisms become resistant to m...
Enterobacteriaceae Humans Machine Learning Drug Design Drug Resistance, Multiple, Bacterial Anti-Bacterial Agents
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Integrated machine learning and physics-based methods assisted de novo design of Fatty Acyl-CoA synthase inhibitors.

Expert opinion on drug discovery Nov 25, 2024
BACKGROUND: Tuberculosis is an infectious disease that has become endemic worldwide. The causative bacteria (Mtb) is targeted via several exciting drug targets. One newly discovered target is the Fatty Acyl-CoA synthase, which plays a significant ro...
Mycobacterium tuberculosis Machine Learning Enzyme Inhibitors Coenzyme A Ligases Algorithms Lipid Metabolism Humans Tuberculosis Drug Design Antitubercular Agents Molecular Docking Simulation
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"Several birds with one stone": exploring the potential of AI methods for multi-target drug design.

Molecular diversity Nov 24, 2024
Drug discovery is a time-consuming and expensive process. Artificial intelligence (AI) methodologies have been adopted to cut costs and speed up the drug development process, serving as promising in silico approaches to efficiently design novel drug ...
Artificial Intelligence Drug Design Drug Discovery Humans
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Drug Discovery in the Age of Artificial Intelligence: Transformative Target-Based Approaches.

International journal of molecular sciences Nov 14, 2024
The complexities inherent in drug development are multi-faceted and often hamper accuracy, speed and efficiency, thereby limiting success. This review explores how recent developments in machine learning (ML) are significantly impacting target-based ...
Neural Networks, Computer Artificial Intelligence Humans Drug Design Machine Learning Drug Discovery Algorithms Deep Learning
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Machine learning-based rational design for efficient discovery of allatostatin analogs as promising lead candidates for novel IGRs.

Pest management science Nov 8, 2024
BACKGROUND: Insect neuropeptide allatostatins (ASTs) play a vital role in regulating insect growth, development, and reproduction, making them potential candidates for new insect growth regulators (IGRs). However, the practical use of natural ASTs in...
Machine Learning Juvenile Hormones Drug Discovery Structure-Activity Relationship Neuropeptides Animals Drug Design
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A deep learning approach for rational ligand generation with toxicity control via reactive building blocks.

Nature computational science Nov 8, 2024
Deep generative models are gaining attention in the field of de novo drug design. However, the rational design of ligand molecules for novel targets remains challenging, particularly in controlling the properties of the generated molecules. Here, ins...
Neural Networks, Computer Algorithms Drug Design Humans Bayes Theorem Ligands Deep Learning Proteins
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Molecular mechanism underlying effect of D93 and D289 protonation states on inhibitor-BACE1 binding: exploration from multiple independent Gaussian accelerated molecular dynamics and deep learning.

SAR and QSAR in environmental research Nov 8, 2024
BACE1 has been regarded as an essential drug design target for treating Alzheimer's disease (AD). Multiple independent Gaussian accelerated molecular dynamics simulations (GaMD), deep learning (DL), and molecular mechanics general Born surface area (...
Amyloid Precursor Protein Secretases Aspartic Acid Endopeptidases Quantitative Structure-Activity Relationship Deep Learning Drug Design Molecular Dynamics Simulation Hydrogen Bonding Protons
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Antiviral Peptide-Generative Pre-Trained Transformer (AVP-GPT): A Deep Learning-Powered Model for Antiviral Peptide Design with High-Throughput Discovery and Exceptional Potency.

Viruses Oct 25, 2024
Traditional antiviral peptide (AVP) discovery is a time-consuming and expensive process. This study introduces AVP-GPT, a novel deep learning method utilizing transformer-based language models and multimodal architectures specifically designed for AV...
Deep Learning Antiviral Agents Respiratory Syncytial Viruses Drug Design Humans Drug Discovery High-Throughput Screening Assays Peptides Influenza A virus
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Towards novel small-molecule inhibitors blocking PD-1/PD-L1 pathway: From explainable machine learning models to molecular dynamics simulation.

International journal of biological macromolecules Oct 15, 2024
Molecular design of small-molecule inhibitors targeting programmed cell death-1 (PD-1)/programmed cell death ligand-1 (PD-L1) pathway has been recognized as an active research area by the clinical success of cancer immunotherapy. In recent years, usi...
Humans Molecular Docking Simulation Machine Learning Drug Design Immune Checkpoint Inhibitors Programmed Cell Death 1 Receptor Small Molecule Libraries B7-H1 Antigen Molecular Dynamics Simulation Ligands Signal Transduction
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