AI Medical Compendium Journal:
Physical chemistry chemical physics : PCCP

Showing 11 to 20 of 37 articles

A low-cost machine learning framework for predicting drug-drug interactions based on fusion of multiple features and a parameter self-tuning strategy.

Physical chemistry chemical physics : PCCP Feb 14, 2024
Poly-drug therapy is now recognized as a crucial treatment, and the analysis of drug-drug interactions (DDIs) offers substantial theoretical support and guidance for its implementation. Predicting potential DDIs using intelligent algorithms is an eme...
Machine Learning Algorithms Drug Interactions
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Evaluation of DNA-protein complex structures using the deep learning method.

Physical chemistry chemical physics : PCCP Dec 21, 2023
Biological processes such as transcription, repair, and regulation require interactions between DNA and proteins. To unravel their functions, it is imperative to determine the high-resolution structures of DNA-protein complexes. However, experimental...
Neural Networks, Computer Research Design Deep Learning Protein Binding Proteins DNA
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Discovery of potential RIPK1 inhibitors by machine learning and molecular dynamics simulations.

Physical chemistry chemical physics : PCCP Nov 22, 2023
Receptor-interacting serine/threonine-protein kinase 1 (RIPK1) plays a crucial role in inflammation and cell death, so it is a promising candidate for the treatment of autoimmune, inflammatory, neurodegenerative, and ischemic diseases. So far, there ...
Machine Learning Molecular Docking Simulation Molecular Dynamics Simulation
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Ligand binding affinity prediction with fusion of graph neural networks and 3D structure-based complex graph.

Physical chemistry chemical physics : PCCP Sep 13, 2023
Accurate prediction of protein-ligand binding affinity is pivotal for drug design and discovery. Here, we proposed a novel deep fusion graph neural networks framework named FGNN to learn the protein-ligand interactions from the 3D structures of prote...
Neural Networks, Computer Algorithms Drug Design Computational Biology Ligands
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Study on the allosteric activation mechanism of SHP2 elastic network models and neural relational inference molecular dynamics simulation.

Physical chemistry chemical physics : PCCP Sep 13, 2023
As a ubiquitous protein tyrosine phosphatase, SHP2 is involved in PD-1/PD-L1 mediated tumor immune escape and undergoes substantial conformational changes. Therefore, it is considered an ideal target for tumor intervention. However, the allosteric me...
Artificial Intelligence Molecular Dynamics Simulation Programmed Cell Death 1 Receptor Allosteric Regulation Protein Tyrosine Phosphatase, Non-Receptor Type 11
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Simplified and enhanced VCD analysis of cyclic peptides guided by artificial intelligence.

Physical chemistry chemical physics : PCCP Aug 23, 2023
Cyclic peptides are privileged structures in medicinal chemistry; however, their solution-state structure characterization is difficult. Vibrational circular dichroism (VCD) spectroscopy is a powerful alternative to NMR, but requires challenging calc...
Artificial Intelligence Peptides, Cyclic Circular Dichroism
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An artificial neural network model to predict structure-based protein-protein free energy of binding from Rosetta-calculated properties.

Physical chemistry chemical physics : PCCP Mar 8, 2023
The prediction of the free energy (Δ) of binding for protein-protein complexes is of general scientific interest as it has a variety of applications in the fields of molecular and chemical biology, materials science, and biotechnology. Despite its ce...
Neural Networks, Computer Protein Binding Proteins Thermodynamics Entropy
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Transformer-based deep learning method for optimizing ADMET properties of lead compounds.

Physical chemistry chemical physics : PCCP Jan 18, 2023
A successful drug needs to exhibit both effective pharmacodynamics (PD) and safe pharmacokinetics (PK). However, the coordinated optimization of PD and PK properties in molecule generation tasks remains a great challenge for most existing methods, es...
Molecular Docking Simulation Drug Discovery Deep Learning
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Combination of explainable machine learning and conceptual density functional theory: applications for the study of key solvation mechanisms.

Physical chemistry chemical physics : PCCP Nov 30, 2022
We present explainable machine learning approaches for the accurate prediction and understanding of solvation free energies, enthalpies, and entropies for different salts in various protic and aprotic solvents. As key input features, we use fundament...
Machine Learning Electronics Solvents Salts Entropy
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Capturing the potential energy landscape of large size molecular clusters from atomic interactions up to a 4-body system using deep learning.

Physical chemistry chemical physics : PCCP Nov 18, 2022
Exploring the structure and properties of molecular clusters with accuracy using the methods is a resource intensive task due to the increasing cost of the methods and the number of distinct conformers as the size increases. The energy landscape of...
Deep Learning Methanol Hydrogen
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