AIMC Topic: Models, Molecular

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QSPR modeling to predict surface tension of psychoanaleptic drugs using the hybrid DA-SVR algorithm.

Journal of molecular graphics & modelling Oct 23, 2024
A robust Quantitative Structure-Property Relationship (QSPR) model was presented to predict the surface tension property of psychoanaleptic (psychostimulant and antidepressant) drugs. A dataset of 112 molecules was utilized, and three feature selecti...
Surface Tension Support Vector Machine Algorithms Models, Molecular Least-Squares Analysis Quantitative Structure-Activity Relationship
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Geometry-Augmented Molecular Representation Learning for Property Prediction.

IEEE/ACM transactions on computational biology and bioinformatics Oct 9, 2024
Accurate molecular representation plays a crucial role in expediting the process of drug discovery. Graph neural networks (GNNs) have demonstrated robust capabilities in molecular representation learning, adept at capturing structural and spatial inf...
Machine Learning Algorithms Neural Networks, Computer Models, Molecular Drug Discovery Computational Biology
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AI Prediction of Structural Stability of Nanoproteins Based on Structures and Residue Properties by Mean Pooled Dual Graph Convolutional Network.

Interdisciplinary sciences, computational life sciences Oct 5, 2024
The structural stability of proteins is an important topic in various fields such as biotechnology, pharmaceuticals, and enzymology. Specifically, understanding the structural stability of protein is crucial for protein design. Artificial design, whi...
Artificial Intelligence Algorithms Neural Networks, Computer Protein Stability Protein Conformation Thermodynamics Proteins Models, Molecular
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Exploring the potential of structure-based deep learning approaches for T cell receptor design.

PLoS computational biology Sep 30, 2024
Deep learning methods, trained on the increasing set of available protein 3D structures and sequences, have substantially impacted the protein modeling and design field. These advancements have facilitated the creation of novel proteins, or the optim...
Receptors, Antigen, T-Cell Protein Binding Humans Deep Learning Protein Conformation Computational Biology Protein Engineering Models, Molecular
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Revolutionizing Molecular Design for Innovative Therapeutic Applications through Artificial Intelligence.

Molecules (Basel, Switzerland) Sep 29, 2024
The field of computational protein engineering has been transformed by recent advancements in machine learning, artificial intelligence, and molecular modeling, enabling the design of proteins with unprecedented precision and functionality. Computati...
Computational Biology Models, Molecular Proteins Machine Learning Humans Protein Engineering Drug Design Artificial Intelligence
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Deciphering Molecular Embeddings with Centered Kernel Alignment.

Journal of chemical information and modeling Sep 25, 2024
Analyzing machine learning models, especially nonlinear ones, poses significant challenges. In this context, centered kernel alignment (CKA) has emerged as a promising model analysis tool that assesses the similarity between two embeddings. CKA's eff...
Cheminformatics Algorithms Models, Molecular Neural Networks, Computer Machine Learning
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In Silico Insights: QSAR Modeling of TBK1 Kinase Inhibitors for Enhanced Drug Discovery.

Journal of chemical information and modeling Sep 17, 2024
TBK1, or TANK-binding kinase 1, is an enzyme that functions as a serine/threonine protein kinase. It plays a crucial role in various cellular processes, including the innate immune response to viruses, cell proliferation, apoptosis, autophagy, and an...
Humans Drug Discovery Protein Serine-Threonine Kinases Machine Learning Computer Simulation Models, Molecular Quantitative Structure-Activity Relationship Protein Kinase Inhibitors
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A Machine Learning Method for RNA-Small Molecule Binding Preference Prediction.

Journal of chemical information and modeling Sep 12, 2024
The interaction between RNA and small molecules is crucial in various biological functions. Identifying molecules targeting RNA is essential for the inhibitor design and RNA-related studies. However, traditional methods focus on learning RNA sequence...
Machine Learning Small Molecule Libraries RNA Models, Molecular Nucleic Acid Conformation
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Toward the Integration of Machine Learning and Molecular Modeling for Designing Drug Delivery Nanocarriers.

Advanced materials (Deerfield Beach, Fla.) Sep 10, 2024
The pioneering work on liposomes in the 1960s and subsequent research in controlled drug release systems significantly advances the development of nanocarriers (NCs) for drug delivery. This field is evolved to include a diverse array of nanocarriers ...
Nanoparticles Drug Carriers Drug Delivery Systems Drug Design Models, Molecular Humans Machine Learning
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Prediction of Inhibitory Activity against the MATE1 Transporter via Combined Fingerprint- and Physics-Based Machine Learning Models.

Journal of chemical information and modeling Sep 10, 2024
Renal secretion plays an important role in excretion of drug from the kidney. Two major transporters known to be highly involved in renal secretion are MATE1/2 K and OCT2, the former of which is highly related to drug-drug interactions. Among publish...
Models, Molecular Organic Cation Transport Proteins Neural Networks, Computer Machine Learning Humans
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