AIMC Topic: Models, Molecular

Clear Filters Showing 141 to 150 of 759 articles

A 4D tensor-enhanced multi-dimensional convolutional neural network for accurate prediction of protein-ligand binding affinity.

Molecular diversity Dec 23, 2024
Protein-ligand interactions are the molecular basis of many important cellular activities, such as gene regulation, cell metabolism, and signal transduction. Protein-ligand binding affinity is a crucial metric of the strength of the interaction betwe...
Deep Learning Convolutional Neural Networks Neural Networks, Computer Models, Molecular Ligands Binding Sites Protein Binding Proteins
View on PubMed DOI

AlphaMut: A Deep Reinforcement Learning Model to Suggest Helix-Disrupting Mutations.

Journal of chemical theory and computation Dec 19, 2024
Helices are important secondary structural motifs within proteins and are pivotal in numerous physiological processes. While amino acids (AA) such as alanine and leucine are known to promote helix formation, proline and glycine disfavor it. Helical s...
Deep Learning Proteins Thermodynamics Protein Structure, Secondary Reinforcement Machine Learning Models, Molecular Mutation
View on PubMed DOI

Determining structures of RNA conformers using AFM and deep neural networks.

Nature Dec 18, 2024
Much of the human genome is transcribed into RNAs, many of which contain structural elements that are important for their function. Such RNA molecules-including those that are structured and well-folded-are conformationally heterogeneous and flexible...
Nucleic Acid Conformation Models, Molecular RNA Microscopy, Atomic Force Signal-To-Noise Ratio Deep Learning Humans Unsupervised Machine Learning Neural Networks, Computer
View on PubMed DOI

Prediction of Inter-Residue Multiple Distances and Exploration of Protein Multiple Conformations by Deep Learning.

IEEE/ACM transactions on computational biology and bioinformatics Dec 10, 2024
AlphaFold2 has achieved a major breakthrough in end-to-end prediction for static protein structures. However, protein conformational change is considered to be a key factor in protein biological function. Inter-residue multiple distances prediction i...
Models, Molecular Protein Conformation Deep Learning Computational Biology Proteins Databases, Protein Algorithms
View on PubMed DOI

ParaAntiProt provides paratope prediction using antibody and protein language models.

Scientific reports Nov 25, 2024
Efficiently predicting the paratope holds immense potential for enhancing antibody design, treating cancers and other serious diseases, and advancing personalized medicine. Although traditional methods are highly accurate, they are often time-consumi...
Antibodies Amino Acid Sequence Computational Biology Single-Domain Antibodies Humans Models, Molecular Complementarity Determining Regions Deep Learning
View on PubMed DOI

AABBA Graph Kernel: Atom-Atom, Bond-Bond, and Bond-Atom Autocorrelations for Machine Learning.

Journal of chemical information and modeling Nov 24, 2024
Graphs are one of the most natural and powerful representations available for molecules; natural because they have an intuitive correspondence to skeletal formulas, the language used by chemists worldwide, and powerful, because they are highly expres...
Machine Learning Models, Molecular
View on PubMed DOI

Accurate RNA 3D structure prediction using a language model-based deep learning approach.

Nature methods Nov 21, 2024
Accurate prediction of RNA three-dimensional (3D) structures remains an unsolved challenge. Determining RNA 3D structures is crucial for understanding their functions and informing RNA-targeting drug development and synthetic biology design. The stru...
Humans Software Models, Molecular Deep Learning RNA Computational Biology Nucleic Acid Conformation
View on PubMed DOI

ProAffinity-GNN: A Novel Approach to Structure-Based Protein-Protein Binding Affinity Prediction via a Curated Data Set and Graph Neural Networks.

Journal of chemical information and modeling Nov 18, 2024
Protein-protein interactions (PPIs) are crucial for understanding biological processes and disease mechanisms, contributing significantly to advances in protein engineering and drug discovery. The accurate determination of binding affinities, essenti...
Models, Molecular Protein Conformation Protein Interaction Mapping Proteins Databases, Protein Neural Networks, Computer Protein Binding
View on PubMed DOI

Rapid prediction of key residues for foldability by machine learning model enables the design of highly functional libraries with hyperstable constrained peptide scaffolds.

PLoS computational biology Nov 18, 2024
Peptides are an emerging modality for developing therapeutics that can either agonize or antagonize cellular pathways associated with disease, yet peptides often suffer from poor chemical and physical stability, which limits their potential. However,...
Machine Learning Amino Acid Sequence Disulfides Computational Biology Protein Folding Peptides Models, Molecular Peptide Library
View on PubMed DOI

A Divide-and-Conquer Approach to Nanoparticle Global Optimisation Using Machine Learning.

Journal of chemical information and modeling Nov 15, 2024
Global optimization of the structure of atomic nanoparticles is often hampered by the presence of many funnels on the potential energy surface. While broad funnels are readily encountered and easily exploited by the search, narrow funnels are more di...
Thermodynamics Models, Molecular Metal Nanoparticles Machine Learning Nanoparticles
View on PubMed DOI
Popular Topics
Recent Journals