AIMC Topic: Models, Molecular

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Data and AI-driven synthetic binding protein discovery.

Trends in pharmacological sciences Jan 3, 2025
Synthetic binding proteins (SBPs) are a class of protein binders that are artificially created and do not exist naturally. Their broad applications in tackling challenges of research, diagnostics, and therapeutics have garnered significant interest. ...
Humans Drug Discovery Protein Binding Computational Biology Artificial Intelligence Animals Protein Engineering Proteins Models, Molecular
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On the emergence of machine-learning methods in bottom-up coarse-graining.

Current opinion in structural biology Jan 2, 2025
Machine-learning methods have gained significant attention in the computational chemistry community as a viable approach to molecular modeling and analysis. Recent successes in utilizing neural networks to learn atomistic force-fields which 'coarse-g...
Machine Learning Neural Networks, Computer Thermodynamics Models, Molecular Molecular Dynamics Simulation
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DPFunc: accurately predicting protein function via deep learning with domain-guided structure information.

Nature communications Jan 2, 2025
Computational methods for predicting protein function are of great significance in understanding biological mechanisms and treating complex diseases. However, existing computational approaches of protein function prediction lack interpretability, mak...
Computational Biology Proteins Algorithms Deep Learning Databases, Protein Humans Models, Molecular Protein Domains Structure-Activity Relationship Protein Conformation
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ConoDL: a deep learning framework for rapid generation and prediction of conotoxins.

Journal of computer-aided molecular design Dec 26, 2024
Conotoxins, being small disulfide-rich and bioactive peptides, manifest notable pharmacological potential and find extensive applications. However, the exploration of conotoxins' vast molecular space using traditional methods is severely limited, nec...
Amino Acid Sequence Conotoxins Deep Learning Models, Molecular
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Predicting RNA structure and dynamics with deep learning and solution scattering.

Biophysical journal Dec 25, 2024
Advanced deep learning and statistical methods can predict structural models for RNA molecules. However, RNAs are flexible, and it remains difficult to describe their macromolecular conformations in solutions where varying conditions can induce confo...
Models, Molecular Magnesium Deep Learning Scattering, Small Angle Nucleic Acid Conformation RNA Solutions X-Ray Diffraction
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Unified Knowledge-Guided Molecular Graph Encoder with multimodal fusion and multi-task learning.

Neural networks : the official journal of the International Neural Network Society Dec 24, 2024
The remarkable success of Graph Neural Networks underscores their formidable capacity to assimilate multimodal inputs, markedly enhancing performance across a broad spectrum of domains. In the context of molecular modeling, considerable efforts have ...
Learning Models, Molecular Humans Neural Networks, Computer Machine Learning
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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...
Reinforcement Machine Learning Thermodynamics Protein Structure, Secondary Deep Learning Models, Molecular Mutation Proteins
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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 Microscopy, Atomic Force Signal-To-Noise Ratio Neural Networks, Computer Deep Learning Models, Molecular RNA Unsupervised Machine Learning Humans
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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...
Proteins Databases, Protein Deep Learning Algorithms Computational Biology Models, Molecular Protein Conformation
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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 Computational Biology Complementarity Determining Regions Single-Domain Antibodies Amino Acid Sequence Models, Molecular Humans Deep Learning
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