AIMC Topic: Proteins

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Comparative evaluation of the prediction accuracy of AlphaFold and ESMFold for monomeric and dimeric proteins.

NAR genomics and bioinformatics Jan 16, 2026
We have evaluated the prediction accuracy of three different tools, deep-learning-based AlphaFold2, AlphaFold3, and large language model-based ESMFold, utilizing the experimentally derived structures deposited in the Protein Data Bank between 2022 an...
Computational Biology Proteins Software Deep Learning Protein Conformation Protein Folding Protein Multimerization Databases, Protein Models, Molecular
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LMProtein: a protein language model based framework for protein structural property prediction.

Physical chemistry chemical physics : PCCP Jan 14, 2026
Recent advances in machine learning and self-supervised deep language modeling have made it possible to accurately predict protein structural properties. Most existing models and pretraining methods leverage evolutionary information in multiple seque...
Neural Networks, Computer Protein Conformation Models, Molecular Protein Structure, Secondary Machine Learning Amino Acid Sequence Proteins
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Computational design of protein complexes: influence of binding affinity.

Chemical communications (Cambridge, England) Jan 13, 2026
The interaction of proteins with diverse molecular partners, including other proteins, nucleic acids, and carbohydrates, is essential for performing various functions, from signal transduction and gene regulation to immune recognition and cellular tr...
DNA Models, Molecular Protein Binding RNA Machine Learning Proteins Deep Learning
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Leak Proof PDBBind: A Reorganized Data Set of Protein-Ligand Complexes for More Generalizable Binding Affinity Prediction.

The journal of physical chemistry. B Jan 4, 2026
The majority of machine learning scoring functions used in drug discovery for predicting protein-ligand binding poses and affinities have been trained on the PDBBind data set. However, it is unclear whether these new scoring functions are actually an...
Protein Binding Proteins Databases, Protein Machine Learning Molecular Docking Simulation Thermodynamics Ligands Binding Sites
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A Multimodal Drug-Target Affinity Prediction Framework with Pretrained Models and Hierarchical Graph Transformer.

Journal of chemical information and modeling Dec 31, 2025
Drug-target affinity (DTA) prediction is crucial in drug discovery. It enables researchers to elucidate the complex interaction mechanisms between candidate drugs and biological targets. However, current methods have limitations in capturing global s...
Drug Discovery Models, Molecular Neural Networks, Computer Proteins Binding Sites
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AlphaFold-RandomWalk and AlphaFold-Ensemble: Sampling Alternative Protein Conformations with Perturbed Versions of AlphaFold.

Journal of chemical information and modeling Dec 30, 2025
The ability of proteins to adopt multiple conformations is fundamental to their biological function. With the advent of AlphaFold, machine learning (ML)-based methods have extended their capabilities to more broadly sample this intrinsic conformation...
Protein Conformation Protein Folding Proteins Molecular Dynamics Simulation Machine Learning
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Data-Driven Machine Learning Framework for the Regulation of Protein Adsorption on Surfaces.

Langmuir : the ACS journal of surfaces and colloids Dec 25, 2025
Protein adsorption on surfaces is a highly complex process, governed by intricate interactions between protein, surface and surrounding environment. However, accurately predicting protein adsorption amounts and precisely controlling adsorption behavi...
Hydrophobic and Hydrophilic Interactions Surface Properties Serum Albumin, Bovine Adsorption Proteins Animals Cattle Humans Machine Learning
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Structure-Aware Heterogeneous Information Fusion Framework for Protein-Ligand Binding Affinity Prediction.

Journal of chemical information and modeling Dec 24, 2025
Accurate prediction of protein-ligand binding affinities (PLAs) is essential for drug discovery and development. Recent advancements suggest that transforming protein-ligand complexes into heterogeneous graph representations may offer a viable soluti...
Protein Binding Proteins Ligands Neural Networks, Computer Drug Discovery
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Flexible protein-ligand docking with diffusion-based side-chain packing.

Proceedings of the National Academy of Sciences of the United States of America Dec 24, 2025
Understanding protein structure and dynamics is crucial for basic biology and drug design. Conventional methods often provide static conformations that inadequately capture protein flexibility. We present PackDock, a framework that integrates deep le...
Protein Binding Proteins Molecular Docking Simulation Deep Learning Protein Conformation Binding Sites Diffusion Ligands
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Exploring Multi-Scale Interaction Features through a Physics-Aware Graph Network for Enhanced Binding Affinity Prediction.

Journal of chemical information and modeling Dec 23, 2025
Protein-ligand binding affinity plays a central role in molecular recognition and drug discovery, yet accurate prediction remains challenging due to the complexity of three-dimensional interactions. Conventional computational approaches, including do...
Ligands Protein Binding Proteins Neural Networks, Computer
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