AIMC Topic: Binding Sites

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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...
Thermodynamics Protein Binding Proteins Ligands Binding Sites Machine Learning Molecular Docking Simulation Databases, Protein
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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 Proteins Models, Molecular Neural Networks, Computer Binding Sites
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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 Diffusion Molecular Docking Simulation Deep Learning Binding Sites Ligands Protein Conformation
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A Comparative Study of Deep Learning and Classical Modeling Approaches for Protein-Ligand Binding Pose and Affinity Prediction in Coronavirus Main Proteases.

Journal of chemical information and modeling Dec 22, 2025
The accurate prediction of protein-ligand binding poses and affinities is central to structure-based drug design. In this study, we first benchmarked three distinct pose generation strategies for data sets from the ASAP Antiviral Challenge 2025: mole...
Middle East Respiratory Syndrome Coronavirus Binding Sites Coronavirus 3C Proteases Ligands Protein Conformation SARS-CoV-2 Molecular Docking Simulation Antiviral Agents Protein Binding Deep Learning Humans
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SSIF-Affinity: Multimodal Deep Learning of Sequence-Structure Features for Precise Protein-Protein Binding Affinity Prediction.

Journal of chemical information and modeling Dec 15, 2025
Quantitative prediction of binding affinity in protein-protein interactions is critical for deciphering biological mechanisms and advancing therapeutic antibody development. While experimental methods for measuring binding affinity remain limited by ...
Deep Learning Proteins Protein Binding Models, Molecular Binding Sites
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Accurate prediction of protein-ATP binding sites based on a protein pretrained large language model and a fractional-order convolutional neural network.

Scientific reports Nov 25, 2025
ATP, a high-energy phosphate compound also known as adenosine triphosphate, serves as a direct energy source for living organisms. Proteins, composed of amino acids, are fundamental macromolecules and essential building blocks of life. The interactio...
Binding Sites Large Language Models Proteins Adenosine Triphosphate Algorithms Neural Networks, Computer Protein Binding Convolutional Neural Networks Deep Learning Computational Biology
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A multi-geometric graph fusion network for protein-ligand affinity prediction.

Physical chemistry chemical physics : PCCP Nov 19, 2025
Protein-ligand binding affinity prediction plays a crucial role in drug discovery. While recent works use two-dimensional graph neural networks to improve affinity prediction, we find that the three-dimensional geometric information of proteins and l...
Neural Networks, Computer Protein Binding Protein Conformation Binding Sites Proteins Ligands
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RAPID-Net: Accurate Pocket Identification for Binding-Site-Agnostic Docking.

Journal of chemical information and modeling Nov 13, 2025
Accurate identification of druggable pockets and their features is essential for structure-based drug design and effective downstream docking. Here, we present RAPID-Net, a deep learning-based algorithm designed for accurate prediction of binding poc...
Proteins Ligands Humans Algorithms Binding Sites Drug Design SARS-CoV-2 Deep Learning Protein Binding Molecular Docking Simulation
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OneProt: Towards multi-modal protein foundation models via latent space alignment of sequence, structure, binding sites and text encoders.

PLoS computational biology Nov 13, 2025
Recent advances in Artificial Intelligence have enabled multi-modal systems to model and translate diverse information spaces. Extending beyond text and vision, we introduce OneProt, a multi-modal Deep Learning model for proteins that integrates stru...
Computational Biology Sequence Analysis, Protein Proteins Neural Networks, Computer Binding Sites Databases, Protein Deep Learning Sequence Alignment
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Design of Carbon Nanotube Inhibitors for Main Proteinase of SARS-CoV-2: A Combined Deep Learning and Molecular Dynamics Simulation Study.

The journal of physical chemistry. B Nov 10, 2025
The rapid development of machine learning (ML) and deep learning (DL) methods provides new opportunities for innovative drug discovery. While these techniques are widely used in docking organic molecules (drugs) with protein, an evaluation of the per...
Molecular Dynamics Simulation Nanotubes, Carbon Protease Inhibitors Coronavirus 3C Proteases Binding Sites Humans SARS-CoV-2 Drug Design Deep Learning Molecular Docking Simulation
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