AIMC Topic: Models, Molecular

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Protein ligand structure prediction: From empirical to deep learning approaches.

Current opinion in structural biology Feb 5, 2025
Protein-ligand structure prediction methods, aiming to predict the three-dimensional complex structure and binding energy of a compound and target protein, are essential in many structure-based drug discovery pipelines, including virtual screening an...
Models, Molecular Ligands Protein Conformation Deep Learning Protein Binding Computational Biology Proteins Drug Discovery
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PDB-IHM: A System for Deposition, Curation, Validation, and Dissemination of Integrative Structures.

Journal of molecular biology Jan 27, 2025
Structures of many large biomolecular assemblies are now being determined using integrative approaches. In these approaches, information derived from multiple experimental and computational methods is combined to compute three-dimensional structures ...
Software Computational Biology Proteins Protein Conformation Data Curation Databases, Protein Models, Molecular
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Transformer Decoder Learns from a Pretrained Protein Language Model to Generate Ligands with High Affinity.

Journal of chemical information and modeling Jan 27, 2025
The drug discovery process can be significantly accelerated by using deep learning methods to suggest molecules with druglike features and, more importantly, that are good candidates to bind specific proteins of interest. We present a novel deep lear...
Models, Molecular Ligands Protein Binding Drug Discovery Proteins Deep Learning
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AI-based methods for biomolecular structure modeling for Cryo-EM.

Current opinion in structural biology Jan 27, 2025
Cryo-electron microscopy (Cryo-EM) has revolutionized structural biology by enabling the determination of macromolecular structures that were challenging to study with conventional methods. Processing cryo-EM data involves several computational steps...
Cryoelectron Microscopy Artificial Intelligence Models, Molecular Deep Learning Macromolecular Substances
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A multiscale molecular structural neural network for molecular property prediction.

Molecular diversity Jan 25, 2025
Molecular Property Prediction (MPP) is a fundamental task in important research fields such as chemistry, materials, biology, and medicine, where traditional computational chemistry methods based on quantum mechanics often consume substantial time an...
Molecular Structure Neural Networks, Computer Machine Learning Algorithms Models, Molecular
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Challenges and compromises: Predicting unbound antibody structures with deep learning.

Current opinion in structural biology Jan 24, 2025
Therapeutic antibodies are manufactured, stored and administered in the free state; this makes understanding the unbound form key to designing and improving development pipelines. Prediction of unbound antibodies is challenging, specifically modellin...
Humans Models, Molecular Antibodies Deep Learning Protein Conformation Protein Binding
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Phyre2.2: A Community Resource for Template-based Protein Structure Prediction.

Journal of molecular biology Jan 23, 2025
Template-based modelling, also known as homology modelling, is a powerful approach to predict the structure of a protein from its amino acid sequence. The approach requires one to identify a sequence similarity between the query sequence and that of ...
Databases, Protein Models, Molecular Computational Biology Proteins Amino Acid Sequence Protein Conformation Software
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MVGNN-PPIS: A novel multi-view graph neural network for protein-protein interaction sites prediction based on Alphafold3-predicted structures and transfer learning.

International journal of biological macromolecules Jan 21, 2025
Protein-protein interactions (PPI) are crucial for understanding numerous biological processes and pathogenic mechanisms. Identifying interaction sites is essential for biomedical research and targeted drug development. Compared to experimental metho...
Proteins Models, Molecular Protein Conformation Protein Binding Binding Sites Protein Interaction Mapping Neural Networks, Computer Computational Biology Graph Neural Networks Algorithms
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Deciphering Protein Secondary Structures and Nucleic Acids in Cryo-EM Maps Using Deep Learning.

Journal of chemical information and modeling Jan 21, 2025
With the resolution revolution of cryo-electron microscopy (cryo-EM) and the rapid development of image processing technology, cryo-EM has become an indispensable experimental method for determining the three-dimensional structures of biological macr...
Models, Molecular Nucleic Acids Deep Learning Protein Structure, Secondary Cryoelectron Microscopy Proteins
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General structure-activity relationship models for the inhibitors of Adenosine receptors: A machine learning approach.

Molecular diversity Jan 20, 2025
Adenosine receptors (A, A, A, A) play critical roles in cellular signaling and are implicated in various physiological and pathological processes, including inflammations and cancer. The main aim of this research was to investigate structure-activity...
Neural Networks, Computer Machine Learning Humans Models, Molecular Receptors, Purinergic P1 Purinergic P1 Receptor Antagonists Algorithms Structure-Activity Relationship
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