AIMC Topic: Models, Molecular

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Enhancing accuracy of virtual kinase profiling via application of graph neural network to 3D pharmacophore ensembles.

Journal of computer-aided molecular design Sep 26, 2025
Kinase profiling is an essential step in both hit identification and selectivity evaluation. Since in vitro testing of large chemical libraries is costly and time-consuming, a computational approach can be applied to narrow down the reasonable chemic...
Neural Networks, Computer Machine Learning Humans Small Molecule Libraries Graph Neural Networks Pharmacophore Drug Design Drug Discovery Models, Molecular Protein Kinase Inhibitors
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PPAP: A Protein-protein Affinity Predictor Incorporating Interfacial Contact-Aware Attention.

Journal of chemical information and modeling Sep 19, 2025
Protein-protein interactions (PPIs) play fundamental roles in biological processes and therapeutic development. Accurately predicting PPI binding affinity is critical for understanding interaction mechanisms and guiding protein engineering. Recent ad...
Protein Binding Proteins Models, Molecular Protein Interaction Mapping Deep Learning
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Partner-RBR: Predicting Multitype RNA-Binding Residues Based on Mutual Learning.

Journal of chemical information and modeling Sep 16, 2025
RNA molecules play diverse and critical roles in various biological processes, including gene expression, post-transcriptional regulation, and disease pathogenesis. Understanding the interaction between proteins and RNA necessitates the precise ident...
RNA-Binding Proteins Databases, Protein Machine Learning Protein Binding Computational Biology Binding Sites Sequence Alignment Models, Molecular RNA
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HitScreen: A Sequence-Based Drug Virtual Screening Approach Using Data Augmentation and Protein Language Models.

Journal of chemical information and modeling Sep 16, 2025
Sequence-based drug-target interaction (DTI) prediction is an effective approach for identifying potential drug candidates without relying on three-dimensional protein structures. However, current sequence-based methods often suffer from limited gene...
Ligands Models, Molecular Drug Evaluation, Preclinical Deep Learning Proteins
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Parametrically guided design of beta barrels and transmembrane nanopores using deep learning.

Proceedings of the National Academy of Sciences of the United States of America Sep 15, 2025
Francis Crick's global parameterization of coiled coil geometry has been widely useful for guiding design of new protein structures and functions. However, design guided by similar global parameterization of beta barrel structures has been less succe...
Protein Conformation, beta-Strand Nanopores Models, Molecular Deep Learning Protein Folding
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Predicting HOMO-LUMO Gaps Using Hartree-Fock Calculated Data and Machine Learning Models.

Journal of chemical information and modeling Sep 10, 2025
The calculation of the highest occupied molecular orbital-lowest unoccupied molecular orbital (HOMO-LUMO) gap for chemical molecules is computationally intensive using quantum mechanics (QM) methods, while experimental determination is often costly a...
Machine Learning Models, Molecular Quantum Theory
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A Machine Learning Model for the Proteome-Wide Prediction of Lipid-Interacting Proteins.

Journal of chemical information and modeling Sep 4, 2025
Lipids are essential metabolites that play critical roles in multiple cellular pathways. Like many primary metabolites, mutations that disrupt lipid synthesis can be lethal. Proteins involved in lipid synthesis, trafficking, and modification, are tar...
Hydrophobic and Hydrophilic Interactions Binding Sites Proteins Models, Molecular Proteome Lipids Humans Protein Binding Machine Learning Computational Biology Algorithms
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MGRL-DDI: Multiview Graph Representation Learning for Accurate Drug-Drug Interaction Prediction.

Journal of chemical information and modeling Sep 3, 2025
Drug-drug interactions (DDIs) present a significant challenge in clinical practice, as they may lead to adverse reactions, diminished therapeutic efficacy, and serious risks to patient safety. However, most existing methods depend on single-view repr...
Models, Molecular Pharmaceutical Preparations Humans Machine Learning Drug Interactions
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Graph Learning-Based Scoring of RNA-Protein Complex Structures.

Journal of chemical theory and computation Aug 29, 2025
Development of suitable scoring functions is essential for the prediction of RNA-protein complex structures. Conventional statistical potential-based scoring functions suffered from deficiencies in handling conformational flexibility. The recent appl...
Neural Networks, Computer Nucleic Acid Conformation Models, Molecular Algorithms Deep Learning RNA Proteins
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MOFGPT: Generative Design of Metal-Organic Frameworks using Language Models.

Journal of chemical information and modeling Aug 28, 2025
The discovery of Metal-Organic Frameworks (MOFs) with application-specific properties remains a central challenge in materials chemistry, owing to the immense size and complexity of their structural design space. Conventional computational screening ...
Models, Molecular Machine Learning Metal-Organic Frameworks
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