AIMC Topic: Proteins

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PPAP: A Protein-protein Affinity Predictor Incorporating Interfacial Contact-Aware Attention.

Journal of chemical information and modeling
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...

Cross-Modal Interaction-Aware Progressive Fusion Network for Drug-Target Interaction Prediction.

Journal of chemical information and modeling
Drug-target interaction (DTI) prediction plays a pivotal role in drug discovery. In recent years, deep learning-based models have been advanced rapidly, accelerating the identification of potential DTIs. However, how to effectively capture the cross-...

Revisiting Protein-Protein Docking: A Systematic Evaluation Framework.

Journal of chemical information and modeling
Protein-protein interactions play pivotal roles in a wide range of biological processes. Determining the atomic-level structures of protein-protein complexes is indispensable for elucidating macromolecular interaction mechanisms and advancing structu...

CGBack: Diffusion Model for Backmapping Large-Scale and Complex Coarse-Grained Molecular Systems.

Journal of chemical information and modeling
Molecular dynamics simulations based on coarse-grained (CG) models are used to accelerate conformational dynamics of biomolecules and other chemical systems with reduced computational costs. CG models achieve this by discarding atomic information nec...

Integrating Machine Learning into Free Energy Perturbation Workflows.

Journal of chemical information and modeling
Free energy perturbation (FEP) methods are among the most accurate tools in structure-based drug design for predicting protein-ligand binding affinities. However, their adoption remains limited due to high computational demands and complex setup proc...

Bioactivity Deep Learning for Complex Structure-Free Compound-Protein Interaction Prediction.

Journal of chemical information and modeling
Protein-ligand binding affinity assessment plays a pivotal role in virtual drug screening, yet conventional data-driven approaches rely heavily on limited protein-ligand crystal structures. Structure-free compound-protein interaction (CPI) methods ha...

Sampling and Ranking of Protein Conformations Using Machine Learning Techniques Do Not Improve the Quality of Rigid Protein-Protein Docking.

Journal of chemical information and modeling
Rigid docking remains the most popular method of predicting protein-protein interactions in cases when experimental 3D structures of the complexes are not available. The docking often relies on known unbound (Apo) protein structures, which may differ...

HitScreen: A Sequence-Based Drug Virtual Screening Approach Using Data Augmentation and Protein Language Models.

Journal of chemical information and modeling
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...

Precision in Predicting Protein-Nucleic Acid Complexes: Establishing a Benchmark Data Set and Comparative Metrics.

Journal of chemical information and modeling
Protein-nucleic acid interactions are fundamental to biological processes and biotechnology, yet their computational prediction lags behind protein structure or protein-protein interaction modeling. This study introduces ProNASet, a benchmark data se...

Exploring the Frontiers of Computational NMR: Methods, Applications, and Challenges.

Chemical reviews
Computational methods have revolutionized NMR spectroscopy, driving significant advancements in structural biology and related fields. This review focuses on recent developments in quantum chemical and machine learning approaches for computational NM...