AIMC Topic: Protein Binding

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Using Machine Learning to Analyze Molecular Dynamics Simulations of Biomolecules.

The journal of physical chemistry. B
Machine learning (ML) techniques have become powerful tools in both industrial and academic settings. Their ability to facilitate analysis of complex data and generation of predictive insights is transforming how scientific problems are approached ac...

A hybrid protocol for peptide development: integrating deep generative models and physics simulations for biomolecular design targeting IL23R/IL23.

International journal of biological macromolecules
Recent advances in machine learning have revolutionized molecular design; however, a gap remains in integrating generative models with physics-based simulations to develop functional modulators, such as stable peptides, for challenging targets like t...

Computational methods for modeling protein-protein interactions in the AI era: Current status and future directions.

Drug discovery today
The modeling of protein-protein interactions (PPIs) has been revolutionized by artificial intelligence, with deep learning and end-to-end frameworks such as AlphaFold and its derivatives now dominating the field. This review surveys the current compu...

Predicted and Explained: Transforming drug discovery with AI for high-precision receptor-ligand interaction modeling and binding analysis.

Computers in biology and medicine
The pharmaceutical industry faces persistent challenges in developing effective treatments for complex diseases, creating an urgent need for innovative approaches to accelerate drug discovery. A pivotal factor in this process is the accurate predicti...

Design and molecular mechanism investigation of ALK inhibitors based on virtual screening and structural descriptor modeling.

Journal of receptor and signal transduction research
To address the challenges of target specificity and drug resistance in Anaplastic lymphoma kinase (ALK) inhibition, this study conducted a virtual screening of the BindingDB database, yielding 711 potential ALK inhibitors. Four QSAR models were estab...

Deciphering the mechanism of baicalein in cervical cancer via bioinformatics, machine learning and computational simulations: PIM1 and CDK2 are key target proteins.

International journal of biological macromolecules
Cervical cancer is one of the leading causes of death among women worldwide. Current treatments are limited by chemoresistance and chemotherapeutic agents' adverse effects, prompting the search for better therapeutic alternatives. Baicalein, a natura...

DeepDTAGen: a multitask deep learning framework for drug-target affinity prediction and target-aware drugs generation.

Nature communications
Identifying novel drugs that can interact with target proteins is a highly challenging, time-consuming, and costly task in drug discovery and development. Numerous machine learning-based models have recently been utilized to accelerate the drug disco...

Drug repurposing targeting COVID-19 3CL protease using molecular docking and machine learning regression approaches.

Scientific reports
The COVID-19 pandemic has initiated a global health emergency, with an exigent need for an effective cure. Progressively, drug repurposing is emerging as a promising solution for saving time, cost, and labor. However, the number of drug candidates th...

TIDGN: A Transfer Learning Framework for Predicting Interactions of Intrinsically Disordered Proteins with High Conformational Dynamics.

Journal of chemical information and modeling
Interactions between intrinsically disordered proteins (IDPs) are crucial for biological processes, such as intracellular liquid-liquid phase separation (LLPS). Experiments (e.g., NMR) and simulations used to study IDP interactions encounter a variet...

CrypTothML: An Integrated Mixed-Solvent Molecular Dynamics Simulation and Machine Learning Approach for Cryptic Site Prediction.

International journal of molecular sciences
Cryptic sites, which are transient binding sites that emerge through protein conformational changes upon ligand binding, are valuable targets for drug discovery, particularly for allosteric modulators. However, identifying these sites remains challen...