AI Medical Compendium Topic:
Ligands

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Computational Ion Channel Research: from the Application of Artificial Intelligence to Molecular Dynamics Simulations.

Cellular physiology and biochemistry : international journal of experimental cellular physiology, biochemistry, and pharmacology
Although ion channels are crucial in many physiological processes and constitute an important class of drug targets, much is still unclear about their function and possible malfunctions that lead to diseases. In recent years, computational methods ha...

Application and assessment of deep learning for the generation of potential NMDA receptor antagonists.

Physical chemistry chemical physics : PCCP
Uncompetitive antagonists of the N-methyl d-aspartate receptor (NMDAR) have demonstrated therapeutic benefit in the treatment of neurological diseases such as Parkinson's and Alzheimer's, but some also cause dissociative effects that have led to the ...

Can machine learning consistently improve the scoring power of classical scoring functions? Insights into the role of machine learning in scoring functions.

Briefings in bioinformatics
How to accurately estimate protein-ligand binding affinity remains a key challenge in computer-aided drug design (CADD). In many cases, it has been shown that the binding affinities predicted by classical scoring functions (SFs) cannot correlate well...

Bionoi: A Voronoi Diagram-Based Representation of Ligand-Binding Sites in Proteins for Machine Learning Applications.

Methods in molecular biology (Clifton, N.J.)
Bionoi is a new software to generate Voronoi representations of ligand-binding sites in proteins for machine learning applications. Unlike many other deep learning models in biomedicine, Bionoi utilizes off-the-shelf convolutional neural network arch...

Artificial Intelligence, Big Data and Machine Learning Approaches in Precision Medicine & Drug Discovery.

Current drug targets
Artificial Intelligence revolutionizes the drug development process that can quickly identify potential biologically active compounds from millions of candidate within a short period. The present review is an overview based on some applications of Ma...

Convolutional Neural Network-based Virtual Screening.

Current medicinal chemistry
Virtual screening is an important means for lead compound discovery. The scoring function is the key to selecting hit compounds. Many scoring functions are currently available; however, there are no all-purpose scoring functions because different sco...

Machine Learning-Based Scoring Functions, Development and Applications with SAnDReS.

Current medicinal chemistry
BACKGROUND: Analysis of atomic coordinates of protein-ligand complexes can provide three-dimensional data to generate computational models to evaluate binding affinity and thermodynamic state functions. Application of machine learning techniques can ...

Application of Machine Learning Techniques to Predict Binding Affinity for Drug Targets: A Study of Cyclin-Dependent Kinase 2.

Current medicinal chemistry
BACKGROUND: The elucidation of the structure of cyclin-dependent kinase 2 (CDK2) made it possible to develop targeted scoring functions for virtual screening aimed to identify new inhibitors for this enzyme. CDK2 is a protein target for the developme...

Confronting pitfalls of AI-augmented molecular dynamics using statistical physics.

The Journal of chemical physics
Artificial intelligence (AI)-based approaches have had indubitable impact across the sciences through the ability to extract relevant information from raw data. Recently, AI has also found use in enhancing the efficiency of molecular simulations, whe...

Multitask deep networks with grid featurization achieve improved scoring performance for protein-ligand binding.

Chemical biology & drug design
Deep learning-based methods have been extensively developed to improve scoring performance in structure-based drug discovery. Extending multitask deep networks in addressing pharmaceutical problems shows remarkable improvements over single task netwo...