AIMC Topic: Ligands

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Big data and artificial intelligence discover novel drugs targeting proteins without 3D structure and overcome the undruggable targets.

Stroke and vascular neurology
The discovery of targeted drugs heavily relies on three-dimensional (3D) structures of target proteins. When the 3D structure of a protein target is unknown, it is very difficult to design its corresponding targeted drugs. Although the 3D structures ...

Improving detection of protein-ligand binding sites with 3D segmentation.

Scientific reports
In recent years machine learning (ML) took bio- and cheminformatics fields by storm, providing new solutions for a vast repertoire of problems related to protein sequence, structure, and interactions analysis. ML techniques, deep neural networks espe...

Improving the binding affinity estimations of protein-ligand complexes using machine-learning facilitated force field method.

Journal of computer-aided molecular design
Scoring functions are routinely deployed in structure-based drug design to quantify the potential for protein-ligand (PL) complex formation. Here, we present a new scoring function Bappl+ that is designed to predict the binding affinities of non-meta...

Machine Learning to Identify Flexibility Signatures of Class A GPCR Inhibition.

Biomolecules
We show that machine learning can pinpoint features distinguishing inactive from active states in proteins, in particular identifying key ligand binding site flexibility transitions in GPCRs that are triggered by biologically active ligands. Our anal...

Machine learning models for drug-target interactions: current knowledge and future directions.

Drug discovery today
Predicting the binding affinity between compounds and proteins with reasonable accuracy is crucial in drug discovery. Computational prediction of binding affinity between compounds and targets greatly enhances the probability of finding lead compound...

Combining Docking Pose Rank and Structure with Deep Learning Improves Protein-Ligand Binding Mode Prediction over a Baseline Docking Approach.

Journal of chemical information and modeling
We present a simple, modular graph-based convolutional neural network that takes structural information from protein-ligand complexes as input to generate models for activity and binding mode prediction. Complex structures are generated by a standard...

Tapping on the Black Box: How Is the Scoring Power of a Machine-Learning Scoring Function Dependent on the Training Set?

Journal of chemical information and modeling
In recent years, protein-ligand interaction scoring functions derived through machine-learning are repeatedly reported to outperform conventional scoring functions. However, several published studies have questioned that the superior performance of m...

A novel artificial intelligence protocol for finding potential inhibitors of acute myeloid leukemia.

Journal of materials chemistry. B
There is currently no effective treatment for acute myeloid leukemia, and surgery is also ineffective as an important treatment for most tumors. Rapidly developing artificial intelligence technology can be applied to different aspects of drug develop...

Machine learning and ligand binding predictions: A review of data, methods, and obstacles.

Biochimica et biophysica acta. General subjects
Computational predictions of ligand binding is a difficult problem, with more accurate methods being extremely computationally expensive. The use of machine learning for drug binding predictions could possibly leverage the use of biomedical big data ...