AI Medical Compendium Topic:
Ligands

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CSM-Potential: mapping protein interactions and biological ligands in 3D space using geometric deep learning.

Nucleic acids research
Recent advances in protein structural modelling have enabled the accurate prediction of the holo 3D structures of almost any protein, however protein function is intrinsically linked to the interactions it makes. While a number of computational appro...

GRaSP-web: a machine learning strategy to predict binding sites based on residue neighborhood graphs.

Nucleic acids research
Proteins are essential macromolecules for the maintenance of living systems. Many of them perform their function by interacting with other molecules in regions called binding sites. The identification and characterization of these regions are of fund...

Complementing machine learning-based structure predictions with native mass spectrometry.

Protein science : a publication of the Protein Society
The advent of machine learning-based structure prediction algorithms such as AlphaFold2 (AF2) and RoseTTa Fold have moved the generation of accurate structural models for the entire cellular protein machinery into the reach of the scientific communit...

SPLDExtraTrees: robust machine learning approach for predicting kinase inhibitor resistance.

Briefings in bioinformatics
Drug resistance is a major threat to the global health and a significant concern throughout the clinical treatment of diseases and drug development. The mutation in proteins that is related to drug binding is a common cause for adaptive drug resistan...

DeepREAL: a deep learning powered multi-scale modeling framework for predicting out-of-distribution ligand-induced GPCR activity.

Bioinformatics (Oxford, England)
MOTIVATION: Drug discovery has witnessed intensive exploration of predictive modeling of drug-target physical interactions over two decades. However, a critical knowledge gap needs to be filled for correlating drug-target interactions with clinical o...

Molecular Modeling Techniques Applied to the Design of Multitarget Drugs: Methods and Applications.

Current topics in medicinal chemistry
Multifactorial diseases, such as cancer and diabetes present a challenge for the traditional "one-target, one disease" paradigm due to their complex pathogenic mechanisms. Although a combination of drugs can be used, a multitarget drug may be a bette...

Molecular persistent spectral image (Mol-PSI) representation for machine learning models in drug design.

Briefings in bioinformatics
Artificial intelligence (AI)-based drug design has great promise to fundamentally change the landscape of the pharmaceutical industry. Even though there are great progress from handcrafted feature-based machine learning models, 3D convolutional neura...

Artificial intelligence in the prediction of protein-ligand interactions: recent advances and future directions.

Briefings in bioinformatics
New drug production, from target identification to marketing approval, takes over 12 years and can cost around $2.6 billion. Furthermore, the COVID-19 pandemic has unveiled the urgent need for more powerful computational methods for drug discovery. H...

A point cloud-based deep learning strategy for protein-ligand binding affinity prediction.

Briefings in bioinformatics
There is great interest to develop artificial intelligence-based protein-ligand binding affinity models due to their immense applications in drug discovery. In this paper, PointNet and PointTransformer, two pointwise multi-layer perceptrons have been...

Classical and Machine Learning Methods for Protein - Ligand Binding Free Energy Estimation.

Current drug metabolism
Binding free energy estimation of drug candidates to their biomolecular target is one of the best quantitative estimators in computer-aided drug discovery. Accurate binding free energy estimation is still a challengeable task even after decades of re...