AIMC Topic: Ligands

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Comprehensive Review on Drug-target Interaction Prediction - Latest Developments and Overview.

Current drug discovery technologies
Drug-target interactions (DTIs) are an important part of the drug development process. When the drug (a chemical molecule) binds to a target (proteins or nucleic acids), it modulates the biological behavior/function of the target, returning it to its...

Exploring Scoring Function Space: Developing Computational Models for Drug Discovery.

Current medicinal chemistry
BACKGROUND: The idea of scoring function space established a systems-level approach to address the development of models to predict the affinity of drug molecules by those interested in drug discovery.

Protein-ligand binding affinity prediction exploiting sequence constituent homology.

Bioinformatics (Oxford, England)
MOTIVATION: Molecular docking is a commonly used approach for estimating binding conformations and their resultant binding affinities. Machine learning has been successfully deployed to enhance such affinity estimations. Many methods of varying compl...

Structural interaction fingerprints and machine learning for predicting and explaining binding of small molecule ligands to RNA.

Briefings in bioinformatics
Ribonucleic acids (RNAs) play crucial roles in living organisms and some of them, such as bacterial ribosomes and precursor messenger RNA, are targets of small molecule drugs, whereas others, e.g. bacterial riboswitches or viral RNA motifs are consid...

GraphscoreDTA: optimized graph neural network for protein-ligand binding affinity prediction.

Bioinformatics (Oxford, England)
MOTIVATION: Computational approaches for identifying the protein-ligand binding affinity can greatly facilitate drug discovery and development. At present, many deep learning-based models are proposed to predict the protein-ligand binding affinity an...

Predicting residue cooperativity during protein folding: A combined, molecular dynamics and unsupervised learning approach.

The Journal of chemical physics
Allostery in proteins involves, broadly speaking, ligand-induced conformational transitions that modulate function at active sites distal to where the ligand binds. In contrast, the concept of cooperativity (in the sense used in phase transition theo...

CAPLA: improved prediction of protein-ligand binding affinity by a deep learning approach based on a cross-attention mechanism.

Bioinformatics (Oxford, England)
MOTIVATION: Accurate and rapid prediction of protein-ligand binding affinity is a great challenge currently encountered in drug discovery. Recent advances have manifested a promising alternative in applying deep learning-based computational approache...

Leveraging scaffold information to predict protein-ligand binding affinity with an empirical graph neural network.

Briefings in bioinformatics
Protein-ligand binding affinity prediction is an important task in structural bioinformatics for drug discovery and design. Although various scoring functions (SFs) have been proposed, it remains challenging to accurately evaluate the binding affinit...

A fully differentiable ligand pose optimization framework guided by deep learning and a traditional scoring function.

Briefings in bioinformatics
The recently reported machine learning- or deep learning-based scoring functions (SFs) have shown exciting performance in predicting protein-ligand binding affinities with fruitful application prospects. However, the differentiation between highly si...

RLBind: a deep learning method to predict RNA-ligand binding sites.

Briefings in bioinformatics
Identification of RNA-small molecule binding sites plays an essential role in RNA-targeted drug discovery and development. These small molecules are expected to be leading compounds to guide the development of new types of RNA-targeted therapeutics c...