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Ligands

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Ligand-Based Virtual Screening Based on the Graph Edit Distance.

International journal of molecular sciences
Chemical compounds can be represented as attributed graphs. An attributed graph is a mathematical model of an object composed of two types of representations: nodes and edges. Nodes are individual components, and edges are relations between these com...

PremPLI: a machine learning model for predicting the effects of missense mutations on protein-ligand interactions.

Communications biology
Resistance to small-molecule drugs is the main cause of the failure of therapeutic drugs in clinical practice. Missense mutations altering the binding of ligands to proteins are one of the critical mechanisms that result in genetic disease and drug r...

Structure-Based Drug Design Using Deep Learning.

Journal of chemical information and modeling
In recent years, deep learning-based methods have emerged as promising tools for drug design. Most of these methods are ligand-based, where an initial target-specific ligand data set is necessary to design potent molecules with optimized properties....

Binding affinity prediction for protein-ligand complex using deep attention mechanism based on intermolecular interactions.

BMC bioinformatics
BACKGROUND: Accurate prediction of protein-ligand binding affinity is important for lowering the overall cost of drug discovery in structure-based drug design. For accurate predictions, many classical scoring functions and machine learning-based meth...

End-to-end learning for compound activity prediction based on binding pocket information.

BMC bioinformatics
BACKGROUND: Recently, machine learning-based ligand activity prediction methods have been greatly improved. However, if known active compounds of a target protein are unavailable, the machine learning-based method cannot be applied. In such cases, do...

Using diverse potentials and scoring functions for the development of improved machine-learned models for protein-ligand affinity and docking pose prediction.

Journal of computer-aided molecular design
The advent of computational drug discovery holds the promise of significantly reducing the effort of experimentalists, along with monetary cost. More generally, predicting the binding of small organic molecules to biological macromolecules has far-re...

Machine Learning Assisted Approach for Finding Novel High Activity Agonists of Human Ectopic Olfactory Receptors.

International journal of molecular sciences
Olfactory receptors (ORs) constitute the largest superfamily of G protein-coupled receptors (GPCRs). ORs are involved in sensing odorants as well as in other ectopic roles in non-nasal tissues. Matching of an enormous number of the olfactory stimulat...

Improving Structure-Based Virtual Screening with Ensemble Docking and Machine Learning.

Journal of chemical information and modeling
One of the main challenges of structure-based virtual screening (SBVS) is the incorporation of the receptor's flexibility, as its explicit representation in every docking run implies a high computational cost. Therefore, a common alternative to inclu...

Development of a proteochemometric-based support vector machine model for predicting bioactive molecules of tubulin receptors.

Molecular diversity
Microtubules are receiving enormous interest in drug discovery due to the important roles they play in cellular functions. Targeting tubulin polymerization presents an excellent opportunity for the development of anti-tubulin drugs. Drug resistance a...

MANORAA: A machine learning platform to guide protein-ligand design by anchors and influential distances.

Structure (London, England : 1993)
The MANORAA platform uses structure-based approaches to provide information on drug design originally derived from mapping tens of thousands of amino acids on a grid. In-depth analyses of the pockets, frequently occurring atoms, influential distances...