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Ligands

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PathDetect-SOM: A Neural Network Approach for the Identification of Pathways in Ligand Binding Simulations.

Journal of chemical theory and computation
Understanding the process of ligand-protein recognition is important to unveil biological mechanisms and to guide drug discovery and design. Enhanced-sampling molecular dynamics is now routinely used to simulate the ligand binding process, resulting ...

Complex machine learning model needs complex testing: Examining predictability of molecular binding affinity by a graph neural network.

Journal of computational chemistry
Drug discovery pipelines typically involve high-throughput screening of large amounts of compounds in a search of potential drugs candidates. As a chemical space of small organic molecules is huge, a "navigation" over it urges for fast and lightweigh...

Identification of new putative inhibitors of 3-dehydroshikimate dehydratase from a combination of ligand- and structure-based and deep learning approaches.

Journal of biomolecular structure & dynamics
The development of new drugs against is an essential strategy for fighting drug resistance. Although 3-dehydroquinate dehydratase (MtDHQ) is known to be a highly relevant target for , current research shows new putative inhibitors of MtDHQ selected ...

Combined Free-Energy Calculation and Machine Learning Methods for Understanding Ligand Unbinding Kinetics.

Journal of chemical theory and computation
The determination of drug residence times, which define the time an inhibitor is in complex with its target, is a fundamental part of the drug discovery process. Synthesis and experimental measurements of kinetic rate constants are, however, expensiv...

Receptor tyrosine kinase MET ligand-interaction classified via machine learning from single-particle tracking data.

Molecular biology of the cell
Internalin B-mediated activation of the membrane-bound receptor tyrosine kinase MET is accompanied by a change in receptor mobility. Conversely, it should be possible to infer from receptor mobility whether a cell has been treated with internalin B. ...

Artificial intelligence-enabled virtual screening of ultra-large chemical libraries with deep docking.

Nature protocols
With the recent explosion of chemical libraries beyond a billion molecules, more efficient virtual screening approaches are needed. The Deep Docking (DD) platform enables up to 100-fold acceleration of structure-based virtual screening by docking onl...

TorsionNet: A Deep Neural Network to Rapidly Predict Small-Molecule Torsional Energy Profiles with the Accuracy of Quantum Mechanics.

Journal of chemical information and modeling
Fast and accurate assessment of small-molecule dihedral energetics is crucial for molecular design and optimization in medicinal chemistry. Yet, accurate prediction of torsion energy profiles remains challenging as the current molecular mechanics (MM...

Deep Learning in Drug Design: Protein-Ligand Binding Affinity Prediction.

IEEE/ACM transactions on computational biology and bioinformatics
Computational drug design relies on the calculation of binding strength between two biological counterparts especially a chemical compound, i.e., a ligand, and a protein. Predicting the affinity of protein-ligand binding with reasonable accuracy is c...

AI in 3D compound design.

Current opinion in structural biology
The success of Artificial Intelligence (AI) across a wide range of domains has fuelled significant interest in its application to designing novel compounds and screening compounds against a specific target. However, many existing AI methods either do...

Predicting Ca and Mg ligand binding sites by deep neural network algorithm.

BMC bioinformatics
BACKGROUND: Alkaline earth metal ions are important protein binding ligands in human body, and it is of great significance to predict their binding residues.