AI Medical Compendium Topic:
Ligands

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Visualizing convolutional neural network protein-ligand scoring.

Journal of molecular graphics & modelling
Protein-ligand scoring is an important step in a structure-based drug design pipeline. Selecting a correct binding pose and predicting the binding affinity of a protein-ligand complex enables effective virtual screening. Machine learning techniques c...

Development of Ligand-based Big Data Deep Neural Network Models for Virtual Screening of Large Compound Libraries.

Molecular informatics
High-performance ligand-based virtual screening (VS) models have been developed using various computational methods, including the deep neural network (DNN) method. There are high expectations for exploration of the advanced capabilities of DNN to im...

Development of a machine-learning model to predict Gibbs free energy of binding for protein-ligand complexes.

Biophysical chemistry
The possibility of using the atomic coordinates of protein-ligand complexes to assess binding affinity has a beneficial impact in the early stages of drug development and design. From the computational view, the creation of reliable scoring functions...

Redefining the Protein Kinase Conformational Space with Machine Learning.

Cell chemical biology
Protein kinases are dynamic, adopting different conformational states that are critical for their catalytic activity. We assess a range of structural features derived from the conserved αC helix and DFG motif to define the conformational space of the...

Fingerprint-Based Machine Learning Approach to Identify Potent and Selective 5-HTR Ligands.

Molecules (Basel, Switzerland)
The identification of subtype-selective GPCR (G-protein coupled receptor) ligands is a challenging task. In this study, we developed a computational protocol to find compounds with 5-HTR versus 5-HTR selectivity. Our approach employs the hierarchical...

Most Ligand-Based Classification Benchmarks Reward Memorization Rather than Generalization.

Journal of chemical information and modeling
Undetected overfitting can occur when there are significant redundancies between training and validation data. We describe AVE, a new measure of training-validation redundancy for ligand-based classification problems, that accounts for the similarity...

Agonists of G-Protein-Coupled Odorant Receptors Are Predicted from Chemical Features.

The journal of physical chemistry letters
Predicting the activity of chemicals for a given odorant receptor is a longstanding challenge. Here the activity of 258 chemicals on the human G-protein-coupled odorant receptor (OR)51E1, also known as prostate-specific G-protein-coupled receptor 2 (...

Knowledge-Based Conformer Generation Using the Cambridge Structural Database.

Journal of chemical information and modeling
Fast generation of plausible molecular conformations is central to molecular modeling. This paper presents an approach to conformer generation that makes extensive use of the information available in the Cambridge Structural Database. By using geomet...

Statistical and machine learning approaches to predicting protein-ligand interactions.

Current opinion in structural biology
Data driven computational approaches to predicting protein-ligand binding are currently achieving unprecedented levels of accuracy on held-out test datasets. Up until now, however, this has not led to corresponding breakthroughs in our ability to des...