AI Medical Compendium Topic:
Ligands

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Machine Learning-Based Modeling of Drug Toxicity.

Methods in molecular biology (Clifton, N.J.)
Toxicity is an important reason for the failure of drug research and development (R&D). The traditional experimental testings for chemical toxicity profile are costly and time-consuming. Therefore, it is attractive to develop the effective and accura...

Optimized Virtual Screening Workflow: Towards Target-Based Polynomial Scoring Functions for HIV-1 Protease.

Combinatorial chemistry & high throughput screening
BACKGROUND: One key step in the development of inhibitors for an enzyme is the application of computational methodologies to predict protein-ligand interactions. The abundance of structural and ligand-binding information for HIV-1 protease opens up t...

Supervised Machine Learning Methods Applied to Predict Ligand- Binding Affinity.

Current medicinal chemistry
BACKGROUND: Calculation of ligand-binding affinity is an open problem in computational medicinal chemistry. The ability to computationally predict affinities has a beneficial impact in the early stages of drug development, since it allows a mathemati...

Some Remarks on Prediction of Drug-Target Interaction with Network Models.

Current topics in medicinal chemistry
System-level understanding of the relationships between drugs and targets is very important for enhancing drug research, especially for drug function repositioning. The experimental methods used to determine drug-target interactions are usually time-...

Machine learning-, rule- and pharmacophore-based classification on the inhibition of P-glycoprotein and NorA.

SAR and QSAR in environmental research
The efflux pumps P-glycoprotein (P-gp) in humans and NorA in Staphylococcus aureus are of great interest for medicinal chemists because of their important roles in multidrug resistance (MDR). The high polyspecificity as well as the unavailability of ...

Use of machine learning approaches for novel drug discovery.

Expert opinion on drug discovery
INTRODUCTION: The use of computational tools in the early stages of drug development has increased in recent decades. Machine learning (ML) approaches have been of special interest, since they can be applied in several steps of the drug discovery met...

Analysis of Drug Design for a Selection of G Protein-Coupled Neuro- Receptors Using Neural Network Techniques.

Current computer-aided drug design
A study is presented on how well possible drug-molecules can be predicted with respect to their function and binding to a selection of neuro-receptors by the use of artificial neural networks. The ligands investigated in this study are chosen to be c...

Constructing query-driven dynamic machine learning model with application to protein-ligand binding sites prediction.

IEEE transactions on nanobioscience
We are facing an era with annotated biological data rapidly and continuously generated. How to effectively incorporate new annotated data into the learning step is crucial for enhancing the performance of a bioinformatics prediction model. Although m...

Compressed images for affinity prediction-2 (CIFAP-2): an improved machine learning methodology on protein-ligand interactions based on a study on caspase 3 inhibitors.

Journal of enzyme inhibition and medicinal chemistry
The aim of this study is to propose an improved computational methodology, which is called Compressed Images for Affinity Prediction-2 (CIFAP-2) to predict binding affinities of structurally related protein-ligand complexes. CIFAP-2 method is establi...