Supervised Machine Learning Methods Applied to Predict Ligand- Binding Affinity
Abstract
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 mathematical model to assess protein-ligand interactions. Due to the availability of structural and binding information, machine learning methods have been applied to generate scoring functions with good...
Paper Details
Title
Supervised Machine Learning Methods Applied to Predict Ligand- Binding Affinity
Published Date
Sep 11, 2017
Journal
Volume
24
Issue
23
Citation AnalysisPro
You’ll need to upgrade your plan to Pro
Looking to understand the true influence of a researcher’s work across journals & affiliations?
- Scinapse’s Top 10 Citation Journals & Affiliations graph reveals the quality and authenticity of citations received by a paper.
- Discover whether citations have been inflated due to self-citations, or if citations include institutional bias.
Notes
History