Automatic recognition of ligands in electron density by machine learning | Zendy

Marcin Kowiel | Zendy; Dariusz Brzeziński | Zendy; Przemyslaw Porebski | Zendy; I.G. Shabalin | Zendy; Mariusz Jaskólski | Zendy; W. Minor | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Open Access

Automatic recognition of ligands in electron density by machine learning

Author(s) -

Marcin Kowiel,

Dariusz Brzeziński,

Przemyslaw Porebski,

I.G. Shabalin,

Mariusz Jaskólski,

W. Minor

Publication year - 2018

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/bty626

Subject(s) - computer science , identification (biology) , benchmark (surveying) , automation , process (computing) , artificial intelligence , machine learning , ligand (biochemistry) , source code , software , protein ligand , algorithm , data mining , chemistry , programming language , mechanical engineering , biochemistry , botany , receptor , geodesy , organic chemistry , engineering , biology , geography

The correct identification of ligands in crystal structures of protein complexes is the cornerstone of structure-guided drug design. However, cognitive bias can sometimes mislead investigators into modeling fictitious compounds without solid support from the electron density maps. Ligand identification can be aided by automatic methods, but existing approaches are based on time-consuming iterative fitting.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.

Having issues? You can contact us here

Accelerating Research