scholarly journals Real spherical harmonic expansion coefficients as 3D shape descriptors for protein binding pocket and ligand comparisons

2005 ◽  
Vol 21 (10) ◽  
pp. 2347-2355 ◽  
Author(s):  
R. J. Morris ◽  
R. J. Najmanovich ◽  
A. Kahraman ◽  
J. M. Thornton
Keyword(s):  
Protein Binding ◽  
Spherical Harmonic ◽  
Binding Pocket ◽  
Spherical Harmonic Expansion ◽  
Shape Descriptors ◽  
3D Shape ◽  
Expansion Coefficients

scholarly journals De Novo Molecule Design Through Molecular Generative Model Conditioned by 3D Information of Protein Binding Sites

2020 ◽  
Author(s):  
Mingyuan Xu ◽  
Ting Ran ◽  
Hongming Chen
Keyword(s):  
Protein Binding ◽  
De Novo ◽  
Structural Information ◽  
Chemical Space ◽  
Three Dimensional ◽  
Binding Pocket ◽  
Generative Model ◽  
Coarse Grained ◽  
Molecule Design ◽  
3D Information

<p><i>De novo</i> molecule design through molecular generative model is gaining increasing attention in recent years. Here a novel generative model was proposed by integrating the 3D structural information of the protein binding pocket into the conditional RNN (cRNN) model to control the generation of drug-like molecules. In this model, the composition of protein binding pocket is effectively characterized through a coarse-grain strategy and the three-dimensional information of the pocket can be represented by the sorted eigenvalues of the coulomb matrix (EGCM) of the coarse-grained atoms composing the binding pocket. In current work, we used our EGCM method and a previously reported binding pocket descriptor DeeplyTough to train cRNN models and compared their performance. It has been shown that the molecules generated with the control of protein environment information have a clear tendency on generating compounds with higher similarity to the original X-ray bound ligand than normal RNN model and also achieving better performance in terms of docking scores. Our results demonstrate the potential application of EGCM controlled generative model for the targeted molecule generation and guided exploration on the drug-like chemical space. </p><p> </p>

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