Journal
JOURNAL OF CHEMICAL INFORMATION AND MODELING
Volume 62, Issue 14, Pages 3291-3306Publisher
AMER CHEMICAL SOC
DOI: 10.1021/acs.jcim.2c00177
Keywords
-
Categories
Funding
- XtalPi Inc
- China postdoctor program [260613]
Ask authors/readers for more resources
This paper proposes a novel molecular deep generative model that adopts a recurrent neural network architecture and ligand-protein interaction fingerprint as constraints for generating compounds with similar binding modes.
In recent years, molecular deep generative models have attracted much attention for its application in de novo drug design. The data-driven molecular deep generative model approximates the high dimensional distribution of the chemical space through learning from a large number of molecular structural data. So far, most of the molecular generative models rely on purely 2D ligand information in structure generation. Here, we propose a novel molecular deep generative model which adopts a recurrent neural network architecture coupled with a ligand-protein interaction fingerprint as constraints. The fingerprint was constructed on ligand docking poses and represents the 3D binding mode of ligands in the protein pocket. In the current work, generative models constrained with interaction fingerprints were trained and compared with normal RNN models. It has been shown that models trained with constraints of ligand-protein interaction fingerprint have a clear tendency to generating compounds maintaining similar binding modes. Our results demonstrate the potential application of the interaction fingerprint-constrained generative model for the targeted molecule generation and guided exploration on the drug-like chemical space.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available