Geometry-Based Molecular Generation With Deep Constrained Variational Autoencoder

Finding target molecules with specific chemical properties plays a decisive role in drug development. We proposed GEOM-CVAE, a constrained variational autoencoder based on geometric representation for molecular generation with specific properties, which is protein-context-dependent. In terms of mach...

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Bibliographic Details
Published in:IEEE transaction on neural networks and learning systems Vol. 35; no. 4; pp. 4852 - 4861
Main Authors: Li, Chunyan, Yao, Junfeng, Wei, Wei, Niu, Zhangming, Zeng, Xiangxiang, Li, Jin, Wang, Jianmin
Format: Journal Article
Language:English
Published: United States IEEE 01.04.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Chemical properties
Chemicophysical properties
Computational modeling
Convolution
Coordinate
Decoding
Drug development
Drugs
Embedding
Feature extraction
Finite element method
geometry
graph convolutional network
Graph representations
Graphical representations
Machine learning
mesh
molecular generation
Molecular structure
Proteins
Solid modeling
Two dimensional models
variational autoencoder (VAE)
Visualization
ISSN:2162-237X, 2162-2388, 2162-2388
Online Access:Get full text
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Summary:Finding target molecules with specific chemical properties plays a decisive role in drug development. We proposed GEOM-CVAE, a constrained variational autoencoder based on geometric representation for molecular generation with specific properties, which is protein-context-dependent. In terms of machine learning, it includes continuous feature embedding encoder and molecular generation decoder. Our key contribution is to propose an efficient geometric embedding method, including the spatial structure representations of drug molecule (converting the 3-D coordinates into image) and the geometric graph representations of protein target (modeling the protein surface as a mesh). The 3-D geometric information is vital to successful molecular generation, which is different from previous molecular generative methods based on 1-D or 2-D. Our model framework generates specific molecules in two phases, by first generating special image with molecular 3-D information to learn latent representations and generating molecules with constrained condition based on geometric graph convolution for specific protein and then inputting the generated structural molecules into a parser network for obtaining Simplified Molecular Input Line Entry System (SMILES) strings. Our model achieves competitive performance that implies its potential effectiveness to enable the exploration of the vast chemical space for drug discovery.
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ISSN:2162-237X
2162-2388
2162-2388
DOI:10.1109/TNNLS.2022.3147790