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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| Veröffentlicht in: | IEEE transaction on neural networks and learning systems Jg. 35; H. 4; S. 4852 - 4861 |
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IEEE
01.04.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| Abstract | 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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| AbstractList | 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. 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.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. |
| Author | Zeng, Xiangxiang Niu, Zhangming Wei, Wei Wang, Jianmin Li, Chunyan Li, Jin Yao, Junfeng |
| Author_xml | – sequence: 1 givenname: Chunyan orcidid: 0000-0003-3014-3363 surname: Li fullname: Li, Chunyan organization: School of Informatics, Xiamen University, Xiamen, China – sequence: 2 givenname: Junfeng orcidid: 0000-0002-2330-7406 surname: Yao fullname: Yao, Junfeng email: yao0010@xmu.edu.cn organization: Institute of Artificial Intelligence and the School of Film, Xiamen University, Xiamen, China – sequence: 3 givenname: Wei orcidid: 0000-0002-7566-2995 surname: Wei fullname: Wei, Wei email: weiwei@xaut.edu.cn organization: School of Computer Science and Engineering, Xi'an University of Technology, Xi'an, China – sequence: 4 givenname: Zhangming orcidid: 0000-0002-7009-946X surname: Niu fullname: Niu, Zhangming organization: MindRank AI Ltd., Hangzhou, Zhejiang, China – sequence: 5 givenname: Xiangxiang orcidid: 0000-0003-1081-7658 surname: Zeng fullname: Zeng, Xiangxiang email: xzeng@hnu.edu.cn organization: College of Computer Science and Electronic Engineering, Hunan University, Changsha, China – sequence: 6 givenname: Jin orcidid: 0000-0002-3628-7037 surname: Li fullname: Li, Jin organization: School of Software, Yunnan University, Kunming, China – sequence: 7 givenname: Jianmin orcidid: 0000-0001-8910-0929 surname: Wang fullname: Wang, Jianmin organization: Integrative Biotechnology & Translational Medicine, Yonsei University, Incheon, Republic of Korea |
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| SubjectTerms | 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 |
| Title | Geometry-Based Molecular Generation With Deep Constrained Variational Autoencoder |
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