MGCVAE: Multi-Objective Inverse Design via Molecular Graph Conditional Variational Autoencoder
The ultimate goal of various fields is to directly generate molecules with desired properties, such as water-soluble molecules in drug development and molecules suitable for organic light-emitting diodes or photosensitizers in the field of development of new organic materials. This study proposes a...
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| Vydané v: | Journal of chemical information and modeling Ročník 62; číslo 12; s. 2943 |
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| Hlavní autori: | , |
| Médium: | Journal Article |
| Jazyk: | English |
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United States
27.06.2022
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| ISSN: | 1549-960X, 1549-960X |
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| Abstract | The ultimate goal of various fields is to directly generate molecules with desired properties, such as water-soluble molecules in drug development and molecules suitable for organic light-emitting diodes or photosensitizers in the field of development of new organic materials. This study proposes a molecular graph generative model based on an autoencoder for the de novo design. The performance of the molecular graph conditional variational autoencoder (MGCVAE) for generating molecules with specific desired properties was investigated by comparing it to a molecular graph variational autoencoder (MGVAE). Furthermore, multi-objective optimization for MGCVAE was applied to satisfy the two selected properties simultaneously. In this study, two physical properties, calculated log
and molar refractivity, were used as optimization targets for designing de novo molecules. Consequently, it was confirmed that among the generated molecules, 25.89% of the optimized molecules were generated in MGCVAE compared to 0.66% in MGVAE. This demonstrates that MGCVAE effectively produced drug-like molecules with two target properties. The results of this study suggest that these graph-based data-driven models are an effective method for designing new molecules that fulfill various physical properties. |
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| AbstractList | The ultimate goal of various fields is to directly generate molecules with desired properties, such as water-soluble molecules in drug development and molecules suitable for organic light-emitting diodes or photosensitizers in the field of development of new organic materials. This study proposes a molecular graph generative model based on an autoencoder for the de novo design. The performance of the molecular graph conditional variational autoencoder (MGCVAE) for generating molecules with specific desired properties was investigated by comparing it to a molecular graph variational autoencoder (MGVAE). Furthermore, multi-objective optimization for MGCVAE was applied to satisfy the two selected properties simultaneously. In this study, two physical properties, calculated logP and molar refractivity, were used as optimization targets for designing de novo molecules. Consequently, it was confirmed that among the generated molecules, 25.89% of the optimized molecules were generated in MGCVAE compared to 0.66% in MGVAE. This demonstrates that MGCVAE effectively produced drug-like molecules with two target properties. The results of this study suggest that these graph-based data-driven models are an effective method for designing new molecules that fulfill various physical properties.The ultimate goal of various fields is to directly generate molecules with desired properties, such as water-soluble molecules in drug development and molecules suitable for organic light-emitting diodes or photosensitizers in the field of development of new organic materials. This study proposes a molecular graph generative model based on an autoencoder for the de novo design. The performance of the molecular graph conditional variational autoencoder (MGCVAE) for generating molecules with specific desired properties was investigated by comparing it to a molecular graph variational autoencoder (MGVAE). Furthermore, multi-objective optimization for MGCVAE was applied to satisfy the two selected properties simultaneously. In this study, two physical properties, calculated logP and molar refractivity, were used as optimization targets for designing de novo molecules. Consequently, it was confirmed that among the generated molecules, 25.89% of the optimized molecules were generated in MGCVAE compared to 0.66% in MGVAE. This demonstrates that MGCVAE effectively produced drug-like molecules with two target properties. The results of this study suggest that these graph-based data-driven models are an effective method for designing new molecules that fulfill various physical properties. The ultimate goal of various fields is to directly generate molecules with desired properties, such as water-soluble molecules in drug development and molecules suitable for organic light-emitting diodes or photosensitizers in the field of development of new organic materials. This study proposes a molecular graph generative model based on an autoencoder for the de novo design. The performance of the molecular graph conditional variational autoencoder (MGCVAE) for generating molecules with specific desired properties was investigated by comparing it to a molecular graph variational autoencoder (MGVAE). Furthermore, multi-objective optimization for MGCVAE was applied to satisfy the two selected properties simultaneously. In this study, two physical properties, calculated log and molar refractivity, were used as optimization targets for designing de novo molecules. Consequently, it was confirmed that among the generated molecules, 25.89% of the optimized molecules were generated in MGCVAE compared to 0.66% in MGVAE. This demonstrates that MGCVAE effectively produced drug-like molecules with two target properties. The results of this study suggest that these graph-based data-driven models are an effective method for designing new molecules that fulfill various physical properties. |
| Author | Min, Kyoungmin Lee, Myeonghun |
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| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/35666276$$D View this record in MEDLINE/PubMed |
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| CitedBy_id | crossref_primary_10_1021_acs_jcim_4c00031 crossref_primary_10_1007_s11030_024_10942_5 crossref_primary_10_1016_j_compbiomed_2024_108486 crossref_primary_10_1016_j_isci_2024_110992 crossref_primary_10_1016_j_inffus_2024_102874 crossref_primary_10_1021_acs_chemmater_5c00964 crossref_primary_10_1093_bib_bbad368 crossref_primary_10_1186_s12859_023_05286_0 crossref_primary_10_1039_D4MH00302K crossref_primary_10_3390_s25154716 crossref_primary_10_1038_s42256_023_00683_9 crossref_primary_10_1007_s13755_025_00344_8 crossref_primary_10_1002_advs_202410640 crossref_primary_10_1021_acs_jctc_5c00305 crossref_primary_10_1098_rspa_2025_0115 crossref_primary_10_1016_j_engappai_2024_108595 crossref_primary_10_1186_s13321_024_00863_8 crossref_primary_10_1016_j_sbi_2023_102537 crossref_primary_10_1002_advs_202416356 crossref_primary_10_3390_ph18081227 crossref_primary_10_26599_BDMA_2023_9020009 crossref_primary_10_1016_j_eswa_2023_122396 |
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| Title | MGCVAE: Multi-Objective Inverse Design via Molecular Graph Conditional Variational Autoencoder |
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