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Toward 3D Structure Augmented Deep Molecular Generation
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.advisor | 이슬 | - |
| dc.contributor.author | 박진준 | - |
| dc.date.accessioned | 2022-11-29T03:01:20Z | - |
| dc.date.available | 2022-11-29T03:01:20Z | - |
| dc.date.issued | 2022-02 | - |
| dc.identifier.other | 31775 | - |
| dc.identifier.uri | https://dspace.ajou.ac.kr/handle/2018.oak/21030 | - |
| dc.description | 학위논문(석사)--아주대학교 일반대학원 :인공지능학과,2022. 2 | - |
| dc.description.tableofcontents | 1 Introduction 1 2 Related Works 4 2.1 Graph Neural Networks 4 2.2 Deep Graph Models for Molecular Generation 4 2.2.1 JT-VAE 5 2.2.2. HierVAE 6 2.2.3 GraphAF 6 2.2.4 GraphDF 7 2.3.5 GraphEBM 7 2.3.6 MoFlow 8 3 Method 9 3.1 Expended GraphEBM 9 3.2 Dataset 11 3.3 Data Sampling Process 12 3.3.1 Evaluation Metrics 13 3.4 Compared Models 14 4 Results 15 4.1 Random Generation Performance Evaluation 15 4.2 Property Optimization Performance Evaluation 17 5 Conclusion 23 Reference 24 | - |
| dc.language.iso | kor | - |
| dc.publisher | The Graduate School, Ajou University | - |
| dc.rights | 아주대학교 논문은 저작권에 의해 보호받습니다. | - |
| dc.title | Toward 3D Structure Augmented Deep Molecular Generation | - |
| dc.type | Thesis | - |
| dc.contributor.affiliation | 아주대학교 일반대학원 | - |
| dc.contributor.department | 일반대학원 인공지능학과 | - |
| dc.date.awarded | 2022. 2 | - |
| dc.description.degree | Master | - |
| dc.identifier.localId | 1245120 | - |
| dc.identifier.uci | I804:41038-000000031775 | - |
| dc.identifier.url | https://dcoll.ajou.ac.kr/dcollection/common/orgView/000000031775 | - |
| dc.subject.keyword | deep molecular generation | - |
| dc.description.alternativeAbstract | Which molecular generation method is best for large molecular generations? Finding a good lead molecule is an important task in drug discovery. Recently several deep graph generative models have been developed for generating novel molecules that can be further tested for synthesizability in the drug development process. Most of the developed models are trained on small molecules with a maximum length of thirty. However, there is a need for the generation of larger molecules. We tested six recently proposed graph neural network-based molecular generation methods on their large molecular generation performance using two datasets from the LigandBox database, which contain larger molecules than typically used ZINC250k and QM9 datasets. In addition, we propose a modified model using 3D coordinate information of molecules and evaluate this model together with recent models. We use twelve evaluation measures to evaluate the quality of the generated molecules, including stability measures such as logP values and QEDs. | - |
- Appears in Collections:
- Graduate School of Ajou University > Department of Artificial Intelligence > 3. Theses(Master)
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