| Item type |
SIG Technical Reports(1) |
| 公開日 |
2022-03-03 |
| タイトル |
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|
タイトル |
In silico drug design by Molecular Generative Model and Docking |
| タイトル |
|
|
言語 |
en |
|
タイトル |
In silico drug design by Molecular Generative Model and Docking |
| 言語 |
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|
言語 |
eng |
| 資源タイプ |
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|
資源タイプ識別子 |
http://purl.org/coar/resource_type/c_18gh |
|
資源タイプ |
technical report |
| 著者所属 |
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|
|
School of Computing, Tokyo Institute of Technology |
| 著者所属 |
|
|
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Tokyo Tech Academy for Convergence of Materials and Informatics (TAC-MI), Tokyo Institute of Technology |
| 著者所属 |
|
|
|
School of Computing, Tokyo Institute of Technology |
| 著者所属(英) |
|
|
|
en |
|
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School of Computing, Tokyo Institute of Technology |
| 著者所属(英) |
|
|
|
en |
|
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Tokyo Tech Academy for Convergence of Materials and Informatics (TAC-MI), Tokyo Institute of Technology |
| 著者所属(英) |
|
|
|
en |
|
|
School of Computing, Tokyo Institute of Technology |
| 著者名 |
Dian, Ma
Nobuaki, Yasuo
Masakazu, Sekijima
|
| 著者名(英) |
Dian, Ma
Nobuaki, Yasuo
Masakazu, Sekijima
|
| 論文抄録 |
|
|
内容記述タイプ |
Other |
|
内容記述 |
This study develops a new deep learning-based extendable multiple-objective molecular generator (MOMolGen). This generator integrates a recurrent neural network (RNN) to generate molecules and Pareto Multi-Objective Monte Carlo Tree Search (Pareto MOMCTS) to decide search direction. This generator is validated by generating compounds for specific target proteins with evaluation on the drug-like properties and docking score. |
| 論文抄録(英) |
|
|
内容記述タイプ |
Other |
|
内容記述 |
This study develops a new deep learning-based extendable multiple-objective molecular generator (MOMolGen). This generator integrates a recurrent neural network (RNN) to generate molecules and Pareto Multi-Objective Monte Carlo Tree Search (Pareto MOMCTS) to decide search direction. This generator is validated by generating compounds for specific target proteins with evaluation on the drug-like properties and docking score. |
| 書誌レコードID |
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収録物識別子タイプ |
NCID |
|
収録物識別子 |
AA12055912 |
| 書誌情報 |
研究報告バイオ情報学（BIO）
巻 2022-BIO-69,
号 5,
p. 1-6,
発行日 2022-03-03
|
| ISSN |
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収録物識別子タイプ |
ISSN |
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収録物識別子 |
2188-8590 |
| Notice |
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SIG Technical Reports are nonrefereed and hence may later appear in any journals, conferences, symposia, etc. |
| 出版者 |
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言語 |
ja |
|
出版者 |
情報処理学会 |
IPSJ-BIO22069005.pdf (3.9 MB)
