Integrating Incompatible Assay Data Sets with Deep Preference Learning-Reference-Cited by-同舟云学术

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Integrating Incompatible Assay Data Sets with Deep Preference Learning

Published:2021-12-29 Issue:1 Volume:13 Page:70-75
ISSN:1948-5875
Container-title:ACS Medicinal Chemistry Letters
language:en
Short-container-title:ACS Med. Chem. Lett.

Author:

Sun Xiaolin¹,Tamura Ryo¹²³⁴^ORCID,Sumita Masato³⁴,Mori Kenichi⁵,Terayama Kei⁴⁶^ORCID,Tsuda Koji¹²⁴^ORCID

Affiliation:

1. Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Chiba 277-8561, Japan

2. Research and Services Division of Materials Data and Integrated System, National Institute for Materials Science, Tsukuba, Ibaraki 305-0047, Japan

3. International Center for Materials Nanoarchitectonics (WPI-MANA), National Institute for Materials Science, Tsukuba, Ibaraki 305-0047, Japan

4. RIKEN Center for Advanced Intelligence Project, Tokyo 103-0027, Japan

5. Astellas Pharma Inc., Tsukuba, Ibaraki 305-8585, Japan

6. Graduate School of Medical Life Science, Yokohama City University, Yokohama 230-0045, Japan

Funder

Astellas Pharma US

China Scholarship Council

Publisher

American Chemical Society (ACS)

Subject

Organic Chemistry,Drug Discovery,Biochemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acsmedchemlett.1c00439

Reference22 articles.

1. Making every SAR point count: the development of Chemistry Connect for the large-scale integration of structure and bioactivity data

2. QSAR Modeling Using Large-Scale Databases: Case Study for HIV-1 Reverse Transcriptase Inhibitors

3. Predictive QSAR modeling of phosphodiesterase 4 inhibitors

4. Ranking-Oriented Quantitative Structure–Activity Relationship Modeling Combined with Assay-Wise Data Integration

5. Factor Xa Inhibitors: Next-Generation Antithrombotic Agents

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