| 书目名称 | q-RASAR |
| 副标题 | A Path to Predictive |
| 编辑 | Kunal Roy,Arkaprava Banerjee |
| 视频video | |
| 概述 | Introduces the reader to a novel cheminformatic workflow.Presents the genesis and model development.Includes practical examples and software tools |
| 丛书名称 | SpringerBriefs in Molecular Science |
| 图书封面 |  |
| 描述 | .This brief offers an introduction to the fascinating new field of quantitative read-across structure-activity relationships (q-RASAR) as a cheminformatics modeling approach in the background of quantitative structure-activity relationships (QSAR) and read-across (RA) as data gap-filling methods. It discusses the genesis and model development of q-RASAR models demonstrating practical examples. It also showcases successful case studies on the application of q-RASAR modeling in medicinal chemistry, predictive toxicology, and materials sciences. The book also includes the tools used for q-RASAR model development for new users. It is a valuable resource for researchers and students interested in grasping the development algorithm of q-RASAR models and their application within specific research domains.. |
| 出版日期 | Book 2024 |
| 关键词 | QSAR; Read-across; q-RASAR; Cheminformatics; Chemometrics; Machine Learning; Predictions; Validation; Data G |
| 版次 | 1 |
| doi | https://doi.org/10.1007/978-3-031-52057-0 |
| isbn_softcover | 978-3-031-52056-3 |
| isbn_ebook | 978-3-031-52057-0Series ISSN 2191-5407 Series E-ISSN 2191-5415 |
| issn_series | 2191-5407 |
| copyright | The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 |
发表于 2025-3-21 19:24:51
