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Titlebook: q-RASAR; A Path to Predictive Kunal Roy,Arkaprava Banerjee Book 2024 The Author(s), under exclusive license to Springer Nature Switzerland

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發(fā)表于 2025-3-23 12:25:52 | 只看該作者
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發(fā)表于 2025-3-23 17:19:28 | 只看該作者
Tools, Applications, and Case Studies (q-RA and q-RASAR),of chemical information compared to conventional descriptor-based QSAR modeling approaches. Thus, in most of the examples of modeling biological activity, toxicity, and materials property modeling using the q-RASAR technique presented in this chapter, the q-RASAR models show better quality of predic
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發(fā)表于 2025-3-23 21:16:37 | 只看該作者
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發(fā)表于 2025-3-24 00:54:43 | 只看該作者
Chemical Information and Molecular Similarity,pes, bond types, functionalities, interatomic distances, arrangements of functionality within a molecular skeleton, branching, cyclicity, hydrogen bonding propensity, molecular size, etc. are critical information in determining the interaction of a molecule with other molecules of the same compound
15#
發(fā)表于 2025-3-24 03:03:59 | 只看該作者
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發(fā)表于 2025-3-24 07:03:33 | 只看該作者
,Quantitative Read-Across (q-RA) and Quantitative Read-Across Structure–Activity Relationships (q-RAhown superior performance over QSAR-derived predictions in several examples. This was further extended to the generation of QSAR-like statistical models, i.e., quantitative read-across structure-activity relationship (q-RASAR) by using various similarity and error-based descriptors computed from ori
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發(fā)表于 2025-3-24 13:53:25 | 只看該作者
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發(fā)表于 2025-3-24 16:22:35 | 只看該作者
Future Prospects,, materials science, and predictive toxicology. The similarity metrics and error considerations may be further refined, possibly with the application of sophistical machine learning approaches, for further development of this new field. More extensive applications of q-RA and q-RASAR in medicinal ch
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發(fā)表于 2025-3-24 22:49:36 | 只看該作者
2191-5407 tools.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 metho
20#
發(fā)表于 2025-3-25 00:00:29 | 只看該作者
Book 2024odel 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..
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