DAFT
发表于 2025-3-23 12:25:52
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免除责任
发表于 2025-3-23 17:19:28
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
虚度
发表于 2025-3-23 21:16:37
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夹死提手势
发表于 2025-3-24 00:54:43
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
直言不讳
发表于 2025-3-24 03:03:59
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冲击力
发表于 2025-3-24 07:03:33
,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
foliage
发表于 2025-3-24 13:53:25
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确定无疑
发表于 2025-3-24 16:22:35
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
pellagra
发表于 2025-3-24 22:49:36
2191-5407tools.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
滔滔不绝地说
发表于 2025-3-25 00:00:29
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..