LEADER 03135nam 2200385 450 001 9910557823803321 005 20230221121720.0 035 $a(CKB)5400000000046345 035 $a(NjHacI)995400000000046345 035 $a(EXLCZ)995400000000046345 100 $a20230221d2019 uy 0 101 0 $arus 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$a????????????? ??????? ?????????? ???????????????? ? ??????????? ??????????? ???????? $e?? ????????? ??????????????? ? ?????????????? ?????? /$fEkaterina Evgrashkina 210 1$aBerlin :$cPeter Lang International Academic Publishing Group,$d2019. 215 $a1 online resource (170 pages) 225 1 $aNeuere Lyrik : interkulturelle und interdisziplina?re Studien 330 $a?????? - ??? ?????????? ???????????? ????????????? ????????????? ????? ? ???? ???????????????? ??????? ????????????? ???????. ? ?????? ???????????????? ? ??????????? ??????????? ???????? ????? ???????? ?????????? ?????????, ? ??????? ????????? ???????????? ????? ?????????? ? ?????????? ?????????? ??? ??????, ? ???? ??????? ?????????? ?? ?????????????, ? ?????????? ???????? ??? ???????????? ?????????? ?????? ? ?????????????? ?????????????? ??? ???????? ?????????. ? ????? ?? ????????? ??????? ?? ???????? ? ??????? ?????? ??????????????? ????????????? ?????????? ???????????? ?????????? ? ??????????? ??????, ? ????? ?????????-?????????????? ????????? ? ??????????????? ????????? ?????????? ???????????????? - ?? ??????????? ?????? ? ????????????? ??????????????? ?? ???????? ? ??????????. 410 0$aNeuere Lyrik. 517 $a????????????? ??????? ????????? ???????????????? ? ??????????? ??????????? ???????? 606 $aLanguage and languages 606 $alinguistics 615 0$aLanguage and languages. 615 0$alinguistics. 676 $a400 700 $aEvgrashkina$b Ekaterina$01279155 801 0$bNjHacI 801 1$bNjHacl 906 $aBOOK 912 $a9910557823803321 996 $a????????????? ??????? ?????????? ???????????????? ? ??????????? ??????????? ????????$93014684 997 $aUNINA LEADER 04695nam 22005655 450 001 9911049216903321 005 20260102122945.0 010 $a3-032-10081-X 024 7 $a10.1007/978-3-032-10081-8 035 $a(CKB)44770036800041 035 $a(MiAaPQ)EBC32471024 035 $a(Au-PeEL)EBL32471024 035 $a(DE-He213)978-3-032-10081-8 035 $a(EXLCZ)9944770036800041 100 $a20260102d2025 u| 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aActivity Cliffs $eWhere QSAR Predictions Fail /$fby Kunal Roy, Arkaprava Banerjee 205 $a1st ed. 2025. 210 1$aCham :$cSpringer Nature Switzerland :$cImprint: Springer,$d2025. 215 $a1 online resource (128 pages) 225 1 $aSpringerBriefs in Molecular Science,$x2191-5415 311 08$a3-032-10080-1 327 $aQSAR ? A tool of Predictive Cheminformatics -- Outliers and the Applicability Domain of QSAR models -- Cliffs in Biological Activity Landscape -- The Arithmetic Residuals in K-Group Analysis (ARKA) for the Detection of Activity Cliffs -- Activity Cliffs and Dataset Modelability ? Future Roadmaps. 330 $aThis brief introduces the readers of predictive cheminformatics to the concept of cliffs in the structure-activity landscape, which may greatly affect the data set modelability and the quality of predictions, hence generating disappointment from the performance of Quantitative Structure-Activity Relationship (QSAR) models. Although QSAR models are based on the assumption of a smooth activity landscape, where similar molecules are expected to have similar activities, some similar molecules can occasionally exhibit large differences in activity (for example, 100-fold). The definition of similarity for identifying activity cliffs may be based on chemical fingerprints or descriptors (classical activity cliffs), substructures (chirality cliffs, matched molecular pair cliffs), three-dimensional structure-based cliffs (3D cliffs), or the target-set-dependent potency difference. Some prediction outliers, even within the applicability domain of QSAR models, may arise due to the activity cliff (AC) behavior. In addition to compound pairs, activity cliffs may also be visualized in coordinated networks forming AC clusters. Despite using high-quality data, the data set's modelability may be significantly compromised in the presence of ACs, among other factors. The modelability of the dataset has been studied using different approaches like modelability index (MODI), weighted modelability index (WMODI), rivality index, etc. At the same time, the applicability domain of QSAR models is evaluated using a variety of methods, including leverage, principal components, standardization methods, and distance to the model in X-space, among others. Different methods for identifying activity cliffs have been proposed, such as the structure-activity landscape index (SALI), the structure-activity relationship (SAR) index, and the structure-activity similarity (SAS) maps. Recently, the Arithmetic Residuals in K-Groups Analysis (ARKA) has been shown to be successful in identifying activity cliffs. This approach has also been applied in small data set classification modeling. A multiclass ARKA approach has also been developed for its possible application in regression-based problems by integrating it with the quantitative read-across structure-activity relationship (q-RASAR) framework. This book showcases the evolution and the current status of the concept of activity cliffs as relevant to QSAR predictions and indicates the future directions in the research on activity cliffs. Researchers in the fields of medicinal chemistry, predictive toxicology, nanosciences, food science, agricultural sciences, and materials informatics should benefit from the concept of activity cliffs, impacting model-derived predictions. . 410 0$aSpringerBriefs in Molecular Science,$x2191-5415 606 $aCheminformatics 606 $aChemistry$xData processing 606 $aMachine learning 606 $aCheminformatics 606 $aComputational Chemistry 606 $aMachine Learning 615 0$aCheminformatics. 615 0$aChemistry$xData processing. 615 0$aMachine learning. 615 14$aCheminformatics. 615 24$aComputational Chemistry. 615 24$aMachine Learning. 676 $a542.85 700 $aRoy$b Kunal$0929716 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9911049216903321 996 $aActivity Cliffs$94521896 997 $aUNINA