Matches in SemOpenAlex for { <https://semopenalex.org/work/W4296035488> ?p ?o ?g. }
- W4296035488 endingPage "4671" @default.
- W4296035488 startingPage "4660" @default.
- W4296035488 abstract "In molecular discovery and drug design, structure-property relationships and activity landscapes are often qualitatively or quantitatively analyzed to guide the navigation of chemical space. The roughness (or smoothness) of these molecular property landscapes is one of their most studied geometric attributes, as it can characterize the presence of activity cliffs, with rougher landscapes generally expected to pose tougher optimization challenges. Here, we introduce a general, quantitative measure for describing the roughness of molecular property landscapes. The proposed roughness index (ROGI) is loosely inspired by the concept of fractal dimension and strongly correlates with the out-of-sample error achieved by machine learning models on numerous regression tasks." @default.
- W4296035488 created "2022-09-17" @default.
- W4296035488 creator A5017287886 @default.
- W4296035488 creator A5040280528 @default.
- W4296035488 creator A5056535696 @default.
- W4296035488 creator A5072958773 @default.
- W4296035488 creator A5076162644 @default.
- W4296035488 creator A5084050064 @default.
- W4296035488 creator A5090160831 @default.
- W4296035488 date "2022-09-16" @default.
- W4296035488 modified "2023-10-02" @default.
- W4296035488 title "Roughness of Molecular Property Landscapes and Its Impact on Modellability" @default.
- W4296035488 cites W128607844 @default.
- W4296035488 cites W1505823450 @default.
- W4296035488 cites W1632595382 @default.
- W4296035488 cites W1757990252 @default.
- W4296035488 cites W1766496453 @default.
- W4296035488 cites W1965313623 @default.
- W4296035488 cites W1969324628 @default.
- W4296035488 cites W1977573154 @default.
- W4296035488 cites W1990150080 @default.
- W4296035488 cites W1990451437 @default.
- W4296035488 cites W1995945562 @default.
- W4296035488 cites W2007344527 @default.
- W4296035488 cites W2008505552 @default.
- W4296035488 cites W2009456532 @default.
- W4296035488 cites W2012444866 @default.
- W4296035488 cites W2014110208 @default.
- W4296035488 cites W2014858249 @default.
- W4296035488 cites W2015558379 @default.
- W4296035488 cites W2019887671 @default.
- W4296035488 cites W2020987809 @default.
- W4296035488 cites W2022478977 @default.
- W4296035488 cites W2025556769 @default.
- W4296035488 cites W2032733199 @default.
- W4296035488 cites W2034541070 @default.
- W4296035488 cites W2034549041 @default.
- W4296035488 cites W2039609876 @default.
- W4296035488 cites W2040651998 @default.
- W4296035488 cites W2042202064 @default.
- W4296035488 cites W2043202573 @default.
- W4296035488 cites W2047603685 @default.
- W4296035488 cites W2052528093 @default.
- W4296035488 cites W2054203041 @default.
- W4296035488 cites W2061013156 @default.
- W4296035488 cites W2061496892 @default.
- W4296035488 cites W2066273100 @default.
- W4296035488 cites W2072582769 @default.
- W4296035488 cites W2080355854 @default.
- W4296035488 cites W2083051183 @default.
- W4296035488 cites W2096541451 @default.
- W4296035488 cites W2096705340 @default.
- W4296035488 cites W2108458189 @default.
- W4296035488 cites W2112415299 @default.
- W4296035488 cites W2127695578 @default.
- W4296035488 cites W2136842561 @default.
- W4296035488 cites W2151697120 @default.
- W4296035488 cites W2161613665 @default.
- W4296035488 cites W2174836755 @default.
- W4296035488 cites W2191748928 @default.
- W4296035488 cites W2220807183 @default.
- W4296035488 cites W2234529989 @default.
- W4296035488 cites W2276859037 @default.
- W4296035488 cites W2305293558 @default.
- W4296035488 cites W2315837940 @default.
- W4296035488 cites W2330317998 @default.
- W4296035488 cites W2406943157 @default.
- W4296035488 cites W2410846838 @default.
- W4296035488 cites W2461620095 @default.
- W4296035488 cites W2473190403 @default.
- W4296035488 cites W2481808473 @default.
- W4296035488 cites W2511526876 @default.
- W4296035488 cites W2520361523 @default.
- W4296035488 cites W2594183968 @default.
- W4296035488 cites W2754478492 @default.
- W4296035488 cites W2759402621 @default.
- W4296035488 cites W2889157215 @default.
- W4296035488 cites W2892349988 @default.
- W4296035488 cites W2940024920 @default.
- W4296035488 cites W2953128081 @default.
- W4296035488 cites W2966797369 @default.
- W4296035488 cites W2970175280 @default.
- W4296035488 cites W3012585755 @default.
- W4296035488 cites W3030193057 @default.
- W4296035488 cites W3086749236 @default.
- W4296035488 cites W3125182304 @default.
- W4296035488 cites W3135127269 @default.
- W4296035488 cites W3136380135 @default.
- W4296035488 cites W4220724035 @default.
- W4296035488 doi "https://doi.org/10.1021/acs.jcim.2c00903" @default.
- W4296035488 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/36112568" @default.
- W4296035488 hasPublicationYear "2022" @default.
- W4296035488 type Work @default.
- W4296035488 citedByCount "7" @default.
- W4296035488 countsByYear W42960354882022 @default.
- W4296035488 countsByYear W42960354882023 @default.
- W4296035488 crossrefType "journal-article" @default.
- W4296035488 hasAuthorship W4296035488A5017287886 @default.