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10.1038/s41467-020-17035-5 http://scihub22266oqcxt.onion/10.1038/s41467-020-17035-5 32591514!7319956!32591514     free     free     free Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 245.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Nat+Commun 2020 ; 11 (1): 3230 Nephropedia Template TP {{tp|p=32591514|t=2020. Representation of molecular structures with persistent homology for machine learning applications in chemistry |pdf=|usr=}}{{32591514}} gab.com Text Representation of molecular structures with persistent homology for machine learning applications in chemistry JO: Nat Commun http://www.kidney.de/pget.php?&tw=1&pmid=32591514 #FOAvip Machine learning and high-throughput computational screening have been valuable tools in accelerated first-principles screening for the discovery of the next generation of functionalized molecules and materials. The application of machine learning for chemical applications requires the conversion of molecular structures to a machine-readable format known as a molecular representation. The choice of such representations impacts the performance and outcomes of chemical machine learning methods. Herein, we present a new concise molecular representation derived from persistent homology, an applied branch of mathematics. We have demonstrated its applicability in a high-throughput computational screening of a large molecular database (GDB-9) with more than 133,000 organic molecules. Our target is to identify novel molecules that selectively interact with CO(2). The methodology and performance of the novel molecular fingerprinting method is presented and the new chemically-driven persistence image representation is used to screen the GDB-9 database to suggest molecules and/or functional groups with enhanced properties. Twit Text FOAVip Representation of molecular structures with persistent homology for machine learning applications in chemistry ????Nat Commun http://www.kidney.de/pget.php?&tw=1&pmid=32591514 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32591514 #FOAvip English Wikipedia <ref>{{Cite pmid|32591514}}</ref> Representation of molecular structures with persistent homology for machine learning applications in chemistry #MMPMID32591514 Townsend J; Micucci CP; Hymel JH; Maroulas V; Vogiatzis KD Nat Commun 2020[Jun]; 11 (1): 3230 PMID32591514show ga Machine learning and high-throughput computational screening have been valuable tools in accelerated first-principles screening for the discovery of the next generation of functionalized molecules and materials. The application of machine learning for chemical applications requires the conversion of molecular structures to a machine-readable format known as a molecular representation. The choice of such representations impacts the performance and outcomes of chemical machine learning methods. Herein, we present a new concise molecular representation derived from persistent homology, an applied branch of mathematics. We have demonstrated its applicability in a high-throughput computational screening of a large molecular database (GDB-9) with more than 133,000 organic molecules. Our target is to identify novel molecules that selectively interact with CO(2). The methodology and performance of the novel molecular fingerprinting method is presented and the new chemically-driven persistence image representation is used to screen the GDB-9 database to suggest molecules and/or functional groups with enhanced properties. äRepresentation of molecular structures with persistent homology for ma(epmc).pdf DeepDyve Pubget Overpricing