| 000 | 01054 a2200313 4500 | ||
|---|---|---|---|
| 005 | 20250518014753.0 | ||
| 264 | 0 | _c20190122 | |
| 008 | 201901s 0 0 eng d | ||
| 022 | _a1089-7690 | ||
| 024 | 7 |
_a10.1063/1.5063533 _2doi |
|
| 040 |
_aNLM _beng _cNLM |
||
| 100 | 1 | _aKlus, Stefan | |
| 245 | 0 | 0 |
_aA kernel-based approach to molecular conformation analysis. _h[electronic resource] |
| 260 |
_bThe Journal of chemical physics _cDec 2018 |
||
| 300 |
_a244109 p. _bdigital |
||
| 500 | _aPublication Type: Journal Article | ||
| 650 | 0 | 4 | _aAlgorithms |
| 650 | 0 | 4 |
_aDipeptides _xchemistry |
| 650 | 0 | 4 | _aMachine Learning |
| 650 | 0 | 4 | _aModels, Theoretical |
| 650 | 0 | 4 | _aMolecular Dynamics Simulation |
| 650 | 0 | 4 | _aProtein Conformation |
| 650 | 0 | 4 |
_aProteins _xchemistry |
| 700 | 1 | _aBittracher, Andreas | |
| 700 | 1 | _aSchuster, Ingmar | |
| 700 | 1 | _aSchütte, Christof | |
| 773 | 0 |
_tThe Journal of chemical physics _gvol. 149 _gno. 24 _gp. 244109 |
|
| 856 | 4 | 0 |
_uhttps://doi.org/10.1063/1.5063533 _zAvailable from publisher's website |
| 999 |
_c29211617 _d29211617 |
||