Antisymmetry benchmark as used in Dieckhaus et al. Table 3.
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13 evaluations · 50 results. Different protocols are not a single leaderboard.
Applied filters: All linked evaluations
| Tested configuration | Protocol and dataset | Finding | Evidence and details |
|---|---|---|---|
| Configuration: ABYSSAL (Dieckhaus et al. 2024) | Protocol: Ssym direct and inverse ddG prediction (Dieckhaus et al. 2024 Table 3) Dataset: Ssym (342 direct and 342 inverse variants with experimental structures) | 0.46 pearson-correlation unitless · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Result quoted from another source · Source checkedMethods, coverage and sourceABYSSAL on Ssym direct and inverse (Dieckhaus et al. 2024) protein-stability-20261009-protocol-dieckhaus2024-ssym Aggregation: Not reported Transfer learning to leverage larger datasets for improved prediction of protein stability changes · Table 3, row 'ABYSSAL ( 16 )', column 'Ssym (direct) PCC' |
| Configuration: ABYSSAL (Dieckhaus et al. 2024) | Protocol: Ssym direct and inverse ddG prediction (Dieckhaus et al. 2024 Table 3) Dataset: Ssym (342 direct and 342 inverse variants with experimental structures) | 0.44 pearson-correlation unitless · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Result quoted from another source · Source checkedMethods, coverage and sourceABYSSAL on Ssym direct and inverse (Dieckhaus et al. 2024) protein-stability-20261009-protocol-dieckhaus2024-ssym Aggregation: Not reported Transfer learning to leverage larger datasets for improved prediction of protein stability changes · Table 3, row 'ABYSSAL ( 16 )', column 'Ssym (inverse) PCC' |
| Configuration: ACDC-NN (Dieckhaus et al. 2024) | Protocol: Ssym direct and inverse ddG prediction (Dieckhaus et al. 2024 Table 3) Dataset: Ssym (342 direct and 342 inverse variants with experimental structures) | 0.58 pearson-correlation unitless · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Result quoted from another source · Source checkedMethods, coverage and sourceACDC-NN on Ssym direct and inverse (Dieckhaus et al. 2024) protein-stability-20261009-protocol-dieckhaus2024-ssym Aggregation: Not reported Transfer learning to leverage larger datasets for improved prediction of protein stability changes · Table 3, row 'ACDC-NN ( 30 , 36 )', column 'Ssym (direct) PCC' |
| Configuration: ACDC-NN (Dieckhaus et al. 2024) | Protocol: Ssym direct and inverse ddG prediction (Dieckhaus et al. 2024 Table 3) Dataset: Ssym (342 direct and 342 inverse variants with experimental structures) | 1.42 root-mean-squared-error kilocalorie-per-mole · lower Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Result quoted from another source · Source checkedMethods, coverage and sourceACDC-NN on Ssym direct and inverse (Dieckhaus et al. 2024) protein-stability-20261009-protocol-dieckhaus2024-ssym Aggregation: Not reported Transfer learning to leverage larger datasets for improved prediction of protein stability changes · Table 3, row 'ACDC-NN ( 30 , 36 )', column 'Ssym (direct) RMSE (kcal/mol)' |
| Configuration: ACDC-NN (Dieckhaus et al. 2024) | Protocol: Ssym direct and inverse ddG prediction (Dieckhaus et al. 2024 Table 3) Dataset: Ssym (342 direct and 342 inverse variants with experimental structures) | 0.55 pearson-correlation unitless · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Result quoted from another source · Source checkedMethods, coverage and sourceACDC-NN on Ssym direct and inverse (Dieckhaus et al. 2024) protein-stability-20261009-protocol-dieckhaus2024-ssym Aggregation: Not reported Transfer learning to leverage larger datasets for improved prediction of protein stability changes · Table 3, row 'ACDC-NN ( 30 , 36 )', column 'Ssym (inverse) PCC' |
| Configuration: ACDC-NN (Dieckhaus et al. 2024) | Protocol: Ssym direct and inverse ddG prediction (Dieckhaus et al. 2024 Table 3) Dataset: Ssym (342 direct and 342 inverse variants with experimental structures) | 1.47 root-mean-squared-error kilocalorie-per-mole · lower Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Result quoted from another source · Source checkedMethods, coverage and sourceACDC-NN on Ssym direct and inverse (Dieckhaus et al. 2024) protein-stability-20261009-protocol-dieckhaus2024-ssym Aggregation: Not reported Transfer learning to leverage larger datasets for improved prediction of protein stability changes · Table 3, row 'ACDC-NN ( 30 , 36 )', column 'Ssym (inverse) RMSE (kcal/mol)' |
| Configuration: FoldX (Dieckhaus et al. 2024) | Protocol: Ssym direct and inverse ddG prediction (Dieckhaus et al. 2024 Table 3) Dataset: Ssym (342 direct and 342 inverse variants with experimental structures) | 0.63 pearson-correlation unitless · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Result quoted from another source · Source checkedMethods, coverage and sourceFoldX on Ssym direct and inverse (Dieckhaus et al. 2024) protein-stability-20261009-protocol-dieckhaus2024-ssym Aggregation: Not reported Transfer learning to leverage larger datasets for improved prediction of protein stability changes · Table 3, row 'FoldX ( 29 , 30 )', column 'Ssym (direct) PCC' |
| Configuration: FoldX (Dieckhaus et al. 2024) | Protocol: Ssym direct and inverse ddG prediction (Dieckhaus et al. 2024 Table 3) Dataset: Ssym (342 direct and 342 inverse variants with experimental structures) | 1.56 root-mean-squared-error kilocalorie-per-mole · lower Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Result quoted from another source · Source checkedMethods, coverage and sourceFoldX on Ssym direct and inverse (Dieckhaus et al. 2024) protein-stability-20261009-protocol-dieckhaus2024-ssym Aggregation: Not reported Transfer learning to leverage larger datasets for improved prediction of protein stability changes · Table 3, row 'FoldX ( 29 , 30 )', column 'Ssym (direct) RMSE (kcal/mol)' |
| Configuration: FoldX (Dieckhaus et al. 2024) | Protocol: Ssym direct and inverse ddG prediction (Dieckhaus et al. 2024 Table 3) Dataset: Ssym (342 direct and 342 inverse variants with experimental structures) | 0.39 pearson-correlation unitless · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Result quoted from another source · Source checkedMethods, coverage and sourceFoldX on Ssym direct and inverse (Dieckhaus et al. 2024) protein-stability-20261009-protocol-dieckhaus2024-ssym Aggregation: Not reported Transfer learning to leverage larger datasets for improved prediction of protein stability changes · Table 3, row 'FoldX ( 29 , 30 )', column 'Ssym (inverse) PCC' |
| Configuration: FoldX (Dieckhaus et al. 2024) | Protocol: Ssym direct and inverse ddG prediction (Dieckhaus et al. 2024 Table 3) Dataset: Ssym (342 direct and 342 inverse variants with experimental structures) | 2.13 root-mean-squared-error kilocalorie-per-mole · lower Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Result quoted from another source · Source checkedMethods, coverage and sourceFoldX on Ssym direct and inverse (Dieckhaus et al. 2024) protein-stability-20261009-protocol-dieckhaus2024-ssym Aggregation: Not reported Transfer learning to leverage larger datasets for improved prediction of protein stability changes · Table 3, row 'FoldX ( 29 , 30 )', column 'Ssym (inverse) RMSE (kcal/mol)' |
| Configuration: MAESTRO (Dieckhaus et al. 2024) | Protocol: Ssym direct and inverse ddG prediction (Dieckhaus et al. 2024 Table 3) Dataset: Ssym (342 direct and 342 inverse variants with experimental structures) | 0.52 pearson-correlation unitless · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Result quoted from another source · Source checkedMethods, coverage and sourceMAESTRO on Ssym direct and inverse (Dieckhaus et al. 2024) protein-stability-20261009-protocol-dieckhaus2024-ssym Aggregation: Not reported Transfer learning to leverage larger datasets for improved prediction of protein stability changes · Table 3, row 'MAESTRO ( 29 , 30 )', column 'Ssym (direct) PCC' |
| Configuration: MAESTRO (Dieckhaus et al. 2024) | Protocol: Ssym direct and inverse ddG prediction (Dieckhaus et al. 2024 Table 3) Dataset: Ssym (342 direct and 342 inverse variants with experimental structures) | 1.36 root-mean-squared-error kilocalorie-per-mole · lower Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Result quoted from another source · Source checkedMethods, coverage and sourceMAESTRO on Ssym direct and inverse (Dieckhaus et al. 2024) protein-stability-20261009-protocol-dieckhaus2024-ssym Aggregation: Not reported Transfer learning to leverage larger datasets for improved prediction of protein stability changes · Table 3, row 'MAESTRO ( 29 , 30 )', column 'Ssym (direct) RMSE (kcal/mol)' |
| Configuration: MAESTRO (Dieckhaus et al. 2024) | Protocol: Ssym direct and inverse ddG prediction (Dieckhaus et al. 2024 Table 3) Dataset: Ssym (342 direct and 342 inverse variants with experimental structures) | 0.32 pearson-correlation unitless · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Result quoted from another source · Source checkedMethods, coverage and sourceMAESTRO on Ssym direct and inverse (Dieckhaus et al. 2024) protein-stability-20261009-protocol-dieckhaus2024-ssym Aggregation: Not reported Transfer learning to leverage larger datasets for improved prediction of protein stability changes · Table 3, row 'MAESTRO ( 29 , 30 )', column 'Ssym (inverse) PCC' |
| Configuration: MAESTRO (Dieckhaus et al. 2024) | Protocol: Ssym direct and inverse ddG prediction (Dieckhaus et al. 2024 Table 3) Dataset: Ssym (342 direct and 342 inverse variants with experimental structures) | 2.09 root-mean-squared-error kilocalorie-per-mole · lower Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Result quoted from another source · Source checkedMethods, coverage and sourceMAESTRO on Ssym direct and inverse (Dieckhaus et al. 2024) protein-stability-20261009-protocol-dieckhaus2024-ssym Aggregation: Not reported Transfer learning to leverage larger datasets for improved prediction of protein stability changes · Table 3, row 'MAESTRO ( 29 , 30 )', column 'Ssym (inverse) RMSE (kcal/mol)' |
| Configuration: mCSM (Dieckhaus et al. 2024) | Protocol: Ssym direct and inverse ddG prediction (Dieckhaus et al. 2024 Table 3) Dataset: Ssym (342 direct and 342 inverse variants with experimental structures) | 0.61 pearson-correlation unitless · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Result quoted from another source · Source checkedMethods, coverage and sourcemCSM on Ssym direct and inverse (Dieckhaus et al. 2024) protein-stability-20261009-protocol-dieckhaus2024-ssym Aggregation: Not reported Transfer learning to leverage larger datasets for improved prediction of protein stability changes · Table 3, row 'mCSM ( 29 , 30 )', column 'Ssym (direct) PCC' |
| Configuration: mCSM (Dieckhaus et al. 2024) | Protocol: Ssym direct and inverse ddG prediction (Dieckhaus et al. 2024 Table 3) Dataset: Ssym (342 direct and 342 inverse variants with experimental structures) | 1.23 root-mean-squared-error kilocalorie-per-mole · lower Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Result quoted from another source · Source checkedMethods, coverage and sourcemCSM on Ssym direct and inverse (Dieckhaus et al. 2024) protein-stability-20261009-protocol-dieckhaus2024-ssym Aggregation: Not reported Transfer learning to leverage larger datasets for improved prediction of protein stability changes · Table 3, row 'mCSM ( 29 , 30 )', column 'Ssym (direct) RMSE (kcal/mol)' |
| Configuration: mCSM (Dieckhaus et al. 2024) | Protocol: Ssym direct and inverse ddG prediction (Dieckhaus et al. 2024 Table 3) Dataset: Ssym (342 direct and 342 inverse variants with experimental structures) | 0.14 pearson-correlation unitless · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Result quoted from another source · Source checkedMethods, coverage and sourcemCSM on Ssym direct and inverse (Dieckhaus et al. 2024) protein-stability-20261009-protocol-dieckhaus2024-ssym Aggregation: Not reported Transfer learning to leverage larger datasets for improved prediction of protein stability changes · Table 3, row 'mCSM ( 29 , 30 )', column 'Ssym (inverse) PCC' |
| Configuration: mCSM (Dieckhaus et al. 2024) | Protocol: Ssym direct and inverse ddG prediction (Dieckhaus et al. 2024 Table 3) Dataset: Ssym (342 direct and 342 inverse variants with experimental structures) | 2.43 root-mean-squared-error kilocalorie-per-mole · lower Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Result quoted from another source · Source checkedMethods, coverage and sourcemCSM on Ssym direct and inverse (Dieckhaus et al. 2024) protein-stability-20261009-protocol-dieckhaus2024-ssym Aggregation: Not reported Transfer learning to leverage larger datasets for improved prediction of protein stability changes · Table 3, row 'mCSM ( 29 , 30 )', column 'Ssym (inverse) RMSE (kcal/mol)' |
| Configuration: MUPRO (Dieckhaus et al. 2024) | Protocol: Ssym direct and inverse ddG prediction (Dieckhaus et al. 2024 Table 3) Dataset: Ssym (342 direct and 342 inverse variants with experimental structures) | 0.79 pearson-correlation unitless · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Result quoted from another source · Source checkedMethods, coverage and sourceMUPRO on Ssym direct and inverse (Dieckhaus et al. 2024) protein-stability-20261009-protocol-dieckhaus2024-ssym Aggregation: Not reported Transfer learning to leverage larger datasets for improved prediction of protein stability changes · Table 3, row 'MUPRO ( 29 , 30 )', column 'Ssym (direct) PCC' |
| Configuration: MUPRO (Dieckhaus et al. 2024) | Protocol: Ssym direct and inverse ddG prediction (Dieckhaus et al. 2024 Table 3) Dataset: Ssym (342 direct and 342 inverse variants with experimental structures) | 0.94 root-mean-squared-error kilocalorie-per-mole · lower Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Result quoted from another source · Source checkedMethods, coverage and sourceMUPRO on Ssym direct and inverse (Dieckhaus et al. 2024) protein-stability-20261009-protocol-dieckhaus2024-ssym Aggregation: Not reported Transfer learning to leverage larger datasets for improved prediction of protein stability changes · Table 3, row 'MUPRO ( 29 , 30 )', column 'Ssym (direct) RMSE (kcal/mol)' |
| Configuration: MUPRO (Dieckhaus et al. 2024) | Protocol: Ssym direct and inverse ddG prediction (Dieckhaus et al. 2024 Table 3) Dataset: Ssym (342 direct and 342 inverse variants with experimental structures) | 0.07 pearson-correlation unitless · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Result quoted from another source · Source checkedMethods, coverage and sourceMUPRO on Ssym direct and inverse (Dieckhaus et al. 2024) protein-stability-20261009-protocol-dieckhaus2024-ssym Aggregation: Not reported Transfer learning to leverage larger datasets for improved prediction of protein stability changes · Table 3, row 'MUPRO ( 29 , 30 )', column 'Ssym (inverse) PCC' |
| Configuration: MUPRO (Dieckhaus et al. 2024) | Protocol: Ssym direct and inverse ddG prediction (Dieckhaus et al. 2024 Table 3) Dataset: Ssym (342 direct and 342 inverse variants with experimental structures) | 2.51 root-mean-squared-error kilocalorie-per-mole · lower Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Result quoted from another source · Source checkedMethods, coverage and sourceMUPRO on Ssym direct and inverse (Dieckhaus et al. 2024) protein-stability-20261009-protocol-dieckhaus2024-ssym Aggregation: Not reported Transfer learning to leverage larger datasets for improved prediction of protein stability changes · Table 3, row 'MUPRO ( 29 , 30 )', column 'Ssym (inverse) RMSE (kcal/mol)' |
| Configuration: PROSTATA (Dieckhaus et al. 2024) | Protocol: Ssym direct and inverse ddG prediction (Dieckhaus et al. 2024 Table 3) Dataset: Ssym (342 direct and 342 inverse variants with experimental structures) | 0.51 pearson-correlation unitless · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Result quoted from another source · Source checkedMethods, coverage and sourcePROSTATA on Ssym direct and inverse (Dieckhaus et al. 2024) protein-stability-20261009-protocol-dieckhaus2024-ssym Aggregation: Not reported Transfer learning to leverage larger datasets for improved prediction of protein stability changes · Table 3, row 'PROSTATA ( 17 )', column 'Ssym (direct) PCC' |
| Configuration: PROSTATA (Dieckhaus et al. 2024) | Protocol: Ssym direct and inverse ddG prediction (Dieckhaus et al. 2024 Table 3) Dataset: Ssym (342 direct and 342 inverse variants with experimental structures) | 1.42 root-mean-squared-error kilocalorie-per-mole · lower Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Result quoted from another source · Source checkedMethods, coverage and sourcePROSTATA on Ssym direct and inverse (Dieckhaus et al. 2024) protein-stability-20261009-protocol-dieckhaus2024-ssym Aggregation: Not reported Transfer learning to leverage larger datasets for improved prediction of protein stability changes · Table 3, row 'PROSTATA ( 17 )', column 'Ssym (direct) RMSE (kcal/mol)' |
| Configuration: PROSTATA (Dieckhaus et al. 2024) | Protocol: Ssym direct and inverse ddG prediction (Dieckhaus et al. 2024 Table 3) Dataset: Ssym (342 direct and 342 inverse variants with experimental structures) | 0.5 pearson-correlation unitless · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Result quoted from another source · Source checkedMethods, coverage and sourcePROSTATA on Ssym direct and inverse (Dieckhaus et al. 2024) protein-stability-20261009-protocol-dieckhaus2024-ssym Aggregation: Not reported Transfer learning to leverage larger datasets for improved prediction of protein stability changes · Table 3, row 'PROSTATA ( 17 )', column 'Ssym (inverse) PCC' |
Source checking is not independent reproduction. Release 2026-10-10-6e93f504adfc.
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5 evidence rows matching the loaded filters
| Property and statement | Original source and location | Review and provenance |
|---|---|---|
| attributes.population Ssym direct and inverse sets (342 each, per the literature definition); counts not printed in Table 3 Context-only references | Transfer learning to leverage larger datasets for improved prediction of protein stability changes Table 3 Version: PNAS 121(6):e2314853121, published 2024-01-29; PMC10861915 full-text XML | not individually reviewed No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| attributes.source_locator Table 3 Context-only references | Transfer learning to leverage larger datasets for improved prediction of protein stability changes Table 3 Version: PNAS 121(6):e2314853121, published 2024-01-29; PMC10861915 full-text XML | not individually reviewed No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| attributes.split External test set Context-only references | Transfer learning to leverage larger datasets for improved prediction of protein stability changes Table 3 Version: PNAS 121(6):e2314853121, published 2024-01-29; PMC10861915 full-text XML | not individually reviewed No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| description Antisymmetry benchmark as used in Dieckhaus et al. Table 3. Context-only references | Transfer learning to leverage larger datasets for improved prediction of protein stability changes Table 3 Version: PNAS 121(6):e2314853121, published 2024-01-29; PMC10861915 full-text XML | not individually reviewed No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| name Ssym (342 direct and 342 inverse variants with experimental structures) Context-only references | Transfer learning to leverage larger datasets for improved prediction of protein stability changes Table 3 Version: PNAS 121(6):e2314853121, published 2024-01-29; PMC10861915 full-text XML | not individually reviewed No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
Release 2026-10-10-6e93f504adfc · Record review: source checked
Stable ID: protein-stability-20261009-data-dieckhaus2024-ssym