Thermostability Prediction
Collection
This group contains all relative training datasets and model checkpoints trained on these datasets for Thermostability Prediction.
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2 items
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Updated
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2
protein
stringlengths 88
5.39k
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float64 0.01
1
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stringclasses 3
values |
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MdAfLkVaFwVdYfGwTqLcKaRpGpQhPdNnHvRvVlLqRvDdGcAvHqGfShAkAdFwRdAaRkGwRwTfLpEwPaYfPfLfVaIqAdGdEpHqNrIgPtWaLgLaHpLdPtGnSdGfRgLtVaEiGtEtVmYiAdVtDdEpRsMnLvRvFsLvDcDvFvEcSvCpPpApLrYwQhRwTdVkLgRkViQfLgLdEdD#R#A#P#G#A#E#E#P#P#A#P#TrAiVdQiChFtVyYtShRdAhTdFdPdPpEcWrAsQvLhPdHgHdDnSgYdDdSlEpGdPpHvGvLnRhYgNdPdRpEvN#R# | 0.420456 | train |
M#G#K#N#K#L#L#H#P#S#L#V#L#L#L#L#V#L#L#P#T#D#A#S#V#S#G#KdPkQkYkMkVkLkVfPfSlLeLaHeTaEpTdTkEfKkGiCkViLaLiSeYsLdNpEwTwVkTfVkSwAkSwLkEdSdV#R#G#NiRdStLqFdTtDrLdEtA#E#NhDgVdLiHdCmVdAiFgAhVhPhKdSdSpSdNqEkEdVkMiFkLmTkVmQwViKdGtPpTpQdEiFdKiKdRiTaTiVgMmVyKgNyEwDdSkLqVkFdVkQaTwDlKfSqIeYdKePqGqQdTkViKkFmRkViVfSiMaDgEpNlFqHfPaLdNqEdLwIfPaLkVkYfIkQaDdPpKvGrNdRtIqAaQmWdQgSgFdQtLaEdGrGrLmKdQmFdSmFdPgLgShSnEdPfFdQfGfSkYmKwViVwVtQaKhKpSsGrGdRiTdEiHdPiFgTtVyEdEdFdV#LfPdKqFwEdVkQdVwTdVwPqKqIaIdTeIlLpEdEfEkMtNkViSkViCfGiLaYgTqYlGgKqPgVfPwGfHkVkTkVkSkIkCwRwKfYfSpD#A#S#D#C#H#G#E#D#S#Q#AiFqCiEdKiFdSmGdQtLqNdSnHrGnCmFdYmQdQmVdKgTcKvVsFnQnLcKfRwKpErYiEaMqKkLmHkTmEkAmQwItQqEgEpG#T#V#V#EiLdTiGdRiQdSmShEgIyTdRlTeIqTkKdLkSdFwVqKfVfDaSlHaFdRaQaGqIaPwFgFkGtQkViRaLiVdDgGpKsGrVqPgIdPfNqKaVwIkFwIkRdGwNvElAqNpYdYiSdNiAdTtTaDhEnHrGrLmVdQmFdStIgNhTrTnNnVrMpGdTqSkLtTkViRkVmNfYrKdDdR#S#P#C#Y#G#Y#Q#WtVrShErEdHhEhEiAdHiHgTmAhYgLyVaFdSwPpSqKsSkFaVkHdLkEgPaMdSrHdEaLaPeCaGqHdTkQdTkViQkAiHkYiIaLgNdG#GvT#L#L#G#LdKqKkLwSkFkYkYkLfIkMdAwKlGsGhItVdRdTiGdTiHdGiLdLgVdKdQ#E#D#MrKiGdHmFdSmIdSiIhPhVgKhSqDrIrApPqVkAmRkLmLkImYwAtVqLtPsTnGlDiViIhGiDdSmAyKmYhDgVyEpNlCdLaAqNkKaVkDaLwSeFkSvPvSlQeSdLaPaAqSdHkAiHkLiRkViTfAtAaPaQqSwVkCkAwLkRwAkVfDfQpSsVsLcLsMvKpP#D#A#EdLdSdAsSvSvVsYlNvLsLrPpEgKgDaLdTaGfFrP#G#P#L#N#D#Q#D#N#E#D#C#I#N#R#H#N#V#Y#I#N#G#I#T#Y#T#P#V#S#S#T#NpEhKdDwMqYvSnFnLsErDrMsGsLmKdAmFdTmNpSgKdIgRdKdPhKhMhC#P#Q#L#Q#Q#Y#E#M#H#G#P#E#G#L#R#V#G#F#Y#E#S#D#V#M#G#R#G#H#A#R#L#V#H#V#E#E#P#H#T#E#T#V#R#K#YfFdPdErTiWqIdWgDdLiVdVtVaNhSrAvGrVmAdEmVdGiVgTtVhPhDnTdIqTrEkWmKwAmGwAmFwCiLhSdEsDvArGrLiGdIiSySdTiAySiLhRgAhFdQdPqFwFeVwEdLwTdMdPfYqSaVaIeRaGfEwAkFfTkLiKkAiTkViLfNhYqLdPqKaCkIfRfVkSqVkQdLkEdAdSdPpAqFkLdAwVdPwVpEdK#E#Q#A#PgHdCtIaChAhNrGgRmQdTiVtSiWiAtVmTgPgKhSdLaGaNkVdNkFiTkVmSkAmEaAtLdEqSdQ#E#LdC#G#T#E#VrPhSdVqP#E#H#G#RrKmDyTiVyIiKdPiLyLhVyEdPyEdGfLdEkKdEkTdTkFfNkSdLkLdCfPdSpGqGdEkVdSkEdEkLdSfLfKaLdPdPpNqVfVgEpEpSqAkRwAkSkVkSkVkLfGqDaIlLcGlSlAcMlQvNqTlQvNpLqL#Q#M#P#Y#G#CwGlElQsNlMvVsLsFaAqPlNlIlYlVvLvDvYlLcNvElTlQvQnLdTdPpEvIsKlSvKvAsIlGvYsLnNsTnGsYlQsRsQnLvNcYqKaHdYpDqGlSaYgSdTgFqGhEdR#Y#G#R#NfQdGgNwTlWlLsTlAlFvVcLlKlTrFlAlQsAcRvAvYrIgFdIdDpEpAcHnInTvQsAsLvIvWvLqSlQvRqQqKdDpNqGlCfFgRdSgSgGgS#L#L#N#N#A#I#K#G#G#V#E#D#EsVlTqLsSlAlYsIsTlIlAsLcLlEsIsPvLdTqVcTpHpPpVsVnRvNsAsLvFsCnLnEvSvAvWqKvTvAqQvEvG#D#H#G#ShHwVlYlTsKlAlLsLsAlYlAsFcAlLsAnGvNvQvDvKvRnKvEvVsLlKvSvLnNvEvEqAwVdKdKdDpNlSaViHwWhEdRgP#Q#K#P#K#A#P#V#G#H#F#Y#E#P#QaAdPfSlAsErVlElMsTlSlYsVnLlLnAsYqLqTsAhQpPpAgPhTdSpEvDsLlTsSnAsTvNrIsVvKsWnInTsKrQqQrNdAsQsGsGaFhSqSaTsQrDsTrVsVsAnLsHsAsLsSsKsYnGcAsAnTpFdTdR#T#G#KdAkAkQkVkTwIkQdSwSpGdTpFdSiSdKiFdQiVdDdNnNvNcRnLsLpLiQdQmVdSiLdPdEdLpPsGtEmYmSmMiKmVmTrGdErGgCmVmYmLmQmTmSmLiKmYtNrIgLhPdEdK#E#E#FaPqFkAdLkGdVkQaTkLvPvQlTaCpDpEdPpKcAlHqTfSkFiQkIiSkLiSkViSaYgTnGhSpRdSqAkSfNrMkAkIkVkDkVwKfMaVwStGqFkIaPfLpKvPvTqVlKvMvLqEcRvSdNpHqVwSsRdTwEdVgSdS#NgHiViLiItYiLgDrKiVhSgNrQdTiLdSmLgFmFgTmVmLgQgDrVtPdVmRaDlLiKdPwAtIkViKkVmYaDrYsYrErTrDsErFiAdIiAdEtYdNdArPnCpS#K#D#L#G#N#A# | 0.660711 | train |
M#CaSeLpGqLqFfPdPdPaPdPdRaGqQwVfTfLfYaEaHdNpNlEaLtVdTiGdSnS#Y#E#S#P#P#P#D#F#R#G#Q#W#I#N#L#P#V#LdQfLdT#K#D#P#L#K#T#P#G#R#L#D#H#G#T#R#T#A#F#I#H#H#R#E#QdV#WvKvRvC#IvNvIcWcR#D#V#GnLdFlGsVsLlNvEsIqAlNpSdE#E#E#V#F#EpWvVsKnTvAvSsGvWvAvLnAvLvCvRvWvAvS#SvL#H#G#S#L#F#P#H#L#S#L#RdSlElDvLlIlAqEqFlAaQlVwTvNgWqSqSqCfCqLwRrVeFkAdWfHdPqHpTfNqKkFiAwVtAqLgLnDnDlSwViRwViYdNhAsSpSdTpIqViPaSiLeKdHdRpLqQsRaNpVwAqSyLkAdWdKqPaLpSaApSfVwLiAwVtAqChQfSfCwIiLwIiWaTgLhDdPpTpSdLsSdTsRyPdSdScGvCrAiQdVtLqSgHdPpGqHqTgPrVwTrSeLwAdWaAqPnSvGsGqRwLiLwStAwSdPlVrDdAqAwIiRwViWaDrVpSvTvEsTdCiVdPtLpPhWdF#R#G#G#GiVwTrNyLwLdWaSqPnDvGsSqKwIiLwAtTwThPqSfAqVkFtRwViWfEgArQpMqWrTdCiEdRiWdPgThL#SpGaRtCwQqTeGwCdWaSaPlDvGnSqRkLiLwFtTfVgLpGpEaPqLwIiYkSiLkSgFaPdE#R#C#G#E#G#K#G#C#V#G#G#A#KpSdAiTdItVaAyDgLqSpEwTdTwIdQqTdPpDvGgEtEdRiLgGgGaEgAfHnSyMwVeWaDaPnSlGrEqRkLiAkVtLwMgKqGfK#P#R#V#Q#D#G#KaPiVwIiLwLiFwRtTwRdNvSpPpVhFtEhLtLhPgCsGhItIgQdGdEgPvGpAwQgPwQtLhIwTyFwHhPnSdFdNdKqGqAiLwLiSwVtGqWtSsTsGrRiIiAdHiIhPyLhYgFdVdN#A#Q#F#P#R#F#S#P#V#L#G#R#A#Q#E#P#P#A#G#G#G#G#S#I#H#D#L#P#L#F#T#E#T#S#P#T#S#A#P#W#D#P#L#P#G#P#P#P#V#L#P#H#S#P#H#S#H#L# | 0.434135 | train |
M#S#K#E#E#R#P#G#R#E#E#I#L#E#CfQdVfMqWaEaPfDdSpKvKdNcTfQlMqDnRvFlRvAvAqVcGcAvAvCvGvLdAdLdEpSaYvDvDsLlYqHvWcScVaEvSvYvSqDvFnWlAvEsFlWcKvFsSlGvIaVdFwSpRdVfYaDpEgVqVwDdTvSvKaGfIqAlDvVlPiEdWgFsKhGpStRwLhNfYlAlEvNlLlLlRdHdKpEpNqDqRdVfAaLeYwIeAdReEaGpKdEpEdItVdKtVdTgFsEnEnLlRqQlElVlAlLlFlAlAlAlMcRvKlMlGpVdKaKaGaDaReVeVeGeYqLePwNlSdEsHvAvVlEsAnMlLsAsAcArSqIrGnAhIaWySeSyTdSyPlDqFfGfVlNqGqVvLlDlRaFcSvQlIrQlPhKqLeIyFeSyVeEqAwVgVdYaNpGnKdEiHdNgHrMvEvKrLqQlQsVnVqKvGsLrPpDsLhKqKaVyVeVyIePhYdVpSdSdRpEvNpIdDdLcSvKsIhPpNrSyVdFyLpDvDvFsLsAvTsGsTdSdEpQdArPgQhLrEhFgEgQtLdPrFqSqHrPfLhFyIfMaFwSaSaG#T#T#GdAgPtKwCtMfVtHwSgAsGnGqTlLlIsQqHlLsKlEcHcLcLfHqGvNsMdTaSlSvDaIeLeLeCeYpTdTtVsGnWwMlMvWsNvWsMqVsSsLnLsAsTrGnAhAhMyVyLrYyDgGySdPcLqVpPpThPvNcVvLvWvDvLvVcDqRvIvGvIhTqVeLyVeTdGeAlKvWnLlSvVvLcEvEvKvAvMaKaPsVvEvTvHhSpLsQpMnLhHaTeIyLeShTaGlS#PqLhKfAlQvSsYsEvYcVcYcRpCrIpKhSsSrIhLdLyGkSyIfSdGdGtTdDqIqIsShCtFqMfGmHgNgFsSsLaPtVdYtKrGrEwIhQqAdRrNrLrGsMfAqVwEfAfWaNdEqEvGgKdAgVdWaGqEdSkGgEfLiVkCgTgKgPhIgPsCrQgPgTsHaFtWrNpDpEvNpGrNvKvYvRcKvAqYaFqSpKsFpPhGsIiWgAgHnGqDwYiCwRhIqNhPpKvTrGrGtIiVhMtLpG#RgSnDnG#TwLdNrPaNsGsVdRtFhGhSpSvElIlYqNvIqVvEvSpFdElEqVfEpDdSkLdCkVaPwQeYqNdKpYrRrEhEtRfViIeLmFeLtKaMtAdSpGpHhAdFdQdPpDvLsVlKvRvIsRqDvAsIsRcMnGnLtSrAnRvHrVrPgSpLyIyLyEyTaKnGdIfPkYaT#L#N#G#K#KtVdErVvAlVlKnQcIqIlAvGvKhAdVdE#Q#G#G#A#FiSpNcPsEvTsLsDvLrYsRhDpIdPpEvLsQpGpFd | 0.111884 | train |
M#RvSvSvCvVvLvLvTvAvLvVvAvLvAvAvYvYlVlYdIdPdLdPdGpSlVaSfDpPvWsKlLlMsLvLvDlAsTvFlRvGvAlQlQvVvSlNvLvIcHvYvLvGvLnSdHhHsLlLvAsLsNqFcIvIvVcSvFvGfKfKdSdAqWdSdSdApQaVwKhVwTdDwTdDdFqDqGnVwEiVkRiVkFiEqGgPhPdKdPpEvEqPqLaKaRaSeVeVeYeIeHeGdGdGlWqArLhAdSfAcKrIgRpYlYnDvEvLlCnTnAvMlAcEhElLrNsAyViIyViSyItEgYfRhLgVpPpKvVdYaFpPpErQrIlHvDsVvVlRrAnTvKqYsFcLqKdPpEvVnLsQvKvYsMrVhDdPlGlRrIyCeIySeGyDeSaAqGrGlNlLsAqAlAlLsGlQlQvFvTlQvDdApSvLgKpNgKhLhKqLeQyAeLhIeYlPyVqLaQaAqLqDdFcNqTqPlSlYsQvQvNqVcNpTpPgIpLdPhRsYlVvMsVlKvYsWvVcDrYnFlKvGhNdYpDvFcVsQvAlMsInVlNrNlHcTcSaLvDvVlEpEvAsAvAvVlRcAvRlLvNpWcTvSvLqLpPdAcSvFsTrKpNvYgKdPdVrVhQhTnThGhNdAvRvIrVcQvEsLvPvQvLsLsDpAcRsSnArPvLsIsAdDdQlAvVsLlQlLsLgPhKaTyYeIyLeTaCeEsHnDeVsLsRrDsDvGrIsMsYsAqKsRsLsEvSvAsGvVhEhVyTdLyDhHyFhEdDnGdFhHgGpClMlIsFqTcSdWpPpTrNnFtSpVvGnIvRvTvRsNvSvYvIsKvWvLsDsQvNrLd | 0.759759 | train |
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Description
Thermostability prediction is a regression task where each input protein x is mapped to a label y ∈ R, corresponding to the thermostability of x.
Splits
Structure type: AF2
The dataset is from FLIP: Benchmark tasks in fitness landscape inferencefor proteins. We employ all proteins from the“Human-cell”splits (proteins that lack AF2 structures are removed), and split them based on 70% structure similarity (see ProteinShake), with the number of training, validation and test set shown below:
- Train: 5310
- Valid: 706
- Test: 706
Label
This label indicates the normalized thermostability of the protein, ranging from 0 to 1 (through min–max normalization). The original label ranges from 40 to 67.
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Size of downloaded dataset files:
7.81 MB
Size of the auto-converted Parquet files:
6.42 MB
Number of rows:
6,722