Protein Engineering

ube4b · Linear

reference_ube4b_lr_contiguousf0_20261104-145809

Metrics

Setmsepearsonrspearmanrr2above_wt_nabove_wt_spearmanrbudget_to_maxhit_rate_at_10mean_at_10max_at_10precision_at_10captured_at_10hit_rate_at_50mean_at_50max_at_50precision_at_50captured_at_50hit_rate_at_100mean_at_100max_at_100precision_at_100captured_at_100hit_rate_at_1pctmean_at_1pctmax_at_1pctprecision_at_1pctcaptured_at_1pct
train3.42560.47450.42430.17954241.00000.465744.00000.90003.31115.85920.00000.56090.90002.92449.18450.16000.58840.83002.30239.18450.17000.54520.86382.43489.18450.24410.6202
validation3.7674-0.0386-0.0376-0.0616458.0000-0.09011817.00000.1000-2.36250.19360.00000.0000-0.12590.1200-2.35523.89960.02000.0000-0.15540.1100-2.15153.89960.01000.0000-0.12530.1923-2.19303.89960.03850.0000-0.1076
test3.2587-0.0766-0.0908-0.43431218.0000-0.00463323.00000.3000-0.37661.41600.00000.00000.08490.1800-1.19932.63540.02000.0000-0.09160.1500-1.31842.77860.02000.0000-0.13830.1860-1.24151.41600.00000.0000-0.0989

Loss per epoch

training validation

Config

name: reference_ube4b_lr_contiguousf0
task: dms
data:
  dataset: ube4b
  encoding: one_hot,aa_index
  split_dir: null
  split_scheme: contiguous
  split_fold: 0
model:
  name: lr
train:
  epochs: 300
  batch_size: 64
  learning_rate: 0.0001
  patience: 15
  min_delta: 1.0e-05
  seed: 7