Protein Engineering

bgl3 · Linear

reference_bgl3_lr_s2_20261104-020842

Metrics

Setmsepearsonrspearmanrr2above_wt_nabove_wt_spearmanrbudget_to_maxhit_rate_at_10mean_at_10max_at_10precision_at_10tie_share_at_10captured_at_10hit_rate_at_50mean_at_50max_at_50precision_at_50tie_share_at_50captured_at_50hit_rate_at_100mean_at_100max_at_100precision_at_100tie_share_at_100captured_at_100hit_rate_at_1pctmean_at_1pctmax_at_1pctprecision_at_1pcttie_share_at_1pctcaptured_at_1pct
test1.32020.55330.49590.30531036.00000.1618369.00000.90001.05461.69460.00000.00000.47920.80000.87361.86900.00000.00000.47820.71000.68932.45670.07000.00000.43670.77780.83831.69460.00000.00000.4373
train1.15980.64340.61720.41028299.00000.23410.10000.51470.04000.55220.05000.59030.06020.5807
validation1.38430.54810.50280.2997933.00000.05531477.00000.90001.04711.69460.00000.00000.47410.88000.94312.08410.02000.00000.50670.78000.73722.85480.07000.00000.45790.87500.96262.08410.00000.00000.4779

Loss per epoch

training validation

Config

name: reference_bgl3_lr_s2
task: dms
data:
  dataset: bgl3
  encoding: one_hot,aa_index
  split_dir: bgl3/splits/main
model:
  name: lr
train:
  epochs: 300
  batch_size: 128
  learning_rate: 0.0001
  patience: 15
  min_delta: 1.0e-05
  seed: 2