Protein Engineering

ube4b · Linear

reference_ube4b_lr_s3_20261104-022752

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
test2.44770.58670.58650.34422077.00000.418433.00000.90003.68146.56280.40000.00000.68340.96003.79217.55410.40000.00000.81840.95003.35587.55410.50000.00000.81310.94903.37957.55410.50000.00000.8150
train2.42150.60260.59550.362916805.00000.45520.10000.76240.18000.74900.29000.77440.50380.8254
validation2.46900.59310.58400.35161869.00000.43982.00001.00005.55887.74370.50000.00000.89350.96003.83917.74370.50000.00000.81460.97003.61527.74370.54000.00000.85830.97733.70017.74370.54550.00000.8563

Loss per epoch

training validation

Config

name: reference_ube4b_lr_s3
task: dms
data:
  dataset: ube4b
  encoding: one_hot,aa_index
  split_dir: ube4b/splits/main
model:
  name: lr
train:
  epochs: 300
  batch_size: 64
  learning_rate: 0.0001
  patience: 15
  min_delta: 1.0e-05
  seed: 3