Protein Engineering

ube4b · Linear

reference_ube4b_lr_s1_20261104-020333

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
test2.44510.58780.58870.34492077.00000.418634.00000.90003.68146.56280.40000.00000.68340.96003.83917.55410.40000.00000.82580.94003.35917.55410.51000.00000.81360.93883.36187.55410.50000.00000.8120
train2.41440.60410.59830.364816805.00000.45460.10000.76240.18000.74260.29000.77530.51510.8310
validation2.46550.59390.58550.35251869.00000.43812.00001.00005.55887.74370.50000.00000.89350.96003.85517.74370.50000.00000.81710.97003.64497.74370.55000.00000.86340.97733.72887.74370.55680.00000.8611

Loss per epoch

training validation

Config

name: reference_ube4b_lr_s1
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: 1