Protein Engineering

gb1 · Linear

reference_gb1_lr_contiguousf1_20261104-154244

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
train0.79370.91820.94800.841133066.00000.57173264.00001.00001.53871.82200.20000.95361.00001.44211.82200.26000.96141.00001.40401.82200.39000.96630.99580.96161.82200.52670.9467
validation9.7290-0.3273-0.3333-0.80362092.0000-0.321115165.00000.0000-5.6188-4.77320.00000.0000-0.34640.0000-5.4969-3.66190.00000.0000-0.33340.0000-5.3535-2.33890.00000.0000-0.31270.0000-5.1657-0.18550.00000.0000-0.2838
test2.9687-0.05460.0122-0.47115403.00000.07826305.00000.5000-0.56630.59630.00000.00000.18930.3800-1.32060.78830.00000.0000-0.14050.4200-1.00530.80680.00000.00000.00390.3881-1.12511.06210.01000.0000-0.0557

Loss per epoch

training validation

Config

name: reference_gb1_lr_contiguousf1
task: dms
data:
  dataset: gb1
  encoding: one_hot,aa_index
  split_dir: null
  split_scheme: contiguous
  split_fold: 1
model:
  name: lr
train:
  epochs: 300
  batch_size: 128
  learning_rate: 0.0001
  patience: 15
  min_delta: 1.0e-05
  seed: 7