Protein Engineering

bgl3 · Fully connected

reference_bgl3_fc_s5_20261104-140535

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
test1.35210.53950.47310.28851036.00000.1386502.00000.70000.78561.69460.00000.00000.38540.72000.69002.31360.02000.00000.40460.66000.58962.62040.07000.00000.39360.70370.69481.86900.00000.00000.3831
train1.23940.61350.57810.36978299.00000.18857209.00001.00001.48683.11600.10000.51950.88001.21103.11600.02000.49620.87001.14193.11600.02000.50230.86571.10163.11600.04170.5221
validation1.41490.53550.48210.2842933.00000.01031539.00000.90001.03121.69460.00000.00000.46850.74000.71392.01740.02000.00000.41450.72000.60352.85480.08000.00000.39980.87501.02502.01740.00000.00000.5012

Loss per epoch

training validation

Config

name: reference_bgl3_fc_s5
task: dms
data:
  dataset: bgl3
  encoding: one_hot,aa_index
  split_dir: bgl3/splits/main
model:
  name: fcn
  hidden:
  - 100
  - 100
  dropout: 0.2
train:
  epochs: 300
  batch_size: 32
  learning_rate: 0.0001
  patience: 15
  min_delta: 1.0e-05
  seed: 5