Protein Engineering

pab1 · Fully connected

reference_pab1_fc_randomf1_20261104-152551

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
train0.39430.94600.93050.89452683.00000.13204070.00000.60000.13500.51660.00000.69570.76000.15721.16150.06000.75990.69000.12471.16150.08000.77670.65310.09461.16150.13470.8084
validation0.50950.93050.91850.8658935.00000.12561178.00000.60000.03190.38360.00000.00000.66740.62000.06770.83230.04000.00000.75240.64000.07320.83230.12000.00000.79450.63410.06980.83230.07320.00000.7821
test0.46810.93540.91800.8749898.00000.13225171.00000.70000.13910.86010.10000.00000.70470.60000.05420.86010.04000.00000.75350.64000.06900.86010.07000.00000.79650.60980.05980.86010.07320.00000.7815

Loss per epoch

training validation

Config

name: reference_pab1_fc_randomf1
task: dms
data:
  dataset: pab1
  encoding: one_hot,aa_index
  split_dir: null
  split_scheme: random
  split_fold: 1
model:
  name: fcn
  hidden:
  - 100
  - 100
  - 100
  dropout: 0.2
train:
  epochs: 300
  batch_size: 128
  learning_rate: 0.001
  patience: 15
  min_delta: 1.0e-05
  seed: 7