Protein Engineering

gb1 · Sequence CNN

reference_gb1_cnn_plm_contiguousf4_20261105-043315

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
train0.23620.97900.96600.958530951.00000.8720108.00001.00001.65751.85270.00000.00000.89721.00001.66532.09420.12000.00000.94091.00001.67212.11100.29000.00000.96121.00001.38432.52950.78280.00000.9887
validation2.68440.35690.36730.12551866.00000.12717234.00000.7000-0.18690.75020.00000.00000.59670.6400-0.12610.78760.12000.00000.66880.6200-0.15050.78760.15000.00000.67920.5373-0.27200.93520.13430.00000.6542
test5.05400.32710.2255-0.13965856.00000.136411497.00000.70000.00080.56170.00000.00000.53800.4400-0.34170.76080.00000.00000.43850.3500-0.49480.89990.00000.00000.39280.3632-0.49410.89990.00000.00000.4061

Loss per epoch

training validation

Config

name: reference_gb1_cnn_plm_contiguousf4
task: dms
data:
  dataset: gb1
  encoding: one_hot,aa_index,plm,plm_sum
  split_dir: null
  split_scheme: contiguous
  split_fold: 4
model:
  name: cnn
  conv:
  - - 128
    - 17
  - - 128
    - 17
  - - 128
    - 17
  hidden:
  - 100
  dropout: 0.2
train:
  epochs: 300
  batch_size: 32
  learning_rate: 0.0001
  patience: 15
  min_delta: 1.0e-05
  seed: 7