Protein Engineering

bgl3 · Graph CNN

reference_bgl3_gcn_randomf4_20261104-163000

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
train1.19740.64170.60570.39686204.00000.21302926.00000.90001.28572.26100.00000.00000.47580.94001.27722.45670.02000.00000.53690.93001.29172.85480.04000.00000.57250.91871.22182.85480.05000.00000.5709
validation1.40380.52350.48290.27392046.00000.09863084.00000.80000.86272.20540.00000.00000.37530.80000.88662.20540.02000.00000.44320.69000.73342.50020.06000.00000.41620.77360.85152.20540.01890.00000.4325
test1.38360.52760.48390.27782018.00000.121018.00000.90000.95731.48330.00000.00000.40890.86001.02283.11600.04000.00000.48550.74000.79923.11600.06000.00000.43580.86791.04903.11600.05660.00000.4974

Loss per epoch

training validation

Config

name: reference_bgl3_gcn_randomf4
task: dms
data:
  dataset: bgl3
  encoding: one_hot,aa_index
  split_dir: null
  graph_threshold: 6
  split_scheme: random
  split_fold: 4
model:
  name: gcn
  filters:
  - 32
  hidden:
  - 100
  dropout: 0.2
train:
  epochs: 300
  batch_size: 128
  learning_rate: 0.0001
  patience: 15
  min_delta: 1.0e-05
  seed: 7