Protein Engineering

pab1 · Graph CNN

reference_pab1_gcn_s4_20261104-135443

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
test0.53710.92710.92020.8588477.00000.0605306.00000.60000.04150.46070.00000.00000.70340.54000.08621.16150.14000.00000.80650.56000.04731.16150.20000.00000.83000.60980.09800.84200.14630.00000.7992
train0.47850.93440.93090.87273650.00000.11566.00000.70000.36241.52040.10000.74160.66000.15901.52040.08000.73630.67000.13581.52040.09000.76020.66770.10691.52040.10270.8089
validation0.50770.92770.92170.8599389.00000.0088953.00000.4000-0.25330.37280.00000.00000.59920.4800-0.02680.84450.08000.00000.76150.5100-0.05560.84450.15000.00000.78790.54050.00050.84450.08110.00000.7548

Loss per epoch

training validation

Config

name: reference_pab1_gcn_s4
task: dms
data:
  dataset: pab1
  encoding: one_hot,aa_index
  split_dir: pab1/splits/main
  graph_threshold: 7
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: 4