Protein Engineering

pab1 · Graph CNN

reference_pab1_gcn_randomf3_20261104-155544

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
train0.42520.94200.93690.88722684.00000.1675415.00000.90000.46130.99450.10000.78490.74000.20700.99450.10000.76310.73000.18110.99450.09000.78790.73060.16191.20690.12650.8287
validation0.51580.92840.92210.8618915.00000.0771834.00000.60000.05960.48520.00000.00000.69430.66000.08880.70360.06000.00000.77190.62000.05100.70400.13000.00000.79320.64630.06440.70360.08540.00000.7881
test0.51470.92810.92560.8612917.00000.095072.00000.60000.06300.49350.00000.00000.69400.70000.09930.68540.10000.00000.78180.65000.08661.16150.17000.00000.81360.68290.10841.16150.17070.00000.8112

Loss per epoch

training validation

Config

name: reference_pab1_gcn_randomf3
task: dms
data:
  dataset: pab1
  encoding: one_hot,aa_index
  split_dir: null
  graph_threshold: 7
  split_scheme: random
  split_fold: 3
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