Protein Engineering

bgl3 · Graph CNN

reference_bgl3_gcn_randomf0_20261104-141827

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
train1.16940.64240.61130.40626172.00000.22084.00001.00001.67833.11600.10000.61290.98001.30793.11600.02000.54940.97001.35773.11600.04000.59540.92501.27843.11600.05000.5894
validation1.35310.55780.50190.31062050.00000.11241183.00000.80001.07292.26100.00000.00000.43080.82000.89882.26100.02000.00000.44570.74000.74072.26100.06000.00000.42050.83020.91622.26100.01890.00000.4551
test1.39210.53040.49140.27992046.00000.11851857.00000.90001.00101.78560.00000.00000.41890.74000.79512.14660.00000.00000.40990.68000.64342.65740.04000.00000.38100.73580.78492.14660.00000.00000.4081

Loss per epoch

training validation

Config

name: reference_bgl3_gcn_randomf0
task: dms
data:
  dataset: bgl3
  encoding: one_hot,aa_index
  split_dir: null
  graph_threshold: 6
  split_scheme: random
  split_fold: 0
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