Protein Engineering

bgl3 · Graph CNN

reference_bgl3_gcn_randomf1_20261104-150253

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
train1.21480.61390.57780.37276174.00000.18844667.00001.00001.40373.11600.10000.51590.98001.35053.11600.06000.55830.92001.20383.11600.04000.53910.88751.13503.11600.03120.5348
validation1.43120.54380.50140.29552044.00000.13432858.00000.80000.95241.69460.00000.00000.42040.80000.91272.50020.02000.00000.45820.77000.84322.54190.07000.00000.46210.81130.90562.50020.01890.00000.4581
test1.36640.55230.49230.30382050.00000.10421445.00000.90001.15412.26100.00000.00000.45560.78000.81172.26100.02000.00000.41430.75000.71202.26100.04000.00000.40930.75470.71982.26100.01890.00000.3837

Loss per epoch

training validation

Config

name: reference_bgl3_gcn_randomf1
task: dms
data:
  dataset: bgl3
  encoding: one_hot,aa_index
  split_dir: null
  graph_threshold: 6
  split_scheme: random
  split_fold: 1
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