Protein Engineering

bgl3 · Graph CNN

reference_bgl3_gcn_randomf2_20261104-155213

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
train1.19340.62480.58580.38406114.00000.20893362.00001.00001.50853.11600.10000.52750.98001.37533.11600.04000.55780.95001.23323.11600.04000.54450.93131.21733.11600.05000.5629
validation1.38080.54340.50370.29522110.00000.1129476.00000.60000.51561.94590.00000.00000.27140.72000.75492.20540.02000.00000.39410.78000.85672.62040.05000.00000.45630.73580.78182.20540.01890.00000.4061
test1.44350.53900.49350.28942044.00000.12782455.00000.80000.86011.69460.00000.00000.39060.76000.74491.94590.00000.00000.39730.76000.81152.50020.04000.00000.44980.77360.77031.94590.00000.00000.4087

Loss per epoch

training validation

Config

name: reference_bgl3_gcn_randomf2
task: dms
data:
  dataset: bgl3
  encoding: one_hot,aa_index
  split_dir: null
  graph_threshold: 6
  split_scheme: random
  split_fold: 2
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