Protein Engineering

bgl3 · Graph CNN

reference_bgl3_gcn_randomf3_20261104-154349

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
train1.17790.64160.60580.40386140.00000.22172279.00000.90001.26962.26100.00000.46180.94001.32042.45670.02000.54520.93001.25532.45670.01000.55690.94371.31132.85480.08130.6021
validation1.37670.53110.49330.28152018.00000.133054.00001.00001.17021.48330.00000.00000.47670.74000.77192.96760.06000.00000.39520.73000.77993.11600.07000.00000.42830.69810.68062.96760.07550.00000.3643
test1.36990.54950.51350.30072110.00000.1376412.00000.80000.89971.78560.00000.00000.40070.80000.79922.14660.00000.00000.41080.81000.83522.14660.04000.00000.44770.81130.81212.14660.00000.00000.4175

Loss per epoch

training validation

Config

name: reference_bgl3_gcn_randomf3
task: dms
data:
  dataset: bgl3
  encoding: one_hot,aa_index
  split_dir: null
  graph_threshold: 6
  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