Protein Engineering

bgl3 · Graph CNN

reference_bgl3_gcn_plm_modulof2_20261104-183546

Metrics

Setmsepearsonrspearmanrr2above_wt_nabove_wt_spearmanrbudget_to_maxhit_rate_at_10mean_at_10max_at_10precision_at_10tie_share_at_10captured_at_10hit_rate_at_50mean_at_50max_at_50precision_at_50tie_share_at_50captured_at_50hit_rate_at_100mean_at_100max_at_100precision_at_100tie_share_at_100captured_at_100hit_rate_at_1pctmean_at_1pctmax_at_1pctprecision_at_1pcttie_share_at_1pctcaptured_at_1pct
train1.43960.48760.44360.21253477.00000.03203769.00000.50000.15391.48330.00000.00000.15320.62000.43692.36370.02000.00000.27220.63000.46792.36370.04000.00000.29970.66320.53292.36370.04210.00000.3226
validation1.26480.28950.23920.0827437.0000-0.1273501.00000.3000-0.33041.04800.00000.00000.02680.3400-0.14612.20540.06000.00000.10890.2900-0.24772.20540.07000.00000.07640.3125-0.24222.20540.06250.00000.0582
test1.38100.32550.31650.1041432.0000-0.0523696.00000.3000-0.79781.35810.00000.0000-0.09270.4000-0.24562.36370.04000.00000.11740.3300-0.32492.36370.09000.00000.09350.3529-0.60201.59450.00000.0000-0.0248

Loss per epoch

training validation

Config

name: reference_bgl3_gcn_plm_modulof2
task: dms
data:
  dataset: bgl3
  encoding: one_hot,aa_index,plm,plm_sum
  split_dir: null
  graph_threshold: 6
  split_scheme: modulo
  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