Protein Engineering

bgl3 · Graph CNN

reference_bgl3_gcn_plm_modulof1_20261104-181141

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
train1.37740.49570.45240.22843546.00000.05772522.00000.80000.62241.59450.00000.00000.29000.62000.55452.82450.04000.00000.30990.62000.53092.82450.06000.00000.32240.62630.54192.82450.06060.00000.3261
validation1.37950.32590.32250.1050432.0000-0.0642582.00000.50000.19711.59450.00000.00000.25640.3600-0.28292.36370.02000.00000.10220.2700-0.45662.36370.07000.00000.03460.52940.19551.59450.00000.00000.2652
test1.35810.37560.31250.1325424.0000-0.1107724.00000.2000-0.03791.94590.00000.00000.13780.2800-0.10131.94590.02000.00000.13610.3100-0.05002.41130.08000.00000.17650.2667-0.03921.94590.00000.00000.1418

Loss per epoch

training validation

Config

name: reference_bgl3_gcn_plm_modulof1
task: dms
data:
  dataset: bgl3
  encoding: one_hot,aa_index,plm,plm_sum
  split_dir: null
  graph_threshold: 6
  split_scheme: modulo
  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