Protein Engineering

bgl3 · Graph CNN

reference_bgl3_gcn_modulof0_20261104-144109

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
train1.22330.57190.53840.31963841.00000.1502727.00001.00001.17711.69460.00000.46770.86001.05832.96760.04000.48500.84001.00852.96760.06000.49420.84551.00342.96760.05450.4966
validation1.53960.13240.09500.0165424.00000.05192.00000.2000-0.32372.79320.10000.00000.03930.4200-0.14362.79320.08000.00000.11900.3700-0.18842.79320.11000.00000.11350.3333-0.06942.79320.06670.00000.1310
test1.50380.10930.10320.0017338.0000-0.0461423.00000.1000-0.89671.59450.00000.0000-0.12080.2800-0.48861.78560.04000.00000.03280.3100-0.48081.94590.10000.00000.03990.0769-1.01671.59450.00000.0000-0.1678

Loss per epoch

training validation

Config

name: reference_bgl3_gcn_modulof0
task: dms
data:
  dataset: bgl3
  encoding: one_hot,aa_index
  split_dir: null
  graph_threshold: 6
  split_scheme: modulo
  split_fold: 0
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