Protein Engineering

avgfp · Graph CNN

reference_avgfp_gcn_randomf3_20261104-154141

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
train0.08740.96110.87640.92243066.00000.2533124.00000.80003.82874.10680.10000.81500.78003.81344.10680.08000.84310.77003.79644.10790.10000.85330.67283.76874.11450.19750.8794
validation0.09960.95510.87180.91101013.00000.2960113.00000.70003.77733.96420.00000.00000.82310.78003.81043.98080.22000.00000.88450.75003.78583.98080.24000.00000.88930.74073.78444.02920.24070.00000.8911
test0.10550.95230.86780.9059965.00000.3068834.00000.60003.75273.86880.00000.00000.80910.66003.77814.05640.12000.00000.86880.69003.76664.05640.18000.00000.88170.68523.76664.05640.18520.00000.8845

Loss per epoch

training validation

Config

name: reference_avgfp_gcn_randomf3
task: dms
data:
  dataset: avgfp
  encoding: one_hot,aa_index
  split_dir: null
  graph_threshold: 7
  split_scheme: random
  split_fold: 3
model:
  name: gcn
  filters:
  - 128
  - 128
  hidden:
  - 100
  dropout: 0.2
train:
  epochs: 300
  batch_size: 32
  learning_rate: 0.0001
  patience: 15
  min_delta: 1.0e-05
  seed: 7