Protein Engineering

avgfp · Graph CNN

reference_avgfp_gcn_modulof1_20261104-153241

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
train0.58350.62060.63730.35981273.00000.0513139.00000.30003.48003.89190.00000.46020.34003.56724.05920.04000.58020.31003.57924.05920.05000.61680.30343.57284.05920.05620.6060
validation0.44810.38650.16600.0361135.0000-0.0077249.00000.00003.03973.51160.00000.0000-0.55720.10003.40803.79030.02000.00000.11900.17003.40593.92220.12000.00000.12460.00002.93453.51160.00000.0000-0.7354
test0.48640.31700.18840.0372216.0000-0.1729458.00000.00003.43033.66080.00000.00000.16640.04003.26683.73230.00000.0000-0.09440.19003.40243.99230.09000.00000.16470.00003.23683.66080.00000.0000-0.1240

Loss per epoch

training validation

Config

name: reference_avgfp_gcn_modulof1
task: dms
data:
  dataset: avgfp
  encoding: one_hot,aa_index
  split_dir: null
  graph_threshold: 7
  split_scheme: modulo
  split_fold: 1
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