Protein Engineering

ube4b · Fully connected

reference_ube4b_fc_plm_modulof4_20261105-043106

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
train2.27550.67710.63700.41316783.00000.479221.00001.00004.89216.18800.00000.00000.70461.00004.90039.15370.26000.00000.82841.00004.47649.15370.40000.00000.83480.98683.97819.15370.53970.00000.8724
validation2.89810.33760.34620.1119645.0000-0.0235134.00000.2000-0.61930.87170.00000.00000.10680.3000-0.44752.65830.02000.00000.17160.3100-0.43783.22920.03000.00000.19870.2143-0.69190.87170.00000.00000.1079
test3.06940.36740.38860.1132318.0000-0.1089353.00000.4000-0.71131.87170.00000.00000.15820.3600-0.68271.87170.02000.00000.22290.3200-0.62855.64210.03000.00000.27780.5000-0.58721.87170.00000.00000.2015

Loss per epoch

training validation

Config

name: reference_ube4b_fc_plm_modulof4
task: dms
data:
  dataset: ube4b
  encoding: one_hot,aa_index,plm,plm_sum
  split_dir: null
  split_scheme: modulo
  split_fold: 4
model:
  name: fcn
  hidden:
  - 100
  - 100
  - 100
  dropout: 0.2
train:
  epochs: 300
  batch_size: 64
  learning_rate: 0.0001
  patience: 15
  min_delta: 1.0e-05
  seed: 7