Protein Engineering

ube4b · Fully connected

reference_ube4b_fc_plm_contiguousf2_20261104-184756

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
train2.02020.57010.56880.31649957.00000.16195289.00001.00002.85034.08670.00000.00000.55130.92002.05744.54160.02000.00000.52430.91001.79734.54160.05000.00000.52570.88791.35835.51430.13790.00000.5539
validation3.35310.32730.34910.1067323.0000-0.0988551.00000.4000-0.13970.78480.00000.00000.26430.3200-0.59401.32410.00000.00000.25020.3100-0.49283.87920.05000.00000.31110.3600-0.41741.15370.00000.00000.2529
test5.17150.37950.40670.108388.00000.025354.00000.60000.17881.64000.00000.00000.28170.3600-0.03734.53270.16000.00000.33590.3400-0.09045.70470.39000.00000.44910.7500-0.06580.17070.00000.00000.2166

Loss per epoch

training validation

Config

name: reference_ube4b_fc_plm_contiguousf2
task: dms
data:
  dataset: ube4b
  encoding: one_hot,aa_index,plm,plm_sum
  split_dir: null
  split_scheme: contiguous
  split_fold: 2
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