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Table 4 Complexity comparisons of SMPLIP-Score

From: SMPLIP-Score: predicting ligand binding affinity from simple and interpretable on-the-fly interaction fingerprint pattern descriptors

Models Features No of features Learning parameters Run time (s)a Complexitya Memory usage (max)b
SMPLIP-RF IFP 140 max_features (dʹ) = ‘auto’ or ‘sqrt’
n-estimators (t) = 100 to 500
2.82 \(O(t*O(d{^{\prime}}*n{^{\prime}}*{\log}(n{^{\prime}}))\) 55.24
IFP + Int-Dist 280 6.55 56.621
IFP + Frag 2422 36.86 57.18
IFP + Int-Dist + Frag 2562 40.37 57.17
SMPLIP-DNN IFP 140 159,601 137.96 \(O(d*\mathrm{n}*layers*nodes)\) 130.49
IFP + Int-Dist 280 215,601 161.83 131.73
IFP + Frag 2422 1,072,401 295.79 130.93
IFP + Int-Dist + Frag 2562 1,128,401 495.57 131.18
SMPLIP-Linear IFP + Frag 2422 Loss = huber, penalty = elasticnet, max_iter (\({t}_{i})\)=100 1.54 \(O({t}_{i}*d*n)\) 57.66
PLEC-Linearc PLEC FP 65,536 Loss = huber, penalty = elasticnet, max_iter (\({t}_{i})\)=100 \(O({t}_{i}*d*n)\)
  1. a,b The run time and memory usages were computed on system (Intel Xeon CPU E5-2650) using PDBbindv.2015-refined set
  2. aThe comparison of time complexity according to chosen features or learning condition
  3. bThe comparison of space complexity according to chosen features or learning condition
  4. cThe data was gained from the original article of PLEC [32]