Table 7 Model performance on LIT-PCBA
From: TB-IECS: an accurate machine learning-based scoring function for virtual screening
Targets | TB-IEC score | Glide_SP | ||||
|---|---|---|---|---|---|---|
ROAUC | F1_Score | EF_1% | ROAUC | EF_1% | ||
ALDH1 | 0.610 | 0.031 | 8.960 | 0.482 | 0 | |
ESR1_ant | 0.697 | 0 | 2.80 | 0.324 | 0 | |
FEN1 | 0.812 | 0 | 1.143 | 0.386 | 0 | |
GBA | 0.689 | 0 | 0 | 0.521 | 0 | |
KAT2A | 0.523 | 0 | 1.299 | 0.517 | 0 | |
MAPK1 | 0.697 | 0 | 0 | 0.429 | 0 | |
MTORC1 | 0.511 | 0 | 0 | 0.555 | 0 | |
PKM2 | 0.632 | 0 | 1.198 | 0.439 | 0 | |
TP53 | 0.654 | 0 | 7.279 | 0.417 | 0 | |
VDR | 0.609 | 0 | 0.543 | 0.530 | 0 | |