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Fig. 6 | Journal of Cheminformatics

Fig. 6

From: MolFilterGAN: a progressively augmented generative adversarial network for triaging AI-designed molecules

Fig. 6

Evaluation of MolFilterGAN, QED and SA on HTS dataset LIT-PCBA. AUC scores for a random guess (RAND), QED, SA and MolFilterGAN (MFG) on 8 target sets, including, a VDR, b ESR-ANTAGO, c FEN1, d GBA, e KAT2A, f PKM2, g MAPK1, and h ALDH1. The random method, QED, SA and MolFilterGAN are represented as solid black, green, blue and salmon lines respectively. i AUC score distribution for the random guess, QED, SA and MolFilterGAN on all target sets. For the random guess, a value of 0 or 1 from a uniform distribution was assigned for each molecule. *p < 0.05, **p < 0.01, ***p < 0.001, and ns not significant. A statistical analysis was performed by one-tailed Student’s t-test

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