Fig. 4: Anti-noise performance comparison across different noise rates.
From: MoleculeFormer is a GCN-transformer architecture for molecular property prediction
The anti-noise performances of Attentive FP, XGBoost, FP-GNN, and MoleculeFormer with different noise rates on the HIV dataset were evaluated. The FP-GNN model was sourced from Cai et al.27. The Attentive FP model was sourced from Xiong et al.52. XGBoost employed the same molecular fingerprint selection as MoleculeFormer. Each experiment included 10 independent samples, and all experiments were conducted without interference between them. Error bars indicate mean ± standard error.
