Fig. 3: Comparison of training/inference time and parameters for models predicting 100k small molecules.
From: MoleculeFormer is a GCN-transformer architecture for molecular property prediction
A comparison chart of the training and inference time consumption and the number of parameters of different models under the training of 100k small molecules, excluding the time consumption for generating 3D features of small molecules and force field optimization. To address the issue of excessively long training time of the model with large-scale data, we also developed the MoleculeFormer-Mini version. Compared with MoleculeFormer, this version sacrifices a small amount of accuracy, and it takes less time than FP-GNN.
