. Author manuscript; available in PMC: 2023 Apr 25.

Published in final edited form as: J Chem Inf Model. 2022 Apr 14;62(8):1840–1848. doi: 10.1021/acs.jcim.2c00260

Table 1.

Performance of A3D-PNAConv-FT on the test set of FreeSolv with multiple random data splits in comparison with previously published models/frameworks.

Method/Framework	RMSE, kcal/mol	MAE, kcal/mol	References	Key notes
A3D-PNAConv-FT	0.719±0.168	0.417±0.066	This work
3DGCN	0.824±0.140	0.575±0.053	27	Feature matrix + inter-atomic position matrix^a
AGBT	0.994±0.217	0.594±0.090	32	SMILES + structures^b
D-MPNN	1.075±0.054	-	26	2D features + molecular features, ChemProp
GraphConv	1.150±0.262	-	32, 56	Universal graph convolutional networks
AttentiveFP	1.091±0.191	-	25, 56	Graph attention + GRU
Weave	1.220±0.280	-	57, 23	GCN + Atom-pair features
FML	-	0.570	21	MD sampling + Kernel Ridge Regression

The relative position matrix is designed to have the inter-atomic positions, rather than individual positions, that ensure translational invariance.

For a given molecular structure and its SMILES strings, AG-FPs are generated from element-specific algebraic subgraphs module and BT-FPs are generated from a deep bidirectional transformer module, and then the random forest algorithm is used to fuse, rank, and select optimal fingerprints (AGBT-FPs) for machine learning.