  There is great interest to develop artificial intelligence-based
protein-ligand affinity models due to their immense applications in drug
discovery. In this paper, PointNet and PointTransformer, two pointwise
multi-layer perceptrons have been applied for protein-ligand affinity
prediction for the first time. Three-dimensional point clouds could be rapidly
generated from the data sets in PDBbind-2016, which contain 3 772 and 11 327
individual point clouds derived from the refined or/and general sets,
respectively. These point clouds were used to train PointNet or
PointTransformer, resulting in protein-ligand affinity prediction models with
Pearson correlation coefficients R = 0.831 or 0.859 from the larger point
clouds respectively, based on the CASF-2016 benchmark test. The analysis of the
parameters suggests that the two deep learning models were capable to learn
many interactions between proteins and their ligands, and these key atoms for
the interaction could be visualized in point clouds. The protein-ligand
interaction features learned by PointTransformer could be further adapted for
the XGBoost-based machine learning algorithm, resulting in prediction models
with an average Rp of 0.831, which is on par with the state-of-the-art machine
learning models based on PDBbind database. These results suggest that point
clouds derived from the PDBbind datasets are useful to evaluate the performance
of 3D point clouds-centered deep learning algorithms, which could learn
critical protein-ligand interactions from natural evolution or medicinal
chemistry and have wide applications in studying protein-ligand interactions.
