  Predicting accurate protein-ligand binding affinity is important in drug
discovery but remains a challenge even with computationally expensive
biophysics-based energy scoring methods and state-of-the-art deep learning
approaches. Despite the recent advances in the deep convolutional and graph
neural network based approaches, the model performance depends on the input
data representation and suffers from distinct limitations. It is natural to
combine complementary features and their inference from the individual models
for better predictions. We present fusion models to benefit from different
feature representations of two neural network models to improve the binding
affinity prediction. We demonstrate effectiveness of the proposed approach by
performing experiments with the PDBBind 2016 dataset and its docking pose
complexes. The results show that the proposed approach improves the overall
prediction compared to the individual neural network models with greater
computational efficiency than related biophysics based energy scoring
functions. We also discuss the benefit of the proposed fusion inference with
several example complexes. The software is made available as open source at
https://github.com/llnl/fast.
