  Robust and efficient interpretation of QSAR methods is quite useful to
validate AI prediction rationales with subjective opinion (chemist or biologist
expertise), understand sophisticated chemical or biological process mechanisms,
and provide heuristic ideas for structure optimization in pharmaceutical
industry. For this purpose, we construct a multi-layer self-attention based
Graph Neural Network framework, namely Ligandformer, for predicting compound
property with interpretation. Ligandformer integrates attention maps on
compound structure from different network blocks. The integrated attention map
reflects the machine's local interest on compound structure, and indicates the
relationship between predicted compound property and its structure. This work
mainly contributes to three aspects: 1. Ligandformer directly opens the
black-box of deep learning methods, providing local prediction rationales on
chemical structures. 2. Ligandformer gives robust prediction in different
experimental rounds, overcoming the ubiquitous prediction instability of deep
learning methods. 3. Ligandformer can be generalized to predict different
chemical or biological properties with high performance. Furthermore,
Ligandformer can simultaneously output specific property score and visible
attention map on structure, which can support researchers to investigate
chemical or biological property and optimize structure efficiently. Our
framework outperforms over counterparts in terms of accuracy, robustness and
generalization, and can be applied in complex system study.
