  De novo molecular design has facilitated the exploration of large chemical
space to accelerate drug discovery. Structure-based de novo method can overcome
the data scarcity of active ligands by incorporating drug-target interaction
into deep generative architectures. However, these strategies are bottlenecked
by the small fraction of experimentally determined protein or complex
structures. In addition, the cost of molecular generation is computationally
expensive due to 3D representations of both molecule and protein. Here, we
demonstrate a widely used and fast protein sequence-based reinforcement
learning (RL) model for drug discovery. In the generative model, one of the
reward components, a binding affinity predictor, is based on 1D protein
sequence and molecular SMILES. As a proof of concept, the RL model was utilized
to design molecules for four targets. The generated compounds showed
bioactivities by the validation of both QSAR and molecular docking with
experimental 3D binding pockets. We also found that the performance of
generated molecules depends on the selection of data source training for the
binding predictor. Furthermore, drug design for a kinase without any
experimental structure, CDK20, was studied by our model. With only 1D protein
sequence as input, the generated novel compounds showed favorable binding
affinity based on the AlphaFold predicted structure.
