Accelerated discovery of novel chemical structures with desired properties is a grand challenge of our time. Here, we present a pipeline to automatize the process of ligand discovery preceding the chemical synthesis in the lab. Focusing on the discovery of potential SARS-CoV-2 antivirals, we integrate deep learning models for 1) virtual drug screening, 2) conditional de-novo molecular design, 3) multistep retrosynthesis prediction and 4) synthesis action generation. 
Using models pretrained for toxicity and binding affinity prediction as reward function for a conditional molecular generator, we construct a generative model that can propose binding ligands for unseen protein targets. From the discovery of these targeted molecules to the derivation of the synthesis procedure this approach does not require intervention by domain experts. The approach is confirmed by the successful synthesis of one potential ligand for the human ACE2 receptor.