PaccMannRL on SARS-CoV-2: Designing antiviral candidates with conditional generative models

A drug discovery framework for antiviral small molecules against SARS-CoV-2:

The conditional compound generator, called agent (see A), can produce novel structures specifically designed to target a protein of interest. The generative process starts with the encoding of the primary structure of the target protein into a latent space of protein sequences. The representation is fed into a molecular decoder of a separately pretrained molecule VAE to produce a candidate compound.

Next, the proposed compound is evaluated by a critic (see B) composed by: a multimodal deep learning model that predicts protein–drug binding affinity using protein and compound sequences as input, and a QSAR-based score to punish toxicity. By means of the reward given by the critic, a closed-loop system is created and is trained with deep reinforcement learning to maximize a multi-objective reward.

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