Multi-Property Molecular Optimization using an Integrated Poly-Cycle Architecture

Molecular lead optimization is an important task of drug discovery focusing on generating molecules similar to a drug candidate but with enhanced properties. Most prior works focused on optimizing a single property. However, in real settings, we wish to find molecules that satisfy multiple constraints, e.g., potency and safety. Simultaneously optimizing these constraints was shown to be difficult, mostly due to the lack of training examples satisfying all constraints. In this work, we present a novel approach for multi-property optimization. Unlike prior approaches, that require a large training set of pairs of a lead molecule and an enhanced molecule, our approach is unpaired. Our architecture learns a transformation for each property optimization separately, while constraining the latent embedding space between all transformations. This allows generating a molecule which optimizes multiple properties simultaneously. We present a novel adaptive loss which balances the separate transformations and stabilizes the optimization process. We evaluate our method on optimizing for two properties: dopamine receptor (DRD2) and drug likeness (QED), and show our method outperforms previous state-of-the-art, especially when training examples satisfying all constraints are sparse.