Machine learning-based methods have shown potential for optimizing existing molecules with more desirable properties, a critical step towards accelerating new chemical discovery. Here we propose QMO, a generic query-based molecule optimization framework that exploits latent embeddings from a molecule autoencoder. QMO improves the desired properties of an input molecule based on efficient queries, guided by a set of molecular property predictions and evaluation metrics. We show that QMO outperforms existing methods in the benchmark tasks of optimizing small organic molecules for drug-likeness and solubility under similarity constraints. We also demonstrate substantial property improvement using QMO on two new and challenging tasks that are also important in real-world discovery problems: (1) optimizing existing potential SARS-CoV-2 main protease inhibitors towards higher binding affinity and (2) improving known antimicrobial peptides towards lower toxicity. Results from QMO show high consistency with external validations, suggesting an effective means to facilitate material optimization problems with design constraints. Zeroth-order optimization is used on problems where no explicit gradient function is accessible, but single points can be queried. Hoffman et al. present here a molecular design method that uses zeroth-order optimization to deal with the discreteness of molecule sequences and to incorporate external guidance from property evaluations and design constraints.
Dec-31-2021, 18:56:52 GMT