Optimizing Molecules using Efficient Queries from Property Evaluations
- URL: http://arxiv.org/abs/2011.01921v2
- Date: Mon, 18 Oct 2021 21:07:56 GMT
- Title: Optimizing Molecules using Efficient Queries from Property Evaluations
- Authors: Samuel Hoffman, Vijil Chenthamarakshan, Kahini Wadhawan, Pin-Yu Chen,
Payel Das
- Abstract summary: We propose QMO, a generic query-based molecule optimization framework.
QMO improves the desired properties of an input molecule based on efficient queries.
We show that QMO outperforms existing methods in the benchmark tasks of optimizing small organic molecules.
- Score: 66.66290256377376
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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 significant
property improvement using QMO on two new and challenging tasks that are also
important in real-world discovery problems: (i) optimizing existing potential
SARS-CoV-2 Main Protease inhibitors toward higher binding affinity; and (ii)
improving known antimicrobial peptides towards lower toxicity. Results from QMO
show high consistency with external validations, suggesting effective means to
facilitate material optimization problems with design constraints.
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