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.

QMO 是一种基于机器学习的分子优化框架，可以利用一个分子自编码器中的潜在嵌入，并通过基于一组分子属性预测和评估度量的有效查询来改善输入分子的期望属性。在类似性约束下，QMO 在优化小有机分子的药物样性和溶解度的基准任务方面优于现有方法，并且在两个新的和具有挑战性的任务中也展示了显著的性能提升，即：(i) 将现有的潜在 SARS-CoV-2 主蛋白酶抑制剂优化为更高的结合亲和力；(ii) 改善已知的抗菌肽的毒性。QMO 的结果与外部验证具有高一致性，为设计约束下的材料优化问题提供了有效的手段。