Despite substantial progress in machine learning for scientific discovery in
recent years, truly de novo design of small molecules which exhibit a property
of interest remains a significant challenge. We introduce LambdaZero, a
generative active learning approach to search for synthesizable molecules.
Powered by deep reinforcement learning, LambdaZero learns to search over the
vast space of molecules to discover candidates with a desired property. We
apply LambdaZero with molecular docking to design novel small molecules that
inhibit the enzyme soluble Epoxide Hydrolase 2 (sEH), while enforcing
constraints on synthesizability and drug-likeliness. LambdaZero provides an
exponential speedup in terms of the number of calls to the expensive molecular
docking oracle, and LambdaZero de novo designed molecules reach docking scores
that would otherwise require the virtual screening of a hundred billion
molecules. Importantly, LambdaZero discovers novel scaffolds of synthesizable,
drug-like inhibitors for sEH. In in vitro experimental validation, a series of
ligands from a generated quinazoline-based scaffold were synthesized, and the
lead inhibitor N-(4,6-di(pyrrolidin-1-yl)quinazolin-2-yl)-N-methylbenzamide
(UM0152893) displayed sub-micromolar enzyme inhibition of sEH.

LambdaZero 利用深度强化学习探索大量分子空间，通过分子对接设计出抑制可溶性环氧解酯化酶 2 的新型小分子。在实验验证中，从生成的脒类新型小分子中选择的 UM0152893 表现出亚微摩尔级的酶抑制活性。