DeepBioisostere: Deep Learning-based Bioisosteric Replacements for Optimization of Multiple Molecular Properties

Optimizing molecules to improve their properties is a fundamental challenge in drug design. For a fine-tuning of molecular properties without losing bio-activity validated in advance, the concept of bioisosterism has emerged. Many in silico methods have been proposed for discovering bioisosteres, but they require expert knowledge for their applications or are restricted to known databases. Here, we introduce DeepBioisostere, a deep generative model to design suitable bioisosteric replacements. Our model allows a fully automated bioisosteric replacement by intelligently selecting fragments for both removal and insertion. Through various scenarios of multiple property control, we showcase the model's capability to delicately tune specific properties, addressing the challenge in molecular optimization. Our model's innovation lies in its capacity to design a bioisosteric replacement reflecting the compatibility with the surroundings of the modification site, facilitating the control of sophisticated properties that depend on the overall molecular structures such as drug-likeness. DeepBioisostere can also provide previously unseen bioisosteric replacements, highlighting its capability for exploring diverse chemical modifications rather than just mining them from known databases. Lastly, we used DeepBioisostere to improve the sensitivity of a known SARS-CoV-2 main protease inhibitor to the E166V mutant that exhibits drug resistance to the inhibitor, demonstrating its potential application in drug discovery.