  Efficient exploration of the chemical space to search the candidate drugs
that satisfy various constraints is a fundamental task of drug discovery.
Advanced deep generative methods attempt to optimize the molecules in the
compact latent space instead of the discrete original space, but the mapping
between the original and latent spaces is always kept unchanged during the
entire optimization process. The unchanged mapping makes those methods
challenging to fast adapt to various optimization scenes and leads to the great
demand for assessed molecules (samples) to provide optimization direction,
which is a considerable expense for drug discovery. To this end, we design a
sample-efficient molecular generative method, HelixMO, which explores the
scene-sensitive latent space to promote sample efficiency. The scene-sensitive
latent space focuses more on modeling the promising molecules by dynamically
adjusting the space mapping by leveraging the correlations between the general
and scene-specific characteristics during the optimization process. Extensive
experiments demonstrate that HelixMO can achieve competitive performance with
only a few assessed samples on four molecular optimization scenes. Ablation
studies verify the positive impact of the scene-specific latent space, which is
capable of identifying the critical characteristics of the promising molecules.
We also deployed HelixMO on the website PaddleHelix
(https://paddlehelix.baidu.com/app/drug/drugdesign/forecast) to provide drug
design service.
