Navigating chemical space: multi-level Bayesian optimization with hierarchical coarse-graining.

ABSTRACT: Molecular discovery within the vast chemical space remains a significant challenge due to the immense number of possible molecules and limited scalability of conventional screening methods. To approach chemical space exploration more effectively, we have developed an active learning-based method that uses transferable coarse-grained models to compress chemical space into varying levels of resolution. By using multiple representations of chemical space with different coarse-graining resolutions, we balance combinatorial complexity and chemical detail. To identify target compounds, we first transform the discrete molecular spaces into smooth latent representations. We then perform Bayesian optimization within these latent spaces, using molecular dynamics simulations to calculate target free

SUBMITTER: Walter LJ 

PROVIDER: S-EPMC12332539 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature