FSscore: A Machine Learning-based Synthetic Feasibility Score Leveraging
  Human Expertise
Rebecca M. Neeser Bruno Correia Philippe Schwaller
Determining whether a molecule can be synthesized is crucial for many aspects
of chemistry and drug discovery, allowing prioritization of experimental work
and ranking molecules in de novo design tasks. Existing scoring approaches to
assess synthetic feasibility struggle to extrapolate to out-of-distribution
chemical spaces or fail to discriminate based on minor differences such as
chirality that might be obvious to trained chemists. This work aims to address
these limitations by introducing the Focused Synthesizability score (FSscore),
which learns to rank structures based on binary preferences using a graph
attention network. First, a baseline trained on an extensive set of
reactant-product pairs is established that subsequently is fine-tuned with
expert human feedback on a chemical space of interest. Fine-tuning on focused
datasets improves performance on these chemical scopes over the pre-trained
model exhibiting moderate performance and generalizability. This enables
distinguishing hard- from easy-to-synthesize molecules and improving the
synthetic accessibility of generative model outputs. On very complex scopes
with limited labels achieving satisfactory gains remains challenging. The
FSscore showcases how human expert feedback can be utilized to optimize the
assessment of synthetic feasibility for a variety of applications.