To this end, we present a formulation of synthesis planning with starting material constraints.

Unfortunately, these theoretical results cannot well explain the empirical successes of deep learning well, as they require the model size tobenolargerthan O(n)(thegeneralization boundsbecomevacuousotherwise).

Tosatisfy that, architectures must provide promising latency and performance trade-offs, support a variety of tasks, scale efficiently with respect to the amounts of data and compute, leverage available data from other tasks, and efficiently support various hardware.

However, most existing machine learning methods for small molecule generation suffer from poor synthesizability of candidate compounds, making experimental validation difficult.