Self-driving laboratories,
in the form of automated experimentation
platforms guided by machine learning algorithms, have emerged as a
potential solution to the need for accelerated science. While new
tools for automated analysis and characterization are being developed
at a steady rate, automated synthesis remains the bottleneck in the
chemical space accessible to self-driving laboratories. Combining
automated and manual synthesis efforts immediately significantly expands
the explorable chemical space. To effectively direct the different
capabilities of automated (higher throughput and less labor) and manual
synthesis (greater chemical versatility), we describe a protocol,
the RouteScore, that quantifies the cost of combined synthetic routes.
In this work, the RouteScore is used to determine the most efficient
synthetic route to a well-known pharmaceutical (structure-oriented
optimization) and to simulate a self-driving laboratory that finds
the most easily synthesizable organic laser molecule with specific
photophysical properties from a space of ∼3500 possible molecules
(property-oriented optimization). These two examples demonstrate the
power and flexibility of our approach in mixed synthetic planning
and optimization and especially in downselecting promising candidates
from a large chemical space via an
a priori
estimation
of the synthetic costs.