  Discrete chemical reaction networks formalize the interactions of molecular
species in a well-mixed solution as stochastic events. Given their basic
mathematical and physical role, the computational power of chemical reaction
networks has been widely studied in the molecular programming and distributed
computing communities. While for Turing-universal systems there is a universal
measure of optimal information encoding based on Kolmogorov complexity,
chemical reaction networks are not Turing universal unless error and unbounded
molecular counts are permitted. Nonetheless, here we show that the optimal
number of reactions to generate a specific count $x \in \mathbb{N}$ with
probability $1$ is asymptotically equal to a ``space-aware'' version of the
Kolmogorov complexity of $x$, defined as $\mathrm{\widetilde{K}s}(x) =
\min_p\left\{\lvert p \rvert / \log \lvert p \rvert +
\log(\texttt{space}(\mathcal{U}(p))) : \mathcal{U}(p) = x \right\}$, where $p$
is a program for universal Turing machine $\mathcal{U}$. This version of
Kolmogorov complexity incorporates not just the length of the shortest program
for generating $x$, but also the space usage of that program. Probability $1$
computation is captured by the standard notion of stable computation from
distributed computing, but we limit our consideration to chemical reaction
networks obeying a stronger constraint: they ``know when they are done'' in the
sense that they produce a special species to indicate completion. As part of
our results, we develop a module for encoding and unpacking any $b$ bits of
information via $O(b/\log{b})$ reactions, which is information-theoretically
optimal for incompressible information. Our work provides one answer to the
question of how succinctly chemical self-organization can be encoded -- in the
sense of generating precise molecular counts of species as the desired state.
