Fig. 1.

Human-in-the-loop de novo molecular design: an AI-assistant helps a chemist to decide parameters of an MPO objective function $S_{r,t}\left(x\right)$ iteratively at round $r$ and iteration $t$, where $r$ are rounds of goal-directed molecule generation with a de novo design tool, and $t$ are online interactions with a chemist. The objective consists of K molecular properties $c_k\left(x\right)$ with relative weights $w_k$. The utility of the $k$:th property is measured using a desirability function ${\varphi }_{r,t,k}$ that defines the range of good property values. At each iteration, the method selects a molecule $x_{r,t}$ to query, which the chemist evaluates with feedback $y$. The method then adapts $S_{r,t}\left(x\right)$ based on the feedback by estimating the parameters of ${\varphi }_{r,t,k}$ to match the chemist’s underlying goal