The Synthesizability of Molecules Proposed by Generative Models

Wenhao Gao and Connor W. Coley (2020)

Highlighted by Jan Jensen

Figure 1 from the paper. (c) The authors 2020. The paper tests method c, d, and e

Disclaimer: I implemented one of the methods (graph based GA) being tested. 

It is well known that generative models (including genetic algorithms) can suggest very weird-looking molecules when used to optimise molecular properties. This is the first paper that I have come across that tries to quantify this problem by computing their synthesizability.

A molecule is defined as synthesizable if a computer-assisted synthesis planning (CASP) program can find a synthetic route to the molecule. The CASP program they used (ASKCOS) can find synthetic routes for between 57-89% of molecules sampled from commonly used databases (or subsets) such as ChEMBL and ZINC. These databases generally contain molecules that have been made, so just because ASKCOS can't figure out how to make it doesn't mean it can't be made.

The authors used ASKCOS to determine the fraction of synthesizable molecules suggested by three generative models (one ML-based and two GA-based methods) for several "hard" optimisation problems. The ML-based method tends to predict higher fractions of synthesizable molecules compared to GAs and for some properties none of the 100 top-scoring molecules suggested by the GAs were deemed synthesizable. 

The authors go on to show that, in many cases,  the fraction of synthesizable molecules can be increased significantly by including an empirical synthesizability measure in the scoring function, which is very welcome news to me. Furthermore, the top synthesizable molecules shown in the paper look very reasonable, which suggests that CASP programs can weed out the crazy structures.

One worry is that CASP programs are overly conservative and weed out viable structures that could teach us some genuinely new chemistry, but if generative models are to be taken seriously we obviously need a method to exclude the crazy molecules before we show them to synthetic chemists.

This work is licensed under a Creative Commons Attribution 4.0 International License.