An Evolutionary Fragment-based Approach to Molecular Fingerprint Reconstruction

For in silico drug discovery various representations have been established regarding storing and processing molecular data. The choice of representation has a great impact on employed methods and algorithms. Molecular fingerprints in the form of fixed-size bit vectors are a widely used representation which captures structural features of a molecule and enables a straight-forward way of estimating molecule similarities. However, since fingerprints are not invertible, they are rarely utilized for molecule generation tasks. This study presents an approach to the reconstruction of molecules from their fingerprint representation that is based on genetic algorithms. The algorithm assembles molecules from BRICS fragments and therefore only generates valid molecular structures. We demonstrate that the genetic algorithm is able to construct molecules similar to the specified target, or even reconstruct the original molecule. Furthermore, to illustrate how this genetic algorithm unlocks fingerprints as a representation for other in silico drug discovery methods, a novel Transformer neural language model trained on molecular fingerprints is introduced as a molecule generation model.

Automated summary

This study presents a genetic algorithm-based approach for reconstructing molecules from their fingerprint representation and introduces a novel Transformer neural language model trained on molecular fingerprints for molecule generation.