Molecular generation by Fast Assembly of (Deep)SMILES fragments
Francois Berenger, Koji Tsuda
Abstract
BACKGROUND: In recent years, in silico molecular design is regaining interest. To generate on a computer molecules with optimized properties, scoring functions can be coupled with a molecular generator to design novel molecules with a desired property profile. RESULTS: In this article, a simple method is described to generate only valid molecules at high frequency ([Formula: see text] molecule/s using a single CPU core), given a molecular training set. The proposed method generates diverse SMILES (or DeepSMILES) encoded molecules while also showing some propensity at training set distribution matching. When working with DeepSMILES, the method reaches peak performance ([Formula: see text] molecule/s) because it relies almost exclusively on string operations. The "Fast Assembly of SMILES Fragments" software is released as open-source at https://github.com/UnixJunkie/FASMIFRA . Experiments regarding speed, training set distribution matching, molecular diversity and benchmark against several other methods are also shown.