Abstract
BackgroundIn 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.ResultsIn this article, a simple method is described to generate only valid molecules at high frequency (>300,000 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 (>340,000 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.
Highlights
In recent years, there has been a surge of methods developed for in silico molecular generation
The training set was fragmented, molecules were generated from those fragments (Fig. 4)
Results on the GuacaMol molecular generation benchmark can be seen in Table 2 and Fig. 6
Summary
There has been a surge of methods developed for in silico molecular generation. Kwon et al [20] use direct crossover and mutation operators over SMILES strings, combined with Conformational Space Annealing [26]. Their method does not require a training set but can generate invalid SMILES. Yoshikawa et al [27] use a populationbased grammatical evolution approach (ChemGE). While their method is fast and inherently parallel, it requires an initial population of molecules and can generate invalid. 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
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