In the master's thesis we've tested the use of a genetic algorithm in conjuction with the concept of citizen science in the in silico molecular docking of small molecules in protein targets. Out of the tested approaches for finding the best ligands, we've found out that the combination with using a genetic algorithm (with the right SMARTS filters) in conjuction of using a gamified approach of citizen science gave the best results. In this approach, individuals, who were mostly high school students, had the possibility of proposing completely new molecules or improve molecules created by other students and at the same time compete among each other in finding the molecule with the best score. In parallel to the input from indidividuals, the genetic algorithm developed (mutated) further the best molecule proposals. The algorithm automatically removed mutated molecules and the proposed molecules by individuals, that haven't passed a simple filter for biological availability that used simple chemical descriptors (Veber filter). Molecules which contained groups, determined via SMARTS filters, that are chemically unstable, reactive, toxic or were classified as PAINS molecules, which predict an unwanted unselective binding of molecules to various targets, were also removed. This sinergy of approaches gave much better results in the sense of predicted binding energy of docking, as it has resulted in 100 ligands with a better predicted binding energy, than the 2nd best ligand that has been found using with the classical strategy of using libraries of compounds. Despite excellent results of this approach, it has been found out, using thorough literature research, that there's a potential problem of using this approach in practice, as we suspected that the best found molecules using this approach, wouldn't achieve such good binding energies in practice in vitro. The limitation is in the quick computational models for molecular docking, which are for now unsuited for prediction of bad binding of molecules. Consequentially our results might not match practical measurments. In this thesis, the later claims weren't tested in practice i.e. in vitro.
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