![]() RORĪ³ SELFIES drug design machine learning molecular docking neural networks. As per the results, we obtained the structures that possess an affinity to the selected protein domains, namely PDB IDs 7NPC, 7NP5, and 7KXD. The affinity to selected protein domains was verified with the use of the AutoDock tool. Newly created chemical structures are sieved by the quantitative estimation of the drug-likeness descriptor, Lipinski's rule of 5, and the synthetic Bayesian accessibility classifier score. ![]() With the use of the created neural network, novel compounds that are chemically sensible can be generated. On the basis of vectorized SELFIES, new chemical structures can be created. Transferring chemical structures into SELFIES codes helped us pass chemical information to a neural network. The research proposed aims to create new chemical structures supported by a deep neural network that will possess an affinity to the selected protein domains. From that, new structures can be proposed. New chemical spaces can be efficiently searched with the application of artificial intelligence. ![]() The use of molecular docking algorithms can help in new drug development by sieving out the worst drug-receptor complexes. While current databases of known compounds are smaller in magnitude (approximately 108), the number of small drug-like molecules is estimated to be between 10. Drug design with machine learning support can speed up new drug discoveries. ![]()
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