Long short-term memory-based chemical language models for bioactive molecular generation using tailored pre-training datasets

Key Points

• Molecular generation outcomes show significant improvements using tailored pre-training datasets for chemical language models.
• Bioactive molecules produced in this approach demonstrate enhanced properties, indicating potential applications in drug design.
• Long short-term memory networks are utilized for modeling chemical structures, generating a diverse array of bioactive compounds.
• The findings highlight the importance of pre-training strategies, which may enable more efficient drug discovery processes.