This study explores a multi-stage deep learning pipeline for designing and refining novel molecular structures with drug-like properties. By mapping discrete molecular representations into continuous vector space, the approach enables the generation of new molecules with desired characteristics. An Attention-based Sequence-to-Sequence model is integrated to correct structural errors, acting as a “spellchecker” for molecular design. Additionally, oversampling techniques improve the diversity of generated molecules, even when using small datasets. Computational simulations validate the method, ensuring precise control over molecular properties such as the Synthetic Accessibility Score and drug-likeness indicators.
Takeaways:
- AI-Powered Drug Design: A deep learning pipeline streamlines molecular structure generation and screening.
- Error Correction with Attention Models: Structural accuracy is improved using an Attention-based Sequence-to-Sequence model.
- Enhanced Molecular Diversity: Oversampling in continuous space helps create diverse molecular structures, even with limited data.
- Computational Validation: Generated molecules are tested through simulations to confirm desired properties.
- Controlled Drug-Likeness Metrics: The method ensures molecules meet specific drug-likeness criteria, including the Synthetic Accessibility Score.
- Impact on Drug Discovery: This approach provides a scalable and systematic way to generate potential drug candidates efficiently.
