AspirinCode/papers-for-molecular-design-using-DL
A curated collection of research papers on generative AI and deep learning methods for molecular and drug design.
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This repository aggregates academic papers and resources on applying deep learning to molecular design and drug discovery. It covers generative models including GANs, VAEs, diffusion models, and graph neural networks applied to tasks such as molecular optimization, structure-based design, and material discovery. The list is organized by model type and application area.
Frequently asked
- What is AspirinCode/papers-for-molecular-design-using-DL?
- A curated collection of research papers on generative AI and deep learning methods for molecular and drug design.
- Is papers-for-molecular-design-using-DL open source?
- Yes — AspirinCode/papers-for-molecular-design-using-DL is open source, released under the GPL-3.0 license.
- How popular is papers-for-molecular-design-using-DL?
- AspirinCode/papers-for-molecular-design-using-DL has 946 stars on GitHub.
- Where can I find papers-for-molecular-design-using-DL?
- AspirinCode/papers-for-molecular-design-using-DL is on GitHub at https://github.com/AspirinCode/papers-for-molecular-design-using-DL.