This repository is a curated list of research papers, datasets, and benchmarks focused on generative artificial intelligence and deep learning for molecular and material design. It serves as a comprehensive resource for researchers, engineers, and practitioners in cheminformatics, drug discovery, and materials science.

How It Works

The project categorizes and links to a vast collection of academic papers, covering various deep learning architectures (e.g., VAEs, GANs, Transformers, Diffusion Models) and methodologies applied to molecular optimization, conformation generation, and material design. It also provides pointers to relevant datasets and evaluation metrics used in the field.

Quick Start & Requirements

This repository is a collection of links and does not require installation or execution. It serves as a knowledge base.

Highlighted Details

• Extensive coverage of generative AI techniques for molecular design, including structure-based, ligand-based, and pharmacophore-based approaches.
• Detailed sections on molecular conformation generation, covering various generative models and benchmarks.
• Includes a broad range of datasets and evaluation metrics crucial for benchmarking generative models in chemistry.
• Features a significant number of recent publications (2023-2025), reflecting the rapid advancements in the field.

Maintenance & Community

The repository is maintained by AspirinCode. Further community engagement details are not specified in the README.

Licensing & Compatibility

The repository itself is a collection of links to external resources and does not have its own license. The licensing of the linked papers and datasets would vary.

Limitations & Caveats

As a curated list, the repository's value is dependent on the comprehensiveness and accuracy of the linked resources. It does not provide any code or tools for direct use.