# A review of recent advances in generative artificial intelligence models for biomolecular sciences

## TL;DR

This paper reviews recent developments in generative AI models and their applications in biomolecular sciences, such as drug discovery and protein design.

## Contribution

The paper provides a systematic overview of generative AI models in biomolecular sciences and identifies key challenges and future directions.

## Key findings

- Generative models like VAEs, GANs, and diffusion models are being used for molecular design and property prediction.
- Current challenges include model interpretability, scalability, and the need for better molecular datasets.
- Emerging research directions aim to improve the reliability and applicability of these models in biomolecular problems.

## Abstract

Generative artificial intelligence (AI) models, a class of AI techniques that learn data distributions to synthesize novel samples, have emerged as impactful tools across scientific disciplines. In recent years, these models have found extensive applications in fields such as natural language processing and biomedical sciences. Despite their growing influence, comprehensive reviews on the application of generative models in biomolecular sciences remain limited. In this review, we provide a systematic overview of recent advances in generative models applied to biomolecular sciences. We discuss several prominent generative architectures, including variational autoencoders, generative adversarial networks, and diffusion models, highlighting their applications in molecular design and bioinformatics. Additionally, we examine how these models contribute to critical challenges such as molecular property prediction and molecular generation. Finally, we discuss key challenges that remain in this field, including model interpretability, scalability, and the need for high-quality molecular datasets. We highlight emerging research directions that aim to overcome these limitations and propose strategies for improving the reliability and applicability of generative models in biomolecular problems. Through this review, our objective is to provide researchers with a comprehensive understanding of the current landscape of generative modeling in biomolecular sciences and to inspire further advancements in this interdisciplinary area.

This article overviews generative artificial intelligence models, highlighting their key applications in drug discovery, reaction prediction, protein design, and integration with quantum methods, while showcasing various architectures like VAEs, GANs, and diffusion models.Image 1