Generative and Deep Learning In Bioinformatics: A Review of Protein Structure Prediction, Sequence Representation Learning and AI-Assisted Drug Discovery

Abstract

Artificial intelligence, and generative modelling in particular, is creating new opportunities in bioinformatics. Conventional computational biology relied on physics-based simulation, sequence alignment and manual feature engineering, each limited by computational cost and the need for expert interpretation. This paper reviews how deep learning and generative models are used in bioinformatics research. It first examines protein structure prediction, where deep learning achieved a step change in accuracy and extended to complexes of biomolecules, and discusses what such predictions can and cannot tell researchers. It then reviews representation learning on biological sequences, in which transformer models trained on large public sequence databases learn representations that capture structural and evolutionary signal and support downstream tasks such as property and variant-effect prediction. The paper surveys the families of generative models used in the field, including variational autoencoders, graph neural networks, diffusion models and transformers, at the level of their modelling assumptions, and reviews published applications in computational drug discovery such as virtual screening and candidate prioritization. It discusses data resources, evaluation practice, the central role of experimental validation, and the governance of research, including responsible-use and biosecurity frameworks. Research gaps, objectives, a comparison of model families and challenges are presented. The review concludes that these methods are decision-support tools for researchers and do not replace laboratory experimentation or institutional oversight.