Bhargava Harshita, Sharma Amita, Valadi Jayaraman K, Suravajhala Prashanth, Chatterjee Sreemoyee
Department of Computer Science and IT, IIS (Deemed to be University), Jaipur, Rajasthan, India.
Bioclues.org, Pune, India.
Methods Mol Biol. 2025;2927:195-220. doi: 10.1007/978-1-0716-4546-8_11.
The COVID crisis has accelerated the integration of artificial intelligence (AI) in drug discovery and omics research, providing novel avenues to tackle intricate issues in virology research. AI has lately enabled significant breakthroughs in a wide range of biological disciplines, including genetic variant interpretation, protein structure prediction, disease detection, and pharmaceutical creation. It has prominently assumed a pivotal role in virology research, with generative AI at the forefront of innovation. Generative AI (GAI) is a subset of AI that majorly focuses on creating new data or content resembling existing data through learning underlying patterns and relationships. It has revolutionized virology/omics study by generating synthetic data to augment limited datasets, predicting protein structures, identifying gene regulatory networks, and assisting in drug discovery through virtual screening, accelerating advancements in genomics, proteomics, and metabolomics research. This chapter aims to discuss the basic concept of generative models and their current and future scope in virology.
新冠疫情加速了人工智能（AI）在药物研发和组学研究中的整合，为解决病毒学研究中的复杂问题提供了新途径。最近，人工智能在广泛的生物学领域取得了重大突破，包括基因变异解读、蛋白质结构预测、疾病检测和药物研发。它在病毒学研究中发挥了关键作用，生成式人工智能处于创新前沿。生成式人工智能（GAI）是人工智能的一个子集，主要专注于通过学习潜在模式和关系来创建类似于现有数据的新数据或内容。它通过生成合成数据来扩充有限的数据集、预测蛋白质结构、识别基因调控网络以及通过虚拟筛选协助药物研发，彻底改变了病毒学/组学研究，加速了基因组学、蛋白质组学和代谢组学研究的进展。本章旨在讨论生成式模型的基本概念及其在病毒学中的当前和未来应用范围。
