REVIEW article
Front. Artif. Intell.
Sec. Medicine and Public Health
Volume 8 - 2025 | doi: 10.3389/frai.2025.1668794
AI alignment is all your need for future drug discovery
Provisionally accepted
- Yunnan Normal University, Kunming, China
Select one of your emails
You have multiple emails registered with Frontiers:
Notify me on publication
Please enter your email address:
If you already have an account, please login
You don't have a Frontiers account ? You can register here
In recent years, the integration of artificial intelligence (AI) with drug discovery has become a promising frontier in biomedical research. However, as artificial intelligence systems become increasingly complex, ensuring their alignment with human values and goals becomes essential. Specifically, combining artificial intelligence systems with human values is crucial for reducing potential risks in the field of drug discovery and maximizing social benefits. This article explores the concepts and challenges related to alignment with artificial intelligence in the context of drug discovery, emphasizing on human-centered approaches to AI development and deployment. We further investigated popular technology frameworks designed for human-centered AI alignment, aimed at improving the robustness and interpretability of AI models. We provide some insights into the challenges of human-centered AI alignment, which represents a significant advancement in addressing robustness and interpretability, thus taking a step forward in the field of AI alignment research. Finally, we discuss strategies for systematically integrating human values into AI-driven drug discovery systems. This article aims to emphasize the importance of AI alignment as a foundational principle in the field of drug discovery and advocate the perspective that "AI alignment is all your need for future drug discovery".
Keywords: AI alignment, Drug Discovery, Human Values, Generative AI, robustness, Interpretability
Received: 18 Jul 2025; Accepted: 13 Oct 2025.
Copyright: © 2025 Li. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Chunyan Li, lchy@ynnu.edu.cn
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.