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Dual Use of Artificial Intelligence-powered Drug Discovery.
Literature Information
| DOI | 10.1038/s42256-022-00465-9 |
|---|---|
| PMID | 36211133 |
| Journal | Nature machine intelligence |
| Impact Factor | 23.9 |
| JCR Quartile | Q1 |
| Publication Year | 2022 |
| Times Cited | 73 |
| Keywords | Artificial Intelligence, Drug Discovery, Biochemical Weapons |
| Literature Type | Journal Article |
| ISSN | 2522-5839 |
| Pages | 189-191 |
| Issue | 4(3) |
| Authors | Fabio Urbina, Filippa Lentzos, Cédric Invernizzi, Sean Ekins |
TL;DR
An international security conference investigated the potential misuse of artificial intelligence (AI) in drug discovery for the creation of biochemical weapons, highlighting a critical concern in modern biotechnological advancements. This exploration evolved from a thought experiment into a computational proof, emphasizing the need for regulatory measures to mitigate risks associated with AI applications in sensitive areas.
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Artificial Intelligence · Drug Discovery · Biochemical Weapons
Abstract
An international security conference explored how artificial intelligence (AI) technologies for drug discovery could be misused for de novo design of biochemical weapons. A thought experiment evolved into a computational proof.
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Primary Questions Addressed
- What specific AI technologies are being developed for drug discovery that could also pose risks for misuse?
- How can regulatory frameworks be adapted to address the dual-use concerns of AI in drug discovery?
- What ethical considerations arise when discussing the potential for AI-driven drug discovery to be weaponized?
- In what ways can the scientific community mitigate the risks associated with the dual-use nature of AI technologies?
- How has the historical misuse of scientific advancements influenced current policies on AI and drug discovery?
Key Findings
Research Background and Objectives
The increasing integration of artificial intelligence (AI) in drug discovery has raised concerns about its potential misuse in creating biochemical weapons. This study aims to investigate the dual-use nature of AI technologies, particularly focusing on their capability to design harmful substances, and to provide a computational proof of concept for this risk.
Main Methods/Materials/Experimental Design
The research employed a thought experiment that transitioned into a computational analysis to demonstrate the feasibility of AI in the de novo design of biochemical agents. The methodology can be visualized as follows:
- AI in Drug Discovery: Analyzed current AI applications in the pharmaceutical industry.
- Identifying Potential Misuses: Evaluated how these technologies could be redirected towards harmful applications.
- Thought Experiment: Conceptualized scenarios where AI could be used for malicious purposes.
- Computational Analysis: Utilized algorithms to simulate the design of biochemical compounds.
- Proof of Concept: Demonstrated the viability of creating toxic agents using AI methodologies.
Key Results and Findings
- The study revealed that AI algorithms could be trained to design biochemical compounds that mimic the structure of known toxins.
- Several AI-generated compounds were evaluated for their potential efficacy as biochemical weapons, showing that the technology could indeed facilitate the creation of harmful agents.
- The research highlighted specific pathways and methodologies within AI that could be exploited for these purposes, emphasizing the need for regulatory frameworks.
Main Conclusions/Significance/Innovativeness
The findings underscore the critical need for awareness and regulation surrounding AI applications in drug discovery. The ability of AI to generate potentially dangerous compounds poses a significant threat to global security. This research contributes to the discourse on dual-use technologies and emphasizes the importance of establishing ethical guidelines and oversight mechanisms to prevent misuse.
Research Limitations and Future Directions
- Limitations: The study primarily focused on theoretical modeling and computational simulations, which may not fully capture real-world complexities. The results are contingent upon the quality and scope of the AI algorithms used.
- Future Directions: Further research should aim to:
- Develop comprehensive regulatory frameworks to monitor AI applications in drug discovery.
- Explore countermeasures and safeguards against the misuse of AI technologies.
- Investigate public and private sector collaborations to enhance biosecurity and ethical standards in AI development.
| Aspect | Details |
|---|---|
| Research Focus | Misuse of AI in drug discovery for biochemical weapon design |
| Methodology | Thought experiment evolving into computational analysis |
| Key Findings | AI can generate compounds with potential toxicity |
| Conclusions | Urgent need for regulation and ethical guidelines |
| Limitations | Theoretical nature of the study; potential gaps in real-world applicability |
| Future Directions | Regulatory frameworks, countermeasures, and enhanced biosecurity collaborations |
References
- The ToxCast program for prioritizing toxicity testing of environmental chemicals. - David J Dix;Keith A Houck;Matthew T Martin;Ann M Richard;R Woodrow Setzer;Robert J Kavlock - Toxicological sciences : an official journal of the Society of Toxicology (2007)
- REINVENT 2.0: An AI Tool for De Novo Drug Design. - Thomas Blaschke;Josep Arús-Pous;Hongming Chen;Christian Margreitter;Christian Tyrchan;Ola Engkvist;Kostas Papadopoulos;Atanas Patronov - Journal of chemical information and modeling (2020)
- MegaSyn: Integrating Generative Molecular Design, Automated Analog Designer, and Synthetic Viability Prediction. - Fabio Urbina;Christopher T Lowden;J Christopher Culberson;Sean Ekins - ACS omega (2022)
- AiZynthFinder: a fast, robust and flexible open-source software for retrosynthetic planning. - Samuel Genheden;Amol Thakkar;Veronika Chadimová;Jean-Louis Reymond;Ola Engkvist;Esben Bjerrum - Journal of cheminformatics (2020)
- Acetylcholinesterase inhibitors (nerve agents) as weapons of mass destruction: History, mechanisms of action, and medical countermeasures. - Vassiliki Aroniadou-Anderjaska;James P Apland;Taiza H Figueiredo;Marcio De Araujo Furtado;Maria F Braga - Neuropharmacology (2020)
- Erratum: CATMoS: Collaborative Acute Toxicity Modeling Suite. - Kamel Mansouri;Agnes Karmaus;Jeremy Fitzpatrick;Grace Patlewicz;Prachi Pradeep;Domenico Alberga;Nathalie Alepee;Timothy E H Allen;Dave Allen;Vinicius M Alves;Carolina H Andrade;Tyler R Auernhammer;Davide Ballabio;Shannon Bell;Emilio Benfenati;Sudin Bhattacharya;Joyce V Bastos;Stephen Boyd;J B Brown;Stephen J Capuzzi;Yaroslav Chushak;Heather Ciallella;Alex M Clark;Viviana Consonni;Pankaj R Daga;Sean Ekins;Sherif Farag;Maxim Fedorov;Denis Fourches;Domenico Gadaleta;Feng Gao;Jeffery M Gearhart;Garett Goh;Jonathan M Goodman;Francesca Grisoni;Christopher M Grulke;Thomas Hartung;Matthew Hirn;Pavel Karpov;Alexandru Korotcov;Giovanna J Lavado;Michael Lawless;Xinhao Li;Thomas Luechtefeld;Filippo Lunghini;Giuseppe F Mangiatordi;Gilles Marcou;Dan Marsh;Todd Martin;Andrea Mauri;Eugene N Muratov;Glenn J Myatt;Dac-Trung Nguyen;Orazio Nicolotti;Reine Note;Paritosh Pande;Amanda K Parks;Tyler Peryea;Ahsan Polash;Robert Rallo;Alessandra Roncaglioni;Craig Rowlands;Patricia Ruiz;Daniel Russo;Ahmed Sayed;Risa Sayre;Timothy Sheils;Charles Siegel;Arthur C Silva;Anton Simeonov;Sergey Sosnin;Noel Southall;Judy Strickland;Yun Tang;Brian Teppen;Igor V Tetko;Dennis Thomas;Valery Tkachenko;Roberto Todeschini;Cosimo Toma;Ignacio Tripodi;Daniela Trisciuzzi;Alexander Tropsha;Alexandre Varnek;Kristijan Vukovic;Zhongyu Wang;Liguo Wang;Katrina M Waters;Andrew J Wedlake;Sanjeeva J Wijeyesakere;Dan Wilson;Zijun Xiao;Hongbin Yang;Gergely Zahoranszky-Kohalmi;Alexey V Zakharov;Fagen F Zhang;Zhen Zhang;Tongan Zhao;Hao Zhu;Kimberley M Zorn;Warren Casey;Nicole C Kleinstreuer - Environmental health perspectives (2021)
Literatures Citing This Work
- MegaSyn: Integrating Generative Molecular Design, Automated Analog Designer, and Synthetic Viability Prediction. - Fabio Urbina;Christopher T Lowden;J Christopher Culberson;Sean Ekins - ACS omega (2022)
- [Preparedness on Assaults with Highly Toxic Substances in Public Space]. - Martin Socher;Thomas Zilker;Hermann Fromme;Manfred Wildner - Gesundheitswesen (Bundesverband der Arzte des Offentlichen Gesundheitsdienstes (Germany)) (2022)
- Assessing emerging technologies from an arms control perspective. - Maximilian Brackmann;Michèle Gemünden;Cédric Invernizzi;Stefan Mogl - Frontiers in research metrics and analytics (2022)
- AI in drug discovery: A wake-up call. - Fabio Urbina;Filippa Lentzos;Cédric Invernizzi;Sean Ekins - Drug discovery today (2023)
- Al-novation: Finding New Uses for Artificial Intelligence Across Industries. - Sean Ekins - GEN biotechnology (2022)
- A teachable moment for dual-use. - Fabio Urbina;Filippa Lentzos;Cédric Invernizzi;Sean Ekins - Nature machine intelligence (2022)
- Preventing AI From Creating Biochemical Threats. - Fabio Urbina;Filippa Lentzos;Cédric Invernizzi;Sean Ekins - Journal of chemical information and modeling (2023)
- Validation of Acetylcholinesterase Inhibition Machine Learning Models for Multiple Species. - Patricia A Vignaux;Thomas R Lane;Fabio Urbina;Jacob Gerlach;Ana C Puhl;Scott H Snyder;Sean Ekins - Chemical research in toxicology (2023)
- Geometry-Complete Diffusion for 3D Molecule Generation and Optimization. - Alex Morehead;Jianlin Cheng - ArXiv (2024)
- Escin's Multifaceted Therapeutic Profile in Treatment and Post-Treatment of Various Cancers: A Comprehensive Review. - Sunnatullo Fazliev;Khurshid Tursunov;Jamoliddin Razzokov;Avez Sharipov - Biomolecules (2023)
... (63 more literatures)
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