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Machine learning models have dual-use potential, potentially serving both beneficial and malicious purposes.
Neural networks and the bias/variance dilemma
Stuart Geman, Elie Bienenstock, and René Doursat · 1992
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The Convention on the Prohibition of the Development, Production, Stockpiling and Use of Chemical Weapons and on their Destruction: Annex on Chemicals, 1998
Organization for the Prohibition of Chemical Weapons · 1998
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Multi-track Microproliferation: Lessons from Aum Shinrikyo and Al Qaida
Gavin Cameron · 1999
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Correcting for regression dilution bias: comparison of methods for a single predictor variable
Chris Frost and Simon G Thompson · 2000
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Secure analysis of distributed chemical databases without data integration
Alan F Karr, Jun Feng, Xiaodong Lin, Ashish P Sanil, S Stanley Young, and Jerome P Reiter · 2005
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Why relevant chemical information cannot be exchanged without disclosing structures
Dmitry Filimonov and Vladimir Poroikov · 2005
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Reverse engineering chemical structures from molecular descriptors: how many solutions?
Jean-Loup Faulon, W Michael Brown, and Shawn Martin · 2005
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Can topological indices transmit information on properties but not on structures?
Alexandru T Balaban · 2005
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Substructural fragments: an universal language to encode reactions, molecular and supramolecular structures
Alexandre Varnek, Denis Fourches, Frank Hoonakker, and Vitaly P Solov’ev · 2005
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Molecular shape and electrostatics in the encoding of relevant chemical information
Anthony Nicholls and J Andrew Grant · 2005
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Descriptor collision and confusion: Toward the design of descriptors to mask chemical structures
Cristian Bologa, Tharun Kumar Allu, Marius Olah, Michael A Kappler, and Tudor I Oprea · 2005
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Surrogate data–a secure way to share corporate data
Igor V Tetko, Ruben Abagyan, and Tudor I Oprea · 2005
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Share and share alike
David Bradley · 2005
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The centroidal algorithm in molecular similarity and diversity calculations on confidential datasets
Sergey Trepalin and Nikolay Osadchiy · 2005
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Similarity-based descriptors (SIBAR)–A tool for safe exchange of chemical information?
Dominik Kaiser, Barbara Zdrazil, and Gerhard F Ecker · 2005
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Possibilities for transfer of relevant data without revealing structural information
Omoshile O Clement and Osman F Güner · 2005
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Ethical and Philosophical Consideration of the Dual-Use Dilemma in the Biological Sciences
Seumas Miller and Michael J. Selgelid · 2008
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Sharing chemical information without sharing chemical structure
Brian B Masek, Lingling Shen, Karl M Smith, and Robert S Pearlman · 2008
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A Precautionary Principle for Dual Use Research in the Life Sciences
Frida Kuhlau, Anna T. Höglund, Kathinka Evers, and Stefan Eriksson · 2009
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Extended-connectivity fingerprints
David Rogers and Mathew Hahn · 2010
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Great expectations—ethics, avian flu and the value of progress
Nicholas G. Evans · 2012
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Innovation, Dual Use, and Security: Managing the Risks of Emerging Biological and Chemical Technologies
Jonathan B. Tucker, editor · 2012
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The Precautionary Principle and the Dual-Use Dilemma
Steve Clarke · 2013
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United States Government Policy for Institutional Oversight of Life Sciences Dual Use Research of Concern
U.S. Government · 2014
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Ethical Alternatives to Experiments with Novel Potential Pandemic Pathogens
Marc Lipsitch and Alison P. Galvani · 2014
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Superintelligence: Paths, Dangers, Strategies
Nick Bostrom · 2014
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Sharing chemical relationships does not reveal structures
Matthew Matlock and S Joshua Swamidass · 2014
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Risk and Benefit Analysis of Gain of Function Research
Gryphon Scientific · 2015
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Securely measuring the overlap between private datasets with cryptosets
S Joshua Swamidass, Matthew Matlock, and Leon Rozenblit · 2015
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Gain-of-Function Research: Ethical Analysis
Michael J. Selgelid · 2016
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Report of the 2016 Informal Meeting of Experts on Lethal Autonomous Weapons Systems (LAWS)
UNCCCW · 2016
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The De Novo Synthesis of Horsepox Virus: Implications for Biosecurity and Recommendations for Preventing the Reemergence of Smallpox
Gregory D. Koblentz · 2017
Cited alongside, same era.
Communication-efficient learning of deep networks from decentralized data
Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Aguera y Arcas · 2017
Cited alongside, same era.
Low data drug discovery with one-shot learning
Han Altae-Tran, Bharath Ramsundar, Aneesh S Pappu, and Vijay Pande · 2017
Cited alongside, same era.
Semi-supervised classification with graph convolutional networks, 2017
Thomas N. Kipf and Max Welling · 2017
Cited alongside, same era.
Machine learning for molecular and materials science
Keith T Butler, Daniel W Davies, Hugh Cartwright, Olexandr Isayev, and Aron Walsh · 2018
Cited alongside, same era.
Chemistry and Dual Use: From Scientific Integrity to Social Responsibility
GPQA: A graduate-level google-proof Q&A benchmark
David Rein, Betty Li Hou, Asa Cooper Stickland, Jackson Petty, Richard Yuanzhe Pang, Julien Dirani, Julian Michael, and Samuel R Bowman · 2023
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Assessment of chemistry knowledge in large language models that generate code
Andrew D White, Glen M Hocky, Heta A Gandhi, Mehrad Ansari, Sam Cox, Geemi P Wellawatte, Subarna Sasmal, Ziyue Yang, Kangxin Liu, Yuvraj Singh, et al · 2023
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Chemcrow: Augmenting large-language models with chemistry tools, 2023
Andres M Bran, Sam Cox, Andrew D White, and Philippe Schwaller · 2023
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Control risk for potential misuse of artificial intelligence in science
Jiyan He, Weitao Feng, Yaosen Min, Jingwei Yi, Kunsheng Tang, Shuai Li, Jie Zhang, Kejiang Chen, Wenbo Zhou, Xing Xie, et al · 2023
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Emergent autonomous scientific research capabilities of large language models, 2023
Daniil A. Boiko, Robert MacKnight, and Gabe Gomes · 2023
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Jan Mehlich · 2018
Cited alongside, same era.
Biodefense in the Age of Synthetic Biology
National Academies of Sciences, Engineering, and Medicine · 2018
Cited alongside, same era.
Minimax-regret querying on side effects for safe optimality in factored markov decision processes
Shun Zhang, Edmund H Durfee, and Satinder Singh · 2018
Cited alongside, same era.
Low-shot learning from imaginary data, 2018
Yu-Xiong Wang, Ross Girshick, Martial Hebert, and Bharath Hariharan · 2018
Cited alongside, same era.
The classification of noise-afflicted remotely sensed data using three machine-learning techniques: Effect of different levels and types of noise on accuracy
Sornkitja Boonprong, Chunxiang Cao, Wei Chen, Xiliang Ni, Min Xu, and Bipin Kumar Acharya · 2018
Cited alongside, same era.
MoleculeNet: a benchmark for molecular machine learning
Zhenqin Wu, Bharath Ramsundar, Evan N Feinberg, Joseph Gomes, Caleb Geniesse, Aneesh S Pappu, Karl Leswing, and Vijay Pande · 2018
Cited alongside, same era.
Who should we fear more: biohackers, disgruntled postdocs, or bad governments? A simple risk chain model of biorisk
Anders Sandberg and Cassidy Nelson · 2019
Cited alongside, same era.
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Fine-tuning aligned language models compromises safety, even when users do not intend to!
Xiangyu Qi, Yi Zeng, Tinghao Xie, Pin-Yu Chen, Ruoxi Jia, Prateek Mittal, and Peter Henderson · 2023
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Will releasing the weights of large language models grant widespread access to pandemic agents?
Anjali Gopal, Nathan Helm-Burger, Lenni Justen, Emily H Soice, Tiffany Tzeng, Geetha Jeyapragasan, Simon Grimm, Benjamin Mueller, and Kevin M Esvelt · 2023
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Figstep: Jailbreaking large vision-language models via typographic visual prompts
Yichen Gong, Delong Ran, Jinyuan Liu, Conglei Wang, Tianshuo Cong, Anyu Wang, Sisi Duan, and Xiaoyun Wang · 2023
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NeMo guardrails: A toolkit for controllable and safe LLM applications with programmable rails
Traian Rebedea, Razvan Dinu, Makesh Narsimhan Sreedhar, Christopher Parisien, and Jonathan Cohen · 2023
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Llama Guard: LLM-based input-output safeguard for human-AI conversations
Hakan Inan, Kartikeya Upasani, Jianfeng Chi, Rashi Rungta, Krithika Iyer, Yuning Mao, Michael Tontchev, Qing Hu, Brian Fuller, Davide Testuggine, et al · 2023
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MELLODDY: Cross-pharma federated learning at unprecedented scale unlocks benefits in qsar without compromising proprietary information
Wouter Heyndrickx, Lewis Mervin, Tobias Morawietz, Noé Sturm, Lukas Friedrich, Adam Zalewski, Anastasia Pentina, Lina Humbeck, Martijn Oldenhof, Ritsuya Niwayama, et al · 2023
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A watermark for large language models, 2023
John Kirchenbauer, Jonas Geiping, Yuxin Wen, Jonathan Katz, Ian Miers, and Tom Goldstein · 2023
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Mart: Improving LLM safety with multi-round automatic red-teaming
Suyu Ge, Chunting Zhou, Rui Hou, Madian Khabsa, Yi-Chia Wang, Qifan Wang, Jiawei Han, and Yuning Mao · 2023
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Small data machine learning in materials science
Pengcheng Xu, Xiaobo Ji, Minjie Li, and Wencong Lu · 2023
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Improving the quality of chemical language model outcomes with atom-in-SMILES tokenization
Umit V. Ucak, Islambek Ashyrmamatov, and Juyong Lee · 2023
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Are large language models superhuman chemists?
Adrian Mirza, Nawaf Alampara, Sreekanth Kunchapu, Martiño Ríos-García, Benedict Emoekabu, Aswanth Krishnan, Tanya Gupta, Mara Schilling-Wilhelmi, Macjonathan Okereke, Anagha Aneesh, et al · 2024
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Prioritizing safeguarding over autonomy: Risks of LLM agents for science
Xiangru Tang, Qiao Jin, Kunlun Zhu, Tongxin Yuan, Yichi Zhang, Wangchunshu Zhou, Meng Qu, Yilun Zhao, Jian Tang, Zhuosheng Zhang, et al · 2024
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Xiaohan Yuan, Jinfeng Li, Dongxia Wang, Yuefeng Chen, Xiaofeng Mao, Longtao Huang, Hui Xue, Wenhai Wang, Kui Ren, and Jingyi Wang · 2024
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Evaluating frontier models for dangerous capabilities
Mary Phuong, Matthew Aitchison, Elliot Catt, Sarah Cogan, Alexandre Kaskasoli, Victoria Krakovna, David Lindner, Matthew Rahtz, Yannis Assael, Sarah Hodkinson, et al · 2024
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ALERT: A comprehensive benchmark for assessing large language models’ safety through red teaming
Simone Tedeschi, Felix Friedrich, Patrick Schramowski, Kristian Kersting, Roberto Navigli, Huu Nguyen, and Bo Li · 2024
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Introducing v0.5 of the AI safety benchmark from MLCommons
Bertie Vidgen, Adarsh Agrawal, Ahmed M Ahmed, Victor Akinwande, Namir Al-Nuaimi, Najla Alfaraj, Elie Alhajjar, Lora Aroyo, Trupti Bavalatti, Borhane Blili-Hamelin, et al · 2024
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The Llama 3 herd of models
Aaron Grattafiori, Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, et al · 2024
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Towards a dataset for state of the art protein toxin classification
Chance A Challacombe and Nikhil S Haas · 2024
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Energy rank alignment: Using preference optimization to search chemical space at scale
Shriram Chennakesavalu, Frank Hu, Sebastian Ibarraran, and Grant M Rotskoff · 2024
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Safe reinforcement learning with learned non-markovian safety constraints
Siow Meng Low and Akshat Kumar · 2024
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Representation noising: A defence mechanism against harmful finetuning, 2024
Domenic Rosati, Jan Wehner, Kai Williams, Łukasz Bartoszcze, David Atanasov, Robie Gonzales, Subhabrata Majumdar, Carsten Maple, Hassan Sajjad, and Frank Rudzicz · 2024
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The Operational Risks of AI in Large-Scale Biological Attacks: Results of a Red-Team Study
Christopher A Mouton, Caleb Lucas, and Ella Guest · 2024
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Building an early warning system for LLM-aided biological threat creation
Tejal Patwardhan, Kevin Liu, Todor Markov, Neil Chowdhury, Dillon Leet, Natalie Cone, Caitlin Maltbie, Joost Huizinga, Carroll Wainwright, Shawn (Froggi) Jackson, Steven Adler, Rocco Casagrande, and Aleksander Madry · 2024
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Anthony M Barrett, Krystal Jackson, Evan R Murphy, Nada Madkour, and Jessica Newman · 2024
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A review of large language models and autonomous agents in chemistry
Mayk Caldas Ramos, Christopher J Collison, and Andrew D White · 2025
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