how can AI, computing, and algorithmic thinking improve science, society, and democracy?
My work is both constructive and critical for drug discovery, ranging from identifying an inhibitor of the 3CL-main-protease of SARS-CoV-2 (see PDB: 7LTJ) to novel applications of language models for navigating chemical space formulated as a lattice. I am passionate about translating computing developments to novel scientific approaches (and vice-versa) to better science (and produce tangible scientific results).
Recently, as an outgrowth of AI ethics, my work has asked the more powerful interpretive question:
how do we know when models are improving X, and how can we interpret them as decision-makers?
In science, for example, my work has pointed out that this question is more complicated than many treat it. And in terms of algorithmic justice, we ought to turn towards political theory rather than single notions of ethics. I ask how democratic institutions can channel algorithmic developments through the public? Social integration, deliberation, and, more generally, feeling at home in the world are decreasing under the current regime of AI thought. I’m currently a vising fellow at the Harvard Kennedy School’s Science and Technology Program, working on these new questions.
Selected Publications and Talks
Please see my CV for the most up to date listing, thanks!
MANCEPT WorkshopAustin Clyde, “Created in our Likeness: Is Open-Source AI at Odds with Animal Rights?” MANCEPT Workshop “Politics, Animals, and Technology” organized by the Manchester Centre for Political Theory, September 2021. (accepted for talk)
CAFCW20 @ SC'20 Scaffold-Induced Molecular Subgraphs (SIMSG): Effective Graph Sampling Methods for High-Throughput Computational Drug Discovery. Clyde, A., Shah, Ashka, Zvyagin, M., Ramanathan, A., Stevens, R., Virtual 13 November 2020. (accepted for talk)
PreprintScalable HPC and AI Infrastructure for COVID-19 Therapeutics"
Hyungro Lee, Andre Merzky, Li Tan, Mikhail Titov, Matteo Turilli, Dario Alfe, Agastya Bhati, Alex Brace, Austin Clyde, Peter Coveney, Heng Ma, Arvind Ramanathan, Rick Stevens, Anda Trifan, Hubertus Van Dam, Shunzhou Wan, Sean Wilkinson, Shantenu Jha. 20 October 2020.
PreprintIMPECCABLE: Integrated Modeling PipelinE for COVID Cure by Assessing Better LEads"
Aymen Al Saadi, Dario Alfe, Yadu Babuji, Agastya Bhati, Ben Blaiszik, Thomas Brettin, Kyle Chard, Ryan Chard, Peter Coveney, Anda Trifan, Alex Brace, Austin Clyde, Ian Foster, Tom Gibbs, Shantenu Jha, Kristopher Keipert, Thorsten Kurth, Dieter Kranzlmüller, Hyungro Lee, Zhuozhao Li, Heng Ma, Andre Merzky, Gerald Mathias, Alexander Partin, Junqi Yin, Arvind Ramanathan, Ashka Shah, Abraham Stern, Rick Stevens, Li Tan, Mikhail Titov, Aristeidis Tsaris, Matteo Turilli, Huub Van Dam, Shunzhou Wan, David Wifling. 13 October 2020.
AACR "Virtual Screening with Deep Learning using Cancer Cell Line Dose Response Data", Austin Clyde, Arvind Ramanathan, Rick Stevens. Advancing Precision Medicine Drug Development: Incorporation of Real-World Data and Other Novel Strategies, San Deigo CA. 10 January 2020. (poster)
Janssen Pharmaceutica "Accelerating Virtual Docking Screens with Deep Learning", Austin Clyde. 5 December 2019. (talk)