ADIOS: Antibody Development via Opponent Shaping
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866910164119977984 |
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| author | Towers, Sebastian Kalisz, Aleksandra Robert, Philippe A. Higueruelo, Alicia Vianello, Francesca Tsai, Ming-Han Chloe Steel, Harrison Foerster, Jakob N. |
| author_facet | Towers, Sebastian Kalisz, Aleksandra Robert, Philippe A. Higueruelo, Alicia Vianello, Francesca Tsai, Ming-Han Chloe Steel, Harrison Foerster, Jakob N. |
| contents | Anti-viral therapies are typically designed to target only the current strains of a virus, a myopic response. However, therapy-induced selective pressures drive the emergence of new viral strains, against which the original myopic therapies are no longer effective. This evolutionary response presents an opportunity: our therapies could both defend against and actively influence viral evolution. This motivates our method ADIOS: Antibody Development vIa Opponent Shaping. ADIOS is a meta-learning framework where the process of antibody therapy design, the outer loop, accounts for the virus's adaptive response, the inner loop. With ADIOS, antibodies are not only robust against potential future variants, they also influence, i.e., shape, which future variants emerge. In line with the opponent shaping literature, we refer to our optimised antibodies as shapers. To demonstrate the value of ADIOS, we build a viral evolution simulator using the Absolut! framework, in which shapers successfully target both current and future viral variants, outperforming myopic antibodies. Furthermore, we show that shapers modify the distribution over viral evolutionary trajectories to result in weaker variants. We believe that our ADIOS paradigm will facilitate the discovery of long-lived vaccines and antibody therapies while also generalising to other domains. Specifically, domains such as antimicrobial resistance, cancer treatment, and others with evolutionarily adaptive opponents. Our code is available at https://github.com/olakalisz/adios. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_10588 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | ADIOS: Antibody Development via Opponent Shaping Towers, Sebastian Kalisz, Aleksandra Robert, Philippe A. Higueruelo, Alicia Vianello, Francesca Tsai, Ming-Han Chloe Steel, Harrison Foerster, Jakob N. Populations and Evolution Artificial Intelligence Computer Science and Game Theory Multiagent Systems 92-08 I.2.1; J.3 Anti-viral therapies are typically designed to target only the current strains of a virus, a myopic response. However, therapy-induced selective pressures drive the emergence of new viral strains, against which the original myopic therapies are no longer effective. This evolutionary response presents an opportunity: our therapies could both defend against and actively influence viral evolution. This motivates our method ADIOS: Antibody Development vIa Opponent Shaping. ADIOS is a meta-learning framework where the process of antibody therapy design, the outer loop, accounts for the virus's adaptive response, the inner loop. With ADIOS, antibodies are not only robust against potential future variants, they also influence, i.e., shape, which future variants emerge. In line with the opponent shaping literature, we refer to our optimised antibodies as shapers. To demonstrate the value of ADIOS, we build a viral evolution simulator using the Absolut! framework, in which shapers successfully target both current and future viral variants, outperforming myopic antibodies. Furthermore, we show that shapers modify the distribution over viral evolutionary trajectories to result in weaker variants. We believe that our ADIOS paradigm will facilitate the discovery of long-lived vaccines and antibody therapies while also generalising to other domains. Specifically, domains such as antimicrobial resistance, cancer treatment, and others with evolutionarily adaptive opponents. Our code is available at https://github.com/olakalisz/adios. |
| title | ADIOS: Antibody Development via Opponent Shaping |
| topic | Populations and Evolution Artificial Intelligence Computer Science and Game Theory Multiagent Systems 92-08 I.2.1; J.3 |
| url | https://arxiv.org/abs/2409.10588 |