Using AI to design antibodies
Конференцијски прилог (Објављена верзија)
,
© 2023 Institute of Molecular Genetics and Genetic Engineering, University of Belgrade
Метаподаци
Приказ свих података о документуАпстракт
Advancements in antibody engineering are crucial for developing effective and safe therapeutic
candidates. Traditional approaches often involve limited screening of sequence space, resulting
in drug candidates with suboptimal binding affinity, developability, or immunogenicity. However,
recent breakthroughs in deep learning and generative artificial intelligence (AI) offer promising
solutions to overcome these challenges.
In our work, we utilized deep contextual language models trained on high-throughput affinity data
to quantitatively predict binding of unseen antibody sequence variants. Our approach spans a wide
range of binding affinities, demonstrating the potential to optimize antibody engineering. Additionally,
we introduced a “naturalness” metric that measures similarity to natural immunoglobulins. We
found that naturalness is associated with measures of drug developability and immunogenicity,
allowing us to optimize it alongside binding affinity using a genetic algori...thm.
Additionally, we explored generative AI-based antibody design, and achieved successful design of
all complementarity-determining regions (CDRs) in the heavy chain of the antibodies. Our designed
antibodies exhibit high binding rates, surpassing randomly sampled antibodies from the Observed
Antibody Space. Moreover, these AI-designed binders display high diversity, low sequence identity
to known antibodies, and favorable naturalness scores, indicating desirable developability profiles
and reduced immunogenicity.
Collectively, our findings demonstrate the immense potential of deep learning and generative
AI in revolutionizing antibody optimization and design. By leveraging large-scale data, predictive
models, and high-throughput experimentation, we can accelerate and improve our antibody
engineering capabilities. The integration of deep contextual language models and the incorporation
of naturalness into the design process provide intelligent screening approaches. Similarly, the
application of generative AI enables us to efficiently and precisely design antibodies from scratch,
outperforming traditional methods in terms of speed and quality.
Кључне речи:
artificial intelligence / antibody designИзвор:
4th Belgrade Bioinformatics Conference, 2023, 4, 62-62Издавач:
- Belgrade : Institute of molecular genetics and genetic engineering
Напомена:
- Book of abstract: 4th Belgrade Bioinformatics Conference, June 19-23, 2023
Колекције
Институција/група
Institut za molekularnu genetiku i genetičko inženjerstvoTY - CONF AU - Rakočević, Goran PY - 2023 UR - https://belbi.bg.ac.rs/ UR - https://imagine.imgge.bg.ac.rs/handle/123456789/2002 AB - Advancements in antibody engineering are crucial for developing effective and safe therapeutic candidates. Traditional approaches often involve limited screening of sequence space, resulting in drug candidates with suboptimal binding affinity, developability, or immunogenicity. However, recent breakthroughs in deep learning and generative artificial intelligence (AI) offer promising solutions to overcome these challenges. In our work, we utilized deep contextual language models trained on high-throughput affinity data to quantitatively predict binding of unseen antibody sequence variants. Our approach spans a wide range of binding affinities, demonstrating the potential to optimize antibody engineering. Additionally, we introduced a “naturalness” metric that measures similarity to natural immunoglobulins. We found that naturalness is associated with measures of drug developability and immunogenicity, allowing us to optimize it alongside binding affinity using a genetic algorithm. Additionally, we explored generative AI-based antibody design, and achieved successful design of all complementarity-determining regions (CDRs) in the heavy chain of the antibodies. Our designed antibodies exhibit high binding rates, surpassing randomly sampled antibodies from the Observed Antibody Space. Moreover, these AI-designed binders display high diversity, low sequence identity to known antibodies, and favorable naturalness scores, indicating desirable developability profiles and reduced immunogenicity. Collectively, our findings demonstrate the immense potential of deep learning and generative AI in revolutionizing antibody optimization and design. By leveraging large-scale data, predictive models, and high-throughput experimentation, we can accelerate and improve our antibody engineering capabilities. The integration of deep contextual language models and the incorporation of naturalness into the design process provide intelligent screening approaches. Similarly, the application of generative AI enables us to efficiently and precisely design antibodies from scratch, outperforming traditional methods in terms of speed and quality. PB - Belgrade : Institute of molecular genetics and genetic engineering C3 - 4th Belgrade Bioinformatics Conference T1 - Using AI to design antibodies EP - 62 IS - 4 SP - 62 UR - https://hdl.handle.net/21.15107/rcub_imagine_2002 ER -
@conference{ author = "Rakočević, Goran", year = "2023", abstract = "Advancements in antibody engineering are crucial for developing effective and safe therapeutic candidates. Traditional approaches often involve limited screening of sequence space, resulting in drug candidates with suboptimal binding affinity, developability, or immunogenicity. However, recent breakthroughs in deep learning and generative artificial intelligence (AI) offer promising solutions to overcome these challenges. In our work, we utilized deep contextual language models trained on high-throughput affinity data to quantitatively predict binding of unseen antibody sequence variants. Our approach spans a wide range of binding affinities, demonstrating the potential to optimize antibody engineering. Additionally, we introduced a “naturalness” metric that measures similarity to natural immunoglobulins. We found that naturalness is associated with measures of drug developability and immunogenicity, allowing us to optimize it alongside binding affinity using a genetic algorithm. Additionally, we explored generative AI-based antibody design, and achieved successful design of all complementarity-determining regions (CDRs) in the heavy chain of the antibodies. Our designed antibodies exhibit high binding rates, surpassing randomly sampled antibodies from the Observed Antibody Space. Moreover, these AI-designed binders display high diversity, low sequence identity to known antibodies, and favorable naturalness scores, indicating desirable developability profiles and reduced immunogenicity. Collectively, our findings demonstrate the immense potential of deep learning and generative AI in revolutionizing antibody optimization and design. By leveraging large-scale data, predictive models, and high-throughput experimentation, we can accelerate and improve our antibody engineering capabilities. The integration of deep contextual language models and the incorporation of naturalness into the design process provide intelligent screening approaches. Similarly, the application of generative AI enables us to efficiently and precisely design antibodies from scratch, outperforming traditional methods in terms of speed and quality.", publisher = "Belgrade : Institute of molecular genetics and genetic engineering", journal = "4th Belgrade Bioinformatics Conference", title = "Using AI to design antibodies", pages = "62-62", number = "4", url = "https://hdl.handle.net/21.15107/rcub_imagine_2002" }
Rakočević, G.. (2023). Using AI to design antibodies. in 4th Belgrade Bioinformatics Conference Belgrade : Institute of molecular genetics and genetic engineering.(4), 62-62. https://hdl.handle.net/21.15107/rcub_imagine_2002
Rakočević G. Using AI to design antibodies. in 4th Belgrade Bioinformatics Conference. 2023;(4):62-62. https://hdl.handle.net/21.15107/rcub_imagine_2002 .
Rakočević, Goran, "Using AI to design antibodies" in 4th Belgrade Bioinformatics Conference, no. 4 (2023):62-62, https://hdl.handle.net/21.15107/rcub_imagine_2002 .