arXiv:2602.18982v4 Announce Type: replace Abstract: Common deep learning approaches for antibody engineering focus on modeling the marginal distribution of sequences. By treating sequences as independent samples, however, these methods overlook affinity maturation as a rich and largely untapped source of information about the evolutionary process by which antibodies explore the underlying fitness landscape. In contrast, classical phylogenetic models explicitly represent evolutionary dynamics but lack the expressivity to capture complex epistatic interactions. We bridge this gap with CoSiNE, a
This development is happening now due to the convergence of advanced deep learning techniques with the growing need for more sophisticated antibody engineering methods that currently lack the expressivity to capture complex epistatic interactions.
A strategic reader should care because improved antibody engineering through advanced AI has significant implications for therapeutics, vaccine development, and broader synthetic biology applications, enabling more precise and effective biological interventions.
Traditional antibody engineering, which primarily focuses on marginal sequence distributions, will be augmented by methods like CoSiNE that can explicitly represent evolutionary dynamics and complex epistatic interactions, leading to more robust designs.
- · Biopharmaceutical companies
- · Synthetic biology research institutions
- · AI-driven drug discovery platforms
- · Patients with complex diseases
- · Traditional antibody engineering methods
- · Companies relying solely on empirical screening
More efficient and effective design of therapeutic antibodies and improved vaccine candidates.
Acceleration in the development of new biological drugs and a reduction in R&D costs within the biopharma sector.
Potential for an entirely new generation of programmable biological interventions with implications for global health and bio-defense.
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Read at arXiv cs.LG