Основні поняття
Direct energy-based preference optimization enhances rationality and functionality in antibody design.
Анотація
The content discusses the challenges in antibody design and introduces a method, ABDPO, for optimizing antibodies based on energy preferences. It covers the importance of rational structures and binding affinity in therapeutic antibodies, detailing the process of direct energy-based preference optimization. The method involves fine-tuning diffusion models using residue-level decomposed energy preferences to generate high-quality antibodies with low total energy and strong binding affinity.
1. Introduction:
- Antibodies play a vital role in the immune system.
- Traditional antibody design methods face challenges due to structural complexities.
- Deep generative models have been employed for antibody design.
2. Method:
- ABDPO formulates antibody design as an optimization problem focusing on rationality and functionality.
- Direct energy-based preference optimization is proposed for fine-tuning diffusion models.
- Energy decomposition and conflict mitigation techniques are introduced.
3. Experiments:
- Dataset curation involved using the Structural Antibody Database (SAbDab).
- Comparison with baselines like HERN, MEAN, dyMEAN, DiffAb shows superior performance of ABDPO.
- Visualization of designed antibodies demonstrates reduced clashes and proper antigen interactions.
4. Results:
- ABDPO outperforms other methods in terms of rationality and functionality metrics.
- Success rate of ABDPO reaches 40% in generating high-quality antibodies.
Статистика
Experiments show that our approach effectively optimizes the energy of generated antibodies achieving state-of-the-art performance.
Цитати
"Our method involves fine-tuning the pre-trained diffusion model using a residue-level decomposed energy preference."
"Experiments show that ABDPO outperforms the state-of-the-art baselines by a large margin."