Active learning for affinity prediction of antibodies
This work addresses the problem of costly and time-consuming antibody optimization for drug discovery, though it is incremental as it builds on existing simulation methods.
The paper tackles the challenge of identifying antibody mutations that enhance binding affinity by developing an active learning framework that iteratively proposes sequences for simulation, reducing computational costs and accelerating the search for improved binders.
The primary objective of most lead optimization campaigns is to enhance the binding affinity of ligands. For large molecules such as antibodies, identifying mutations that enhance antibody affinity is particularly challenging due to the combinatorial explosion of potential mutations. When the structure of the antibody-antigen complex is available, relative binding free energy (RBFE) methods can offer valuable insights into how different mutations will impact the potency and selectivity of a drug candidate, thereby reducing the reliance on costly and time-consuming wet-lab experiments. However, accurately simulating the physics of large molecules is computationally intensive. We present an active learning framework that iteratively proposes promising sequences for simulators to evaluate, thereby accelerating the search for improved binders. We explore different modeling approaches to identify the most effective surrogate model for this task, and evaluate our framework both using pre-computed pools of data and in a realistic full-loop setting.