Active learning for affinity prediction of antibodies

TL;DR

This paper introduces an active learning framework that efficiently predicts antibody affinity improvements by iteratively selecting promising mutations for simulation, reducing computational costs and experimental efforts.

Contribution

The work presents a novel active learning approach tailored for antibody affinity prediction, optimizing mutation selection to accelerate drug development.

Findings

Active learning significantly reduces the number of simulations needed.

The framework outperforms traditional methods in identifying high-affinity antibodies.

Different surrogate models are evaluated to optimize prediction accuracy.

Abstract

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.