Related papers: Active learning for affinity prediction of antibodies

Active learning for affinity prediction of antibodies

URL: http://arxiv.org/abs/2406.07263v1
Date: Tue, 11 Jun 2024 13:42:49 GMT
Title: Active learning for affinity prediction of antibodies
Authors: Alexandra Gessner, Sebastian W. Ober, Owen Vickery, Dino Oglić, Talip Uçar,
Abstract summary: For large molecules such as antibodies, identifying mutations that enhance antibody affinity is challenging. FERB methods can offer valuable insights into how different mutations will impact the potency and selectivity of a drug candidate. We present an active learning framework that iteratively proposes sequences for simulators to evaluate, thereby accelerating the search for improved binders.
Score: 45.58662352490961
License: http://creativecommons.org/licenses/by/4.0/
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.

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