使用具有聚类感知验证的机器学习模型预测VHH的非特异性结合

摘要(中文译文)

※ 中文译文由 AI 辅助生成,仅供学术参考,请以英文原文为准。

非特异性结合的倾向——也称为多反应性——是生物治疗候选药物的严重可开发性风险因素。为了将这一风险降至最低，制药公司越来越多地依赖利用机器学习方法的计算机模拟工具，但开发这些工具具有挑战性。例如，可用数据通常包含许多来自药物研发项目的密切相关的序列，这可能在计算机模拟模型的训练和基准测试中引入显著偏差，导致在新数据上的泛化能力较差。我们在此提出一个工作流程，旨在诊断和缓解与使用研发管线数据相关的一些问题。该工作流程基于一种自定义的交叉验证程序，可以在不同背景下评估模型在未见数据上的性能。作为该工作流程的演示，我们用它来训练一个模型，以预测仅重链可变区片段抗体（VHH）与杆状病毒颗粒（BVP）的结合——这是一种广泛用于非特异性结合的检测方法。利用基于计算蛋白质结构的描述符，该工作流程识别出若干与较高多反应性水平相关的风险因素。

Abstract

Propensity for nonspecific binding-also known as polyreactivity-is a serious developability risk factor for biotherapeutic candidates. To minimize this risk, drug companies are increasingly relying on tools utilizing machine learning methods, but developing these tools is challenging. For example, the available data often contains many closely related sequences originating from drug pipeline projects, which can introduce significant biases in the models training and benchmarking, leading to poor generalizability on new data. We present here a workflow designed to diagnose and mitigate some of the problems associated with using pipeline data. The workflow is based on a custom cross-validation procedure that can evaluate model performance on unseen data in different contexts. As a demonstration of the workflow, we use it to train a model to predict variable heavy-chain only fragment antibodies (VHH) binding to baculovirus particles (BVP)-a widely used assay for nonspecific binding. Using descriptors based on computed protein structures, the workflow identifies several risk factors that correlate with higher polyreactivity levels.

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