Federated Learning in Computational Toxicology: An Industrial Perspective on the Effiris Hackathon

Abstract

In silico approaches have acquired a towering role in pharmaceutical research and development, allowing laboratories all around the world to design, create, and optimize novel molecular entities with unprecedented efficiency. From a toxicological perspective, computational methods have guided the choices of medicinal chemists toward compounds displaying improved safety profiles. Even if the recent advances in the field are significant, many challenges remain active in the on-target and off-target prediction fields. Machine learning methods have shown their ability to identify molecules with safety concerns. However, they strongly depend on the abundance and diversity of data used for their training. Sharing such information among pharmaceutical companies remains extremely limited due to confidentiality reasons, but in this scenario, a recent concept named "federated learning" can help overcome such concerns. Within this framework, it is possible for companies to contribute to the training of common machine learning algorithms, using, but not sharing, their proprietary data. Very recently, Lhasa Limited organized a hackathon involving several industrial partners in order to assess the performance of their federated learning platform, called "Effiris". In this paper, we share our experience as Roche in participating in such an event, evaluating the performance of the federated algorithms and comparing them with those coming from our in-house-only machine learning models. Our aim is to highlight the advantages of federated learning and its intrinsic limitations and also suggest some points for potential improvements in the method.

Introduction to the Special Issue: AI Meets Toxicology

“…New emerging approaches can leverage company data by using secure multiparty computation, where calculations are performed using encrypted data, or by means of federated learning, where local models are trained in each company and only gradients are exchanged thus keeping underlying data secure as exemplified by the innovative MELLODDY project . Bassani et al described the experience of Roche scientists, who used an alternative method in which local models predicted an unlabeled set, which was then used to teach the federated model, thus exploiting the idea of surrogate data sharing . DL can especially capitalize on such methods particularly in the toxicology field, where data are sparse, limited, and frequently distributed between multiple partners.…”