Industry-Scale Orchestrated Federated Learning for Drug Discovery

Oldenhof, Martijn; Ács, Gergely; Pejó, Balázs; Schuffenhauer, Ansgar; Holway, Nicholas; Sturm, Noé; Dieckmann, Arne; Fortmeier, Oliver; Boniface, Eric; Mayer, Clément; Gohier, Arnaud; Schmidtke, Peter; Niwayama, Ritsuya; Kopecky, Dieter; Mervin, Lewis; Rathi, Prakash Chandra; Friedrich, Lukas; Formanek, András; Péter, Antal; Rahaman, Jordon; Zalewski, Adam; Oluoch, Ezron; Stößel, Manuel; Vančo, Michal; Endico, David; Gelus, Fabien; Boisfossé, Thaïs de; Darbier, Adrien; Nicollet, Ashley; Blottière, Matthieu; Teleńczuk, Maria; Nguyen, Van Tien; Martinez, Thibaud; Boillet, Camille; Moutet, Kelvin; Picosson, Alexandre; Gasser, Aurélien; Djafar, Inal; Arany, Ádám; Simm, Jaak; Moreau, Yves; Engkvist, Ola; Ceulemans, Hugo; Marini, Camille; Galtier, Mathieu

doi:10.48550/arxiv.2210.08871

Cited by 6 publications

(4 citation statements)

References 29 publications

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“…In this study, only binary classification tasks were considered. Small-molecule SMILES are standardised following the MELLODDY-Tuner protocol (Oldenhof et al 2022). MELLODDY-Tuner is also used to identify LSH-based as well as Murcko scaffold-based splits, which can be used optionally.…”

Section: Data Pre-processingmentioning

confidence: 99%

First fully-automated AI/ML virtual screening cascade implemented at a drug discovery centre in Africa

Turón¹,

Hlozek

Woodland

et al. 2022

Preprint

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We present ZairaChem, an artificial intelligence (AI)- and machine learning (ML)-based tool to train small-molecule activity prediction models. ZairaChem is fully automated, requires low computational resources and works across a broad spectrum of datasets, ranging from whole-cell growth inhibition assays to drug metabolism properties. The tool has been implemented end-to-end at the Holistic Drug Discovery and Development (H3D) Centre, the leading integrated drug discovery unit in Africa, at which no prior AI/ML capabilities were available. We have exploited in-house data collected from over a decade of drug discovery research in malaria and tuberculosis and built models to predict the outcomes of 15 key checkpoint assays. We subsequently deployed these models as a virtual screening cascade at an organisational scale to increase the hit rate of current experimental assays. We show how computational profiling of compounds, prior to synthesis and experimental testing, can increase the rate of progression by up to 40%. Moreover, we demonstrate that the approach can be applied to prioritise small molecules within a chemical series and to assess the likelihood of success of novel chemotypes, promoting efficient usage of limited experimental resources. This project is part of a first-of-its-kind collaboration between the H3D Centre, a research centre operating in a low-resource setting, and the Ersilia Open Source Initiative, a young tech non-profit devoted to building data science capacity in the Global South.

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Section: Data Pre-processingmentioning

confidence: 99%

First fully-automated AI/ML virtual screening cascade implemented at a drug discovery centre in Africa

Turón¹,

Hlozek

Woodland

et al. 2022

Preprint

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“…FL has demonstrated potential in accelerating drug discovery and development processes. Very recently, 10 pharmaceutical companies, academic research labs, large industrial companies and startups constructed a large industry-scale FL model for drug discovery without sharing the confidential data sets [4]. Diverse patient data can be trained on the FL models to potentially achieve the identification of potential drug targets, prediction of drug efficacy, and optimization of treatment protocols, while confidentiality of the patient data are preserved.…”

Section: Introductionmentioning

confidence: 99%

A Review of Privacy Enhancement Methods for Federated Learning in Healthcare Systems

Gu,

Sabrina,

Fan

et al. 2023

IJERPH

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Federated learning (FL) provides a distributed machine learning system that enables participants to train using local data to create a shared model by eliminating the requirement of data sharing. In healthcare systems, FL allows Medical Internet of Things (MIoT) devices and electronic health records (EHRs) to be trained locally without sending patients data to the central server. This allows healthcare decisions and diagnoses based on datasets from all participants, as well as streamlining other healthcare processes. In terms of user data privacy, this technology allows collaborative training without the need of sharing the local data with the central server. However, there are privacy challenges in FL arising from the fact that the model updates are shared between the client and the server which can be used for re-generating the client’s data, breaching privacy requirements of applications in domains like healthcare. In this paper, we have conducted a review of the literature to analyse the existing privacy and security enhancement methods proposed for FL in healthcare systems. It has been identified that the research in the domain focuses on seven techniques: Differential Privacy, Homomorphic Encryption, Blockchain, Hierarchical Approaches, Peer to Peer Sharing, Intelligence on the Edge Device, and Mixed, Hybrid and Miscellaneous Approaches. The strengths, limitations, and trade-offs of each technique were discussed, and the possible future for these seven privacy enhancement techniques for healthcare FL systems was identified.

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“…Federated learning has already been applied to drug discovery, as assessed by the recent outcomes of the MELLODDY project, which involved ten pharma companies, sharing federated information for a total of more than 21 million small molecules. , The MELLODDY approach uses multitask learning and underlying deep neural network architectures. In brief, local models were trained at each company, and only the gradients were exchanged, thus avoiding the disclosure of information on the underlying data and on the modeled end points.…”

Section: Introductionmentioning

confidence: 99%

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

Bassani,

Brigo,

Andrews-Morger

2023

Chem. Res. Toxicol.

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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.

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Industry-Scale Orchestrated Federated Learning for Drug Discovery

Cited by 6 publications

References 29 publications

First fully-automated AI/ML virtual screening cascade implemented at a drug discovery centre in Africa

First fully-automated AI/ML virtual screening cascade implemented at a drug discovery centre in Africa

A Review of Privacy Enhancement Methods for Federated Learning in Healthcare Systems

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

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