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The Apheris Resource Library

Life Sciences Data Networks for AI

Regulation

News

AI Drug Discovery

Pharma

Platform & Technology

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Co-Folding AI

Computational Governance

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Machine Learning

ADMET

Product Release

NVIDIA FLARE

NVIDIA

Apheris rethinks the AI data bottleneck in life science with federated computing

TechChrunch

Apheris raises Series-A with leading European investors. OTB is leading the round in collaboration with eCAPITAL.

When does protein–ligand co-folding become useful in real medicinal chemistry?

by Dr. Rosemary Huckvale

Using a recent J. Med. Chem. TrmD study, we evaluate how well OpenFold3 protein–ligand co-folding recovers binding modes and key interactions, and where it can realistically support medicinal chemistry decisions.

AI Drug Discovery

Co-Folding AI

Pharma

Building fine-tuning capabilities for co-folding models in pharmaceutical research

Innovations in Pharmaceutical Technology (IPT) Magazine

Co-folding models perform well on benchmarks but struggle on novel targets. Real impact comes from building internal capabilities: benchmarking on proprietary data, fine-tuning where needed, and extending generalisability. Read Robin Röhm's IPT piece to learn more

Fine-tuning OpenFold3 on a small set of structures: The PDE10A case study

by Alwin Otto Bucher

We fine-tuned OpenFold3 on just 10 PDE10A protein–ligand complexes and evaluated on 17 held-out structures. Even this low-n setup corrected systematic pose errors and improved interface metrics, making predictions more usable for design decisions.

AI Drug Discovery

Co-Folding AI

Machine Learning

Beyond the data-volume assumption: The role of complementary data in ADMET modelling

by Dr. Lewis Mervin

Improved ADMET predictions come from complementary chemistry, not sheer data volume. A scientific study of public–proprietary integration shows why diversity, balance, and harmonization matter for reliability, calibration, and broader model applicability.

Federated Learning

ADMET

Machine Learning

Federated Learning for ADMET Prediction: Expanding Model Applicability

This blog explores state-of-the-art science in ADMET prediction, showing how federated learning enables pharma companies to collaboratively train models on diverse data, achieving higher accuracy and broader applicability without compromising data privacy.

Federated Learning

Machine Learning

Hands-On with ApherisFold: Reproducing a SIK3–AMPK Selectivity Study Using OpenFold3

We tested how well OpenFold3 predicts a novel protein–ligand complex by reproducing a SIK3–inhibitor complex and analyzing its selectivity over AMPK. Using ApherisFold, we accurately replicated the experimental ligand pose and could rationalize the observed selectivity through steric effects.

Co-Folding AI

Machine Learning

Advancing protein prediction with Federated Learning on NVIDIA DGX Cloud

Apheris shares insights from federating large protein models, OpenFold3 and Boltz-1, on NVIDIA DGX Cloud with NVFLARE. The results show that with the right setup, federated learning can match centralized training—enabling secure, collaborative AI in drug discovery.

Co-Folding AI

NVIDIA FLARE

NVIDIA

Insights into the Unknown: Federated Data Diversity Analysis on Molecular Data

arXiv

AI methods often rely on limited public datasets, restricting industrial impact. Federated learning enables secure collaboration across private pharma data, but data access remains a major challenge.

Apheris Launches ApherisFold to Make OpenFold3 Securely Usable in Pharma Environments

by Marie Roehm

Apheris, a leading provider of AI applications for drug discovery, today announced the launch of ApherisFold, an enterprise software product that enables pharmaceutical organizations to securely run, benchmark, and fine-tune the latest co-folding models, including OpenFold3 and Boltz-2, directly within their own IT environments.

Co-Folding AI

Product Release

OpenFold Expands AI for Biological Modeling Mission with Two New Members

Business Wire

Apheris will bring privacy-preserving, AI infrastructure and collaboration on proprietary data to the OpenFold Consortium

AISB Network expands Federated OpenFold3 initiative with three new pharma contributors

Discover Pharma

The AI Structural Biology (AISB) Network, powered by Apheris GmbH, has expanded its Federated OpenFold3 Initiative with the addition of Astex Pharmaceuticals, Bristol Myers Squibb, and Takeda. The new partners join founding members AbbVie and Johnson & Johnson to fine-tune OpenFold3 on proprietary structural datasets.

AISB Network Expands Federated OpenFold3 Initiative with Three New Pharma Contributors

by Marie Roehm

Apheris announced today the expansion of the Federated OpenFold3 Initiative from the AI Structural Biology (AISB) Network.

News

Co-Folding AI

AI Drug Discovery

Ginkgo Datapoints and Apheris Launch Antibody Developability Consortium

Gingko Datapoints

Ginkgo Bioworks today announced a series of new initiatives from its Datapoints offering to accelerate the application of artificial intelligence in biologics drug discovery. These include a strategic partnership with Apheris to launch the Antibody Developability Consortium and, separately, the AbDev AI Competition.

Federated learning for lesion segmentation in multiple sclerosis: a real-world multi-center feasibility study

Frontiers

In this proof-of-concept work, we aim to apply and adopt Federated Learning (FL) in a real-world hospital setting. We assessed FL for MS lesion segmentation using the self-configuring nnU-Net model, leveraging 512 MRI cases from three sites without sharing raw patient data.

Why co-folding models are here to stay

by Dr. Lukas Pluska

Co‑folding models, like AlphaFold 3, Boltz‑2, and OpenFold3, can predict the joint 3D structures of two (or more) molecules at the same time. While these models perform well on public benchmarks, they often become less accurate when applied to novel targets underrepresented in the training data.

Co-Folding AI

AI Drug Discovery

Federated learning-based protein language models with Apheris on AWS

AWS

In collaboration with AWS, we implemented FRA-LoRA (Full Rank Aggregation of Low-Rank Adapters) in a federated setting to fine-tune ESM-2 across multiple sites, all without sharing raw data. LoRA reduced trainable parameters to <2% of the original model, cutting communication overhead while preserving accuracy.

AI for ADMETox predictions: state-of-the-art

BioAscent

In the ever-evolving landscape of drug discovery, understanding how a drug behaves in the body is crucial. In this blog, Dr Angelo Pugliese explores the pivotal role ADMETox plays in this process.

Aggregating Low Rank Adapters in Federated Fine-tuning

arXiv

Fine-tuning large language models requires high computational and memory resources, and is therefore associated with significant costs. When training on federated datasets, an increased communication effort is also needed. For this reason, parameter-efficient methods (PEFT) are becoming increasingly important.

Toward a tipping point in federated learning in healthcare and life sciences

Patterns (a Cell Press journal)

We discuss the real-world application of federated learning (FL) in the healthcare and life sciences industry, noting a tipping point in its adoption beyond academia.

Secure AI Collaboration Will Fine-Tune OpenFold3 with Proprietary Data

Genetic Engineering & Biotechnology News

In a new initiative by the AI Structural Biology (AISB) Consortium and powered by Apheris, OpenFold3, a protein structure prediction algorithm developed by the lab of Mohammed AlQuraishi, will be fine-tuned using proprietary data from AbbVie and Johnson & Johnson.

AbbVie, J&J to add proprietary data to AIprotein model in bid to accelerate drugdiscovery

STAT

AbbVie, J&J to add proprietary data to AIprotein model in bid to accelerate drugdiscovery. OpenFold3 will access the companies’ data using federation technology from Apheris

AlphaFold is running out of data - so drug firms are building their own version

Nature News

Thousands of 3D protein structures locked up in big-pharma vaults will be usedto create a new AI tool that won’t be open to academics.

AlQuraishi lab’s OpenFold3 to Be Fine-Tuned with Pharma Industry Data in a Secure AI Collaboration Powered by Apheris

by Marie Roehm

OpenFold3, a structure prediction system developed by AlQuraishi Lab at Columbia University, will be fine-tuned using proprietary data from AbbVie and Johnson & Johnson in a confidentiality-preserving and secure federated environment powered by Apheris.

AI Drug Discovery

Pharma

News

Apheris Achieves SOC 2 Type I Attestation – Reinforcing Our Commitment to Security

Apheris achieves SOC 2 Type I attestation, reaffirming its commitment to data security and privacy, and the comprehensive measures we have implemented to protect sensitive information and ensure the highest level of security.

Security

Platform & Technology

Apheris Launches Trust Center, Elevating Security and Data Privacy Standards

by Marie Roehm

The Apheris Trust Center serves as a comprehensive resource for organizations seeking to uphold the highest standards of security and data privacy. It offers guidance and our certifications and attestations, including ISO 27001 and SOC 2, that customers value as essential components to fulfil their compliance obligations.