
Apheris provides a technology layer for federated data networks in the life sciences industry, enabling collaborative training of AI models on sensitive, proprietary data without compromising…

Apheris provides a technology layer for federated data networks in the life sciences industry, enabling collaborative training of AI models on sensitive, proprietary data without compromising…
Founded: 2019
Headquarters: Berlin, Germany
Core product: Apheris Gateway — privacy-preserving federated computing platform for life sciences
Focus: Federated data networks and secure local inference for drug discovery
Notable network: AI Structural Biology (AISB) Network with major pharma participants
Series A (~$20.8M) Jan 2025
Data silos and privacy/IP barriers that limit access to diverse, high-quality datasets for AI in drug discovery and life sciences.
2019
Data and Analytics
20800000
Reported round approximately $20.8M; participation from existing investors including Octopus Ventures and Heal Capital.
“Raised a reported Series A led by specialist venture investors (OTB Ventures and eCAPITAL) with participation from existing VCs, indicating continued institutional VC support for its life-sciences federated computing focus.”
About Apheris
At Apheris, we are building the future of how AI is applied in pharmaceutical R&D.
We enable leading pharmaceutical teams to discover and develop drugs faster. We host the industry’s largest federated data networks for drug discovery AI, spanning co-folding, ADMET, and antibody developability.
Across these networks, models are trained on proprietary industry datasets to achieve higher performance and broader applicability while keeping data control and IP protected. We deliver these superior models through drug discovery applications that enable teams to run them at scale, further customize them, and integrate them into existing R&D workflows.
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AI Structural Biology (AISB) Network:
Pharmaceutical companies collaborate in the field of co-folding, structure-based binding affinity
predictions
and antibody design.
ADMET Network:
Pharmaceutical and biotech companies collaborate to improve small-molecule property prediction and expand
in
to further drug modalities.
Antibody Developability Network:
Pharma partners collaborate to federate historical and purpose-built antibody
developability
datasets for secure ML training, without data leaving each partner’s environment.
About the role
We are looking for a Forward-Deployed Cheminformatician to own how binding data is prepared across our co-folding focused networks and initiatives. Binding data is the input that decides whether our co-folding and binding-affinity models perform in real drug programs. It arrives from pharma partners in heterogeneous shapes — different assay registries, different metadata, different chemical-representation standards, different choices on qualifiers, replicates and censoring. We need someone who turns this into a repeatable, well-documented preparation pipeline that pharma representatives can run alongside us, and that scales to the public-data corpus we build for our own model training.
This is half engineering, half forward-deployed work. You will define the protocol, harden it with validators and scripts, integrate it into the Apheris products, run it with each new partner, and own the equivalent pipeline for the public binding-data corpus.
What you will do
Define and own the binding-data preparation protocol — data schema, small-molecule standardization, assay metadata model, value handling (KD, Ki, IC50, pIC50), qualifier and censored-value handling,
duplicate
and replicate aggregation.
Build the tooling that runs it — modular scripts, validators with actionable errors, and reusable pipelines that survive different pharma upstream systems (
Dotmatics
, Spotfire, in-house registries).
Work
forward-deployed
with pharma. Sit with their biologists and medicinal chemists, walk them through the protocol, sense-check what an assay column
actually measures
, and unblock retrieval.
Maintain the small-molecule representation pipeline —
RDKit
standardization, tautomer and ionization handling, stereochemistry preservation,
and
PAINS / frequent-hitter filtering.
Curate the public binding-data foundation —
ChEMBL
,
BindingDB
, PubChem
BioAssay
— prepared to the same standard, so our models train on the strongest public baseline anyone can assemble.
Hand the productized pipeline cleanly to
engineering for scaling, and partner with ML to keep the data contract
valid
as
models and networks evolve.
What we expect from you
You should apply if:
You have a BSc, MSc, PhD or equivalent in cheminformatics, computational chemistry, or a related field, plus 3+ years preparing biological assay data in a discovery setting.
You are fluent in Python and
RDKit
. SMILES normalization, tautomer / ionization / stereochemistry handling, and scaffold extraction are second nature, and you understand why each
matters
for activity cliffs and model training.
You have hands-on experience curating quantitative binding assay data (KD, Ki, IC50, pIC50) and HTS data — censored values, qualifiers, duplicates, replicate aggregation, and assay metadata interpretation.
You write good engineering code — version control, tested modular scripts, validators that return useful errors.
You are comfortable forward-deployed with pharma medicinal chemists and biologists. You can sit in a sense-check meeting, pull out what is
actually meant
by a column label, and encode that back into the protocol.
You enjoy turning a messy ad-hoc cleaning job into a repeatable protocol others can run.
Bonus points if:
You have practical familiarity with public
binding-data
sources (
ChEMBL
,
BindingDB
, PubChem
BioAssay
) and the gotchas in each.
You have applied LLM tooling (Claude, Codex, Cursor) to accelerate data cleaning or metadata harmonization.
You have worked across institutional data boundaries — federated, multi-party, or otherwise — where the data-preparation contract
has to
hold
under partial visibility.
You have a publication record or open-source contributions in cheminformatics or quantitative pharmacology.
What we offer you
Industry-competitive compensation, including early-stage virtual share options
Remote-first work — work where you work best
Wellbeing budget, mental health support, work-from-home budget, co-working stipend, and learning budget
Generous holiday allowance
Office Days at our Berlin HQ or a different European location (3x per year)
A high-
calibre
, execution-focused team with experience from leading organizations