Diversification distal to the electrophile profoundly alters the protein interaction landscape of covalent stereoprobes, revealing GRPEL1–HSPA9 disruptors and nonorthosteric liganding events missed by...

Diversification distal to the electrophile profoundly alters the protein interaction landscape of covalent stereoprobes, revealing GRPEL1–HSPA9 disruptors and nonorthosteric liganding events missed by...

The computational design of small-molecule ligands for pharmaceutical targets has historically been hindered by a strict trade-off between screening throughput and predictive accuracy. While next-gene...

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. 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 into further drug modalities. Antibody Developability Network: Pharma partners collaborate to federate historical and purpose-built antibody developability data sets for secure ML training, without data leaving each partner’s environment. About the role At Apheris, we power federated data network in life sciences to address the data bottleneck in training highly performant ML models. Publicly available, molecular datasets are insufficient to train high-quality ML models that meet industry requirements. Our product addresses this by hosting networks where biopharma organizations collaboratively train higher quality models on their combined data. The Apheris product is a set of drug discovery applications - enriched with the proprietary data of network participants. Our federated computing infrastructure with built-in governance and privacy controls ensure that the data IP and ownership always stays with the data custodians. As we are doubling down on structural biology use cases as a focus area within our drug discovery work, we are looking for a Senior ML Engineer to drive the technical execution for our structural biology models. This is a hands-on, high-impact role focused on advancing the state of the art in applying foundational models to structural biology problems. You’ll work closely with our leadership team and will serve as the technical authority on ML modelling, architecture, and experimentation in this domain. You should bring deep expertise in training and deploying contemporary models for protein structure prediction and related tasks. You must also understand the application of these models in drug discovery workflows and have a track record of setting strategy, breaking down complex technical problems, and delivering impactful ML systems. If you want to be part of a mission-driven team building cutting-edge AI systems for life sciences – and you know what it takes to move from foundational models to domain-specific impact – this role is for you. What you will do Build and implement ML applications in structural biology, particularly around fine-tuning and extending foundational models like OpenFold, Boltz-2 and ESMFold. Design and implement model extensions for specific tasks such as protein complex and binding affinity prediction, including data distillation, benchmarking, and evaluation pipelines. Work with our customers and academic partners to define data preprocessing, selection, and benchmarking strategies for novel training tasks involving protein structures, complexes, and multimodal biological data. Carry out case-studies associated with the above, providing scientific and technical expertise to our customers. You will be involved in the full project pipeline, from scoping through to results delivery and dissemination. Design, build, and maintain scalable machine learning models and the pipelines needed for training, inference, and deployment in production. Collaborate cross-functionally to ensure models address real-world drug discovery needs. Contribute to publications or open-source contributions where relevant. What we expect from you By month 3: Develop a deep technical understanding of the Apheris product and how it maps to the current Structural Biology use-cases we are working on. Contribute to delivery of at least one customer-driven cofolding project. By month 6: Building on the outcomes of the aforementioned project, build a customer-ready package for results analysis. Work with our privacy team to understand potential for model reverse-engineering. Drive adoption of the Apheris-generated models with our customers for real-world drug discovery pipelines. By month 12: Take ownership of a customer-driven cofolding model development stream, drive product requirements to build out the next generation of cofolding collaborations. You should apply if You have deep experience building and training contemporary models in production, at scale (e.g. AlphaFold, OpenFold, Boltz) and are familiar with modern MLOps tooling. You have experience applying ML to real-world protein structure or drug discovery problems. You are comfortable working in a fast-paced startup environment and enjoy on customer-driven projects. You understand the technical challenges of structural biology and can design scalable data preprocessing, training, and evaluation workflows. Nice to have You have experience in federated learning, privacy-preserving ML, or privacy-preserving model training. You’ve published in ML or biology journals/conferences (e.g., NeurIPS, ICML, Nature Methods, Bioinformatics). What we offer you Industry-competitive compensation, incl. early-stage virtual share options Remote-first working – work where you work best, whether from home or a co-working space near you Great suite of benefits, including a wellbeing budget, mental health benefits, a work-from-home budget, a co-working stipend and a learning and development budget Regular team lunches and social events Generous holiday allowance Quarterly All Hands meet-up at our Berlin HQ or a different European location A fun, diverse team of mission-driven individuals with a drive to see AI and ML used for good Plenty of room to grow personally and professionally and shape your own role Logistics Our interview process is split into three phases: Initial Screening: If your application matches our requirements, we invite you to an initial video call to explore the fit. In this 30-45 minutes interview, you will get to know us and the role. The interviewer will be interested in your relevant experiences and skills, as well as answer any question on the company and the role itself that you may have. Deep Dive: In this phase, a domain expert from our team will assess your skills and knowledge required for the role by asking you about meaningful experiences or your solutions for specific scenarios in line with the role we are staffing. Final Interview: Finally, we invite you for up to three hours of targeted sessions with our founders, talking about our culture and meeting future co-workers on the ground.

AlphaFold 3-style cofolding models predict biomolecular complexes with high structural accuracy, yet a large fraction of their outputs are physically invalid: chains overlap at interfaces, ligand bond lengths and angles are distorted, rings are non-planar, and stereocentres are inverted. Current ap…

The recently released Boltz-2 cofolding model is generating high expectations by enabling both protein–ligand structure and binding affinity predictions. When applied to a recently described and chall...

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Nature Structural & Molecular Biology, Published online: 08 May 2026; doi:10.1038/s41594-026-01797-5This work introduces the Runs N’ Poses dataset for benchmarking deep learning methods on the protein–ligand complex prediction task. It shows that current methods rely on memorization, challenging their effectiveness for real-world applications such as drug discovery.

Nature Structural & Molecular Biology, Published online: 08 May 2026; doi:10.1038/s41594-026-01797-5This work introduces the Runs N’ Poses dataset for benchmarking deep learning methods on the protein–ligand complex prediction task. It shows that current methods rely on memorization, challenging their effectiveness for real-world applications such as drug discovery.

<p>Recorded: 16/4/2026</p> <p>Dr. Janani Durairaj (Biozentrum, University of Basel, Basel, Switzerland)</p> <p>Abstract: Deep learning has driven major breakthroughs in protein structure prediction, however the next critical advance is accurately predicting how proteins interact with small molecule ligands, to enable real-world applications such as drug discovery. Recent cofolding methods aim to address this challenge, but evaluating their performance has been inconclusive due to the lack o...