For technology providers, solution partners, and innovators, AIDDD is more than an event. It’s a platform to connect with decision-makers across pharma and biotech, build strategic partnerships, and position your organisation at the centre of the AI revolution transforming pharmaceutical R&D.
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Content tracks for 2027
AIDDD 2027 is built around nine tracks, each written for a specific R&D, data or technology role. Four follow the science from target selection through large and small molecule design to the move into the clinic. One covers how trials are designed and evidenced. The remaining four cover the data, methods, compute and lab capability that make AI work at scale. Choose the track closest to your remit, or move between them to see how the pieces connect.
Target ID / Target Validation
A weak target is the costliest mistake a programme can make, and most clinical failures trace back to it. AI is changing how targets are chosen, with multimodal data and causal biology used together to find targets with stronger evidence behind them. This track is for discovery biologists, genomics scientists and computational teams, and covers validating targets in human-relevant disease models before Phase 2 spend.
Key themes
- Multimodal target discovery
- Causal biology and genomics
- Human-relevant disease models
- Validating targets before Phase 2
Large Molecules
Generative models are impacting how antibodies and proteins are designed, and biologics teams are now expected to deliver binders with the right affinity and specificity and to know early whether they will be developable and safe. This track is for antibody and protein engineers and computational biologists. It covers AI-led design, early prediction of developability and immunogenicity, and formats beyond conventional antibodies.
Key themes
- Generative antibody and protein design
- Affinity and specificity optimisation
- Developability and immunogenicity prediction
- Designing formats beyond antibodies
Small Molecules
AI is moving from generating ideas to sitting at the centre of medicinal chemistry. Medicinal and computational chemists must balance potency, selectivity, ADME and toxicity at once, often before anything has been made. This track covers generative molecular design, multi-parameter lead optimisation, ADME and toxicity prediction to inform what gets synthesised, and where quantum chemistry adds real accuracy to design decisions.
Key themes
- Generative molecular design
- Multi-parameter lead optimisation
- ADME and toxicity prediction
- Quantum chemistry for design
Translational and Preclinical
Translational and preclinical scientists carry the burden of showing that what worked in a model will work in patients, and this is where many programmes lose their way. Computational methods are now shaping how response is predicted and how safety is assessed before a first-in-human study. This track covers predictive biomarkers, human-relevant alternatives to animal testing and in silico safety prediction, with a focus on turning preclinical evidence into confident clinical decisions.
Key themes
- Predictive biomarkers for response
- Alternatives to animal testing
- Bridging preclinical to clinic
- In silico safety prediction
Data Science
Data science and AI leaders are under pressure to move from proofs of concept to methods that scientists and regulators will accept. Agentic AI that plans and runs research tasks is the sharpest example of that shift. This track covers agentic AI in R&D, integrating data across modalities and the emerging role of quantum machine learning, alongside the explainability and governance that earn trust in model outputs.
Key themes
- Agentic AI in R&D
- Multimodal data integration
- Quantum machine learning
- Explainability and model governance
Data Management
AI is only as good as the data underneath it, so the push to deploy AI has made data foundations a front-line problem for data architects and R&D informatics leads. This track covers building AI-ready data foundations, harmonising data from different sources and formats, sharing data securely through federated approaches and running knowledge graphs at enterprise scale.
Key themes
- AI-ready data foundations
- Harmonising heterogeneous data
- Federated, secure data sharing
- Knowledge graphs at scale
AI Platforms, Infrastructure and Scientific Computing
As models grow, so do the demands on compute, and CIO, CTO and scientific computing teams must meet them while keeping enterprise data secure. This track covers hybrid architectures combining AI, HPC and quantum, scaling compute for foundation models, moving platforms from pilot to production and running AI inference securely across the enterprise.
Key themes
- Hybrid AI, HPC and quantum
- Scaling compute for foundation models
- Moving platforms from pilot to production
- Secure enterprise AI inference
Clinical Development
AI is reshaping how trials are planned and run, and the decisions that matter most fall to clinical data scientists and biostatisticians: how a trial is designed, who is recruited and whether the evidence will withstand inspection. This track covers adaptive, data-driven decision science, smarter trial design, faster patient recruitment and deploying AI in a way regulators will accept.
Key themes
- Adaptive, data-driven decision science
- Smarter trial design strategies
- Accelerating patient recruitment
- Inspection-ready AI deployment
Digital Transformation and Lab Automation
AI is turning the lab into a data-generating system in its own right, and lab automation and R&D digital leads are asked to justify the spend while instruments, data and workflows remain disconnected. This track covers self-driving labs, digital twins and robotics, why integrating instruments matters more than the instruments themselves, and how to prove the return on automation investment.
Key themes
- Self-driving labs
- Digital twins and robotics
- Integration over instruments
- Proving automation ROI
“Wonderful opportunity to connect with people on the cutting edge of applying AI to drug development. Great discussions on how to invent the future of medicine, together. Loved the event!”
“Quality over Quantity conference for sure. The people who matter were here, no distractions and a scientific atmosphere.”
“AIDDD brought together a thoughtful mix of perspectives across AI, data, and drug discovery. I appreciated the candid discussions on real-world implementations and the generous sharing of practical experience. The event offered a clear sense of where the field is heading and sparked several ideas I’m bringing back to my team. Overall, a well-curated and genuinely valuable event.”
"The energy in the room was high the entire time, and the discussions felt genuinely relevant to where the industry is right now. Well worth being there."
"A great conference that's very focused on AI Driven Drug Discovery & Development. We had many discussions and got lots of useful information."
"We met with the right set of customers for our offerings. Great for meaningful discussions with key personnel."
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