Full papers
Up to 5 pages presenting complete research; page limits exclude references and appendices.
Machine Learning/CORE Unranked
NeurIPS2026-AI4DD 2026
NeurIPS 2026 Workshop on AI for Drug Discovery: Bridging the Translation Gap
The AI4DD 2026 workshop at NeurIPS focuses on bridging the translation gap between AI performance on benchmarks and real-world impact in drug discovery. It brings together machine learning researchers with computational chemists, biologists, and industry practitioners to advance robust, trustworthy, and experimentally actionable AI for therapeutic development.
Paper fit
Contribution paths
A strong submission should clearly identify its contribution and evaluate it appropriately.
Short papers
Up to 2 pages presenting preliminary results, well-supported perspectives, or promising new research directions; not expected to include complete experimental results but must articulate motivation, key insights, and potential impact; page limits exclude references and appendices.
Research areas in scope
01
Scope & Topics
From Benchmarks to Closed-Loop Translation: realistic benchmark design, external and prospective validation, retrospective–prospective discrepancies, negative and inconclusive results, failure analysis, evidence standards, reproducibility, and integration into design–make–test–learn workflowsLearning under Scarce, Biased, and Shifting Data: few-shot, zero-shot, transfer, active, and physics-informed learning; noisy or censored assays; missing modalities; cross-laboratory and cross-target distribution shift; adaptation from public datasets to real discovery campaignsScientific Reasoning and AI Co-Scientists: grounded hypothesis generation, experiment planning, multimodal reasoning, tool use, provenance, verification, failure recovery, and prospective evaluation of long-horizon scientific workflowsTrustworthy and Decision-Aware AI: uncertainty quantification, calibration, conformal and selective prediction, out-of-distribution detection, causal and mechanistic interpretation, abstention, decision-aware metricsMulti-Objective and Resource-Constrained Discovery: cost-aware optimization and experimental value of information under conflicting objectives such as potency, selectivity, toxicity, synthesizability, pharmacokinetics, developability, and limited wet-lab budgets
Policies worth checking twice
- All submissions undergo double-blind review through OpenReview
- Page limits exclude references and appendices
- Concurrent submissions are allowed
- The workshop is non-archival and does not publish proceedings
- Authors must use the NeurIPS 2026 LaTeX template and change the footnote to 'Submitted to in the AI for Drug Discovery workshop (NeurIPS 2026).'
- The NeurIPS checklist is not required
Official sources
Compiled from the official call for papers. The organizers’ pages remain authoritative.
Last verified September 9, 2026