Real deployments and the repeatable workflow patterns behind them — what teams are running in discovery, development, manufacturing, and the clinic, and how each workflow actually hangs together.
Use cases are single workflows, described end to end — either a named deployment at a specific organization, or an anonymized workflow pattern that teams run in practice. Guides, playbooks, and cross-cutting analysis live in Articles. Vendor/service provider-authored case studies about their own product or service are commercial content and live with that listing in Products & Services.
How teams use models across the ADC design space — antibody selection and developability, linker and payload choice, conjugation site prediction, and DAR/stability trade-offs — to narrow candidates before wet-lab work.
How agricultural biotech teams use models to decide which microbial strains, consortia, and breeding candidates earn a slot in a limited field-trial season — and where field variability breaks naive predictions.
Biomanufacturing teams are deploying machine learning to cluster historical QMS records and surface relevant investigation precedents. This pattern overview examines workflow integration, quality unit oversight, and change control strategies.
Machine learning models can detect subtle temperature drift in biomanufacturing cold chains long before static alarm thresholds trigger. This pattern outlines how teams integrate time-series anomaly detection into existing quality management workflows without creating alert fatigue.
Integrating AI into biopharma literature reviews requires systematic search design, explicit extraction schemas, and provenance tracking. Here is how operating teams build defensible evidence workflows that withstand internal and external scrutiny.
A peer-reviewed Nature Medicine study behind Paige's technology describes weakly supervised deep learning on whole-slide images at clinical scale. The authors report performance they characterize as "clinical-grade" and discuss its potential to help prioritize slides for pathologist review. This is a research report, not a statement of approved or cleared clinical use.
A peer-reviewed paper evaluates prognostic digital twins generated by Unlearn.AI models and the effect of covariate adjustment on required sample size in Alzheimer's disease trials.
Insilico Medicine announced Nature Medicine publication of Phase IIa results for rentosertib (ISM001-055), a TNIK inhibitor for idiopathic pulmonary fibrosis whose target and molecule were identified with generative AI. Rentosertib is investigational and is not approved by any regulator; Phase IIa findings are preliminary and do not establish safety or efficacy.
Diagnostic research labs increasingly use machine learning models to surface rare features and reorder manual sample queues. Successful deployments maintain strict boundaries from clinical workflows while actively monitoring sub-population bias and class imbalance.
A generalized pattern for aggregating, normalizing, and searching multi-year electronic lab notebook entries using hybrid retrieval architecture. Learn how informatics teams maintain access boundaries, normalize target nomenclature, and enforce source citation.
Predictive structure models allow early discovery teams to triage target lists by evaluating prospective binding pockets at scale. This composite pattern outlines how teams combine confidence metrics with pocket algorithms while recognizing model limits.
Biopharma sponsors often face submission delays due to terminology drift across protocols, SAPs, CSRs, and ICFs. This composite pattern details how teams use semantic comparison tools to flag discrepancies while keeping human experts in full control.
What AI workflow would you like to see broken down? We research and write the most requested patterns.
