Long-form guides, explainers, and operating playbooks on how AI is being applied across drug discovery, development, manufacturing, and the clinic — from the Allbio.AI team and the life sciences community.
Articles here are analysis, guides, and opinion written by community practitioners and the Allbio.AI team — how to think, decide, staff, buy, govern, or measure across workflows. A single workflow described end to end belongs in Use Cases. Vendor/service provider-authored articles and case studies about their own product or service are commercial content and live with that listing in Products & Services.
Four patterns food and beverage teams actually run: model-guided ingredient and formulation development with sensory prediction, shelf-life and spoilage modeling, inline vision for quality and yield, and surveillance and traceback in food-safety programs.
Generative AI expands antibody design, yet loop flexibility and developability liabilities remain key failure points. Discovery teams must integrate computational screening with rigorous wet-lab testing to validate leads.
A practical AI policy for life science R&D must balance data security with scientific velocity. Here is how operating leaders can structure clear, lightweight guidelines that fit real research workflows.
A practical walkthrough of how artificial intelligence and machine learning are being deployed across the modern drug development lifecycle — from pre-clinical and CMC through clinical trials and regulatory submission.
Recursion describes MolPhenix, a foundation model trained on its phenomics image data to predict how a molecule will change cell morphology, including for molecules not seen during training.
DeepMind and EMBL-EBI expanded the AlphaFold Protein Structure Database to cover nearly all catalogued proteins — around 200 million predicted structures — making structure prediction a routine starting point for research teams.
Generative AI and natural language processing offer significant efficiency gains in pharmacovigilance intake and literature triage. However, clinical accountability and safety database oversight must remain strictly under human control.
Targeted machine learning models offer bioprocess development teams immediate savings by reducing physical experimental runs and providing real-time quality monitoring. Successful deployment requires modest model architectures and early quality governance.
Using large language models in regulatory drafting can significantly shorten authoring cycles for submission dossiers. However, generated text remains a draft artefact, requiring strict source traceability and human oversight to maintain submission integrity.
A practical rubric — grounded in questions vendors don't want to answer — for buyers in discovery, CMC, and clinical operations.
Algorithmic feasibility tools promise to replace unreliable site surveys with data-driven enrolment forecasts and amendment predictions. Operating teams must look past vendor claims to evaluate underlying biases, temporal leakage, and equity constraints.
Most AI failures in biopharma stem from fragmented data systems and missing metadata rather than flawed models. A structured, decision-focused approach to data readiness ensures infrastructure investments deliver practical value.
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