Building the Data Foundation: Data Engineering Patterns for Molecular and Genomic ML in Pharma
The convergence of chemical and biological data in pharmaceutical ML is accelerating. Foundation models trained simultaneously on molecular structures, protein sequences, and genomic data demand even more rigorous data engineering — consistent molecular representations, reliable cross-modal entity resolution, and auditable provenance across data types that were never designed to interoperate. Organizations that invest in their data foundation now, treating molecular data engineering as infrastructure rather than scripting, will be positioned to adopt these capabilities as they mature.
Ten Essential Practices for Building Sustainable ML Systems in Pharma
In pharmaceutical ML, unlike consumer technology, your systems may need to justify decisions made today for decades to come. A model supporting an IND filing in 2024 may face scrutiny in patent litigation in 2034 or regulatory audit in 2029. Investing in sustainability is not optional—it is professional responsibility.
Developing Customizable Machine Learning Pipelines: A Systems-First Approach to Reliability and Trust
Machine learning models come and go, superseded by improved architectures, better data, or changing business requirements. Pipelines persist. They outlive individual models, span multiple projects, survive personnel changes, and adapt to shifting research questions. When treated as first-class systems rather than incidental scaffolding, pipelines transform from sources of fragility into foundations for sustainable AI capability.
Common Failure Modes in Pathogen Genomics Machine Learning Pipelines: Lessons for AMR, Fungal and Viral Drug Discovery
Machine learning (ML) promises transformative gains in pathogen genomics — from antimicrobial resistance (AMR) prediction to fungal target identification and rapid viral variant characterization. Yet, across research and translational environments, pipelines that integrate high-throughput sequencing with ML models regularly fail to deliver robust, generalizable outcomes.
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