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. These failures are not random: they stem from systemic challenges in data provenance, model assumptions, feature generation, and evaluation that are well documented in genomics and bioinformatics literature. The stakes for pharmaceutical R&D and regulatory engagement — particularly with agencies such as the U.S. Food and Drug Administration (FDA) and the European Medicines Agency (EMA) — are profound: unreliable models threaten the validity of evidence submitted for regulatory review, compromise translational programs, and undermine patient safety.

Below, we distill common failure modes supported by existing scientific evidence and propose pharma-centric mitigation strategies aligned with regulatory expectations for rigor, reproducibility, and clinical relevance.

1. Data Provenance and Reproducibility Gaps

Effective ML in pathogen genomics depends on consistent, traceable data and reproducible workflows. However, bioinformatics pipelines — particularly in high-throughput settings — often lack rigorous data tracking and provenance management. This results in outputs that cannot be audited, traced to source data, or reproduced months later, even within the same organization. Comprehensive data management, versioning, and auditing mechanisms are foundational to clinical and regulatory acceptance but are frequently absent in research-grade implementations. PMC

Implication for AMR and Drug Discovery: Without clear audit trails, ML models supporting susceptibility inference or compound prioritization cannot meet FDA/EMA standards for analytical validation.

Pharma Mitigation: Mandate integrated data lineage and metadata capture from raw reads to model outputs, and adopt pipeline governance equivalent to what is required for clinical diagnostic workflows.

2. Inappropriate Statistical Assumptions in Biological Contexts

Machine learning models are often designed under assumptions such as identically distributed samples or independence between cases. In genomics, these assumptions frequently fail: biological samples exhibit heterogeneity, population structure, and complex feature dependencies. Reviews highlight that such mismatches systematically distort performance estimates and undermine model generalizability. PubMed

For example, differences in feature distributions across environments (e.g., in vitro vs. clinical isolates) can produce models that perform well internally but fail in the real world. Likewise, genomic loci and linked features violate independence assumptions used in common cross-validation schemes. Front Line Genomics

Implication for Fungal and Viral Models: Models trained on limited strain diversity risk failing on emergent variants or clinical isolates outside training distributions.

Pharma Mitigation: Evaluate and report distributional shifts and adopt cross-validation schemes that reflect true biological independence (e.g., leave-lineage–out validation). Explicitly document dependence structure in data to align with regulatory expectations for statistical rigor.

3. Reference Database Errors and Downstream Bias

ML pipelines in pathogen genomics — especially metagenomic classifiers and taxonomic profilers — depend on reference sequence databases. Yet comprehensive reviews show that these databases suffer pervasive issues including contamination, misannotation, taxonomic errors, and inappropriate inclusion/exclusion criteria. Frontiers

Such errors propagate directly into ML features and labels, leading to misclassified genomic signatures and downstream analytical bias that can masquerade as biological signal.

Implication for AMR and Fungal Genomics: If reference databases mislabel or omit key resistance genes or pathogen variants, ML models may systematically underperform on critical clinical cases.

Pharma Mitigation: Integrate curated, standardized reference sets with clear versioning and quality control. Maintain transparent update policies so that ML models referencing external databases remain verifiable throughout regulatory review.

4. Model Evaluation and Performance Interpretation Errors

Inflated performance metrics are a persistent hazard when evaluation practices are not tailored to genomic reality. Reviews of evaluation practices show that inappropriate metrics — or misinterpretation of common metrics — can produce misleading conclusions about model quality. Frontiers

For instance, internal cross-validation that ignores batch effects or sample relatedness can overestimate accuracy, creating an illusion of robust predictive power that does not hold in independent validation cohorts.

Implication for Regulatory Submissions: Regulatory bodies require comprehensive performance characterization across representative clinical populations. Models that only demonstrate favorable metrics in narrowly defined internal benchmarks risk rejection.

Pharma Mitigation: Align model evaluation with intended use cases. Report sensitivity, specificity, calibration, and external validation on orthogonal datasets. Ensure interpretations respect genomic structure and avoid superficial metric inflation.

5. Black-Box Models and Interpretability Shortfalls

Explainability is central to regulatory confidence and decision support in drug discovery. ML models that function as opaque black boxes — without interpretable evidence for predictions — face increasing scrutiny from agencies such as the FDA, which favors transparent, mechanistically plausible decision support. As broader literature on explainable AI highlights, model interpretability should be integrated into pipeline design, not retrofitted post hoc. arXiv

Implication for Drug Discovery: Black-box predictions of resistance mechanisms or pathogenicity without clear mechanistic support may be insufficient for regulatory claims.

Pharma Mitigation: Implement interpretable models or layer explainability frameworks that link predictions to biologically validated features. Cross-validate these insights with experimental data.

6. Infrastructure and Workflow Fragility

Pipelines often rely on heterogeneous, unstandardized tools assembled without rigorous engineering controls, leading to brittle workflows that break across environments. This is particularly problematic when models trained in research environments are deployed in regulated settings or transferred between labs.

Implication for AMR and Clinical Deployment: Fragile pipelines introduce operational risk that is incompatible with regulated diagnostic workflows.

Pharma Mitigation: Adopt robust software engineering practices — containerization, automated testing, dependency freezing, and reproducible execution environments — that meet software quality standards found in regulated medical device software.

Conclusion

Machine learning holds immense promise for pathogen genomics in pharmaceutical contexts, including AMR profiling, fungal drug target identification, and rapid viral variant prioritization. However, this potential will only be realized when pipelines are evaluated and engineered with biological realism, statistical rigor, and regulatory readiness at their core. The failures documented in foundational genomic pipelines and ML reviews are not esoteric; they represent predictable breakdowns that jeopardize translational success.

Pharmaceutical R&D organizations should treat ML pipelines not as exploratory tools but as regulated instruments requiring governance, auditability, curatable reference inputs, rigorous evaluation, and explainability — all aligned with contemporary expectations from the FDA and EMA. By embedding these principles into ML-augmented genomics workflows, the industry can move beyond superficial performance and towards trustworthy, deployable genomic intelligence that genuinely accelerates drug discovery and improves clinical outcomes.