A perfect prediction means little if it is unclear to you why it works. AI has reshaped drug discovery, but many of these models operate as “black boxes”: they deliver outputs without showing their reasoning. In high-stakes biomedical research, and especially in regulated environments, that is a problem.
What Is Explainable AI (XAI) And How Does It Differ From Traditional “Black Box” Models
In AI-driven drug discovery, not all models are created equal when it comes to transparency. Explainable AI (XAI) models are built with methods that make their inner workings more transparent [1]. They can explain why they made a specific prediction or recommendation, which helps scientists validate results, detect potential biases, and build trust in the system.
In contrast, black-box models can achieve outstanding accuracy, but their decision-making process is hidden from view. They deliver answers without showing the reasoning behind them, much like getting a lab result with no explanation of how it was obtained [1].
In drug discovery, this difference matters: while black-box models can spot complex patterns in data, XAI models help ensure those patterns are scientifically sound and ethically defensible.
Why Is Interpretability Critical In Regulated Environments
In highly regulated environments such as FDA or EMA submissions, explainability is not a “nice to have” – it’s a prerequisite for acceptance. Regulatory agencies expect AI-driven decisions to be transparent, auditable, and scientifically justified. Suppose a model flags a compound as high-risk. In that case, reviewers must understand the reasoning in terms they recognize, such as mechanism of action, toxicity pathways, or target interactions – not just a probability score [2].
Explainable AI (XAI) techniques, such as SHAP or LIME, make it possible to trace predictions back to specific data features and biological rationale. This level of interpretability supports compliance with data integrity principles, ensures alignment with domain knowledge, and enables thorough audits. It also reduces the risks associated with “black-box” models, whose predictions may be accurate but cannot be defended in front of a regulatory panel [1, 2].
Moreover, explainability strengthens the scientific narrative behind a submission: it connects algorithm outputs to established pharmacological principles, making it easier to justify label claims, risk assessments, and trial decisions. In post-market surveillance, interpretable models facilitate tracing safety signals back to their origin, supporting ongoing patient safety [1, 2].
In short, interpretability is essential in FDA and EMA submissions because it:
- Enables regulatory reviewers to understand and trust model decisions for safety and efficacy evaluation.
- Meets legal transparency, auditability, and data integrity requirements.
- Supports the scientific justification needed for regulatory acceptance and label claims.
Helps balance model accuracy with transparency, especially in high-stakes healthcare environments [2].
💡 Black Box vs. Glass Box: A Regulatory Scenario
Imagine two AI models assessing the same new oncology compound for FDA submission.
- Black Box: The model outputs “High cardiac risk” – but can’t explain why. The review stalls. Regulators request additional animal studies, delaying the trial by months.
- Glass Box (Explainable AI): The model also predicts “High cardiac risk”, but backs it up with clear evidence: elevated QT interval in preclinical ECG data, structural similarity to known cardiotoxic agents, and ion channel binding patterns from in vitro assays. Regulatory reviewers can trace each step, confirm it aligns with known pharmacology, and approve a targeted cardiac safety monitoring plan instead of halting the trial.
The difference? One output is a verdict without context; the other is a defensible, data-backed argument – exactly what regulators need to make safe, timely decisions.
Use Cases Where Explainability Builds Trust: Biomarker Discovery, Patient Stratification, MoA Hypotheses
In regulated and clinical settings, explainability turns AI from a “black box” into a trusted partner by showing exactly how conclusions are reached. This transparency is especially critical in the areas of biomarker discovery, patient stratification and mechanism of action (MoA) hypotheses.
Explainable AI can highlight the specific patterns and features in complex biological datasets that lead to a biomarker’s identification. By revealing why a signal was flagged, researchers can validate whether the finding is biologically relevant and clinically meaningful. This increases confidence among scientists and accelerates adoption in the clinic.
When AI models group patients for diagnosis, prognosis, or targeted therapies (patient stratification process), local explainability shows which clinical features drove the classification. Physicians can see, for example, that a patient was placed in a high-risk group based on lab results, imaging patterns, and specific comorbidities. This alignment with physicians’ decision-making processes helps build trust in the AI’s recommendations and supports shared decision-making in healthcare.
In early-stage R&D, explainability helps researchers explore how a compound achieves its effect. By mapping cause–and–effect relationships, such as linking phenotypic changes to molecular targets or pathway alterations (mechanism of action), explainable models provide hypotheses that are both plausible and grounded in scientific knowledge. These insights guide experimental validation and ensure the AI’s reasoning complements, rather than contradicts, established biology.
Use Case | How Explainability is Applied | Trust Benefit |
|---|---|---|
Biomarker Discovery | Highlights specific patterns (e.g., gene expression, protein levels, imaging features) that lead to biomarker identification. | Enables scientific validation, increases clinical adoption, and ensures relevance to disease biology. |
Patient Stratification | Shows which clinical features (e.g., lab results, imaging findings, comorbidities) drove patient grouping. | Aligns with physicians’ reasoning, supports clear patient communication, and fosters clinical confidence. |
Mechanism of Action (MoA) Hypotheses | Maps cause–and–effect relationships between molecular targets, pathways, and phenotypic outcomes | Generates biologically plausible hypotheses, guiding experimental validation and aligning with known science. |
Tab. 1. Three areas of AI Explainability application in drug discovery and its benefits.
Techniques For Model Interpretation
The first generation of explainability techniques in AI focused on feature attribution and attention maps. Methods like basic saliency maps or gradient visualizations highlighted which input variables or image regions influenced a prediction. While these approaches made black-box models less opaque, they often provided only descriptive “heatmaps” with limited actionable value for scientists or clinicians.
Today, explainability has moved far beyond static visual cues. More advanced approaches, such as Shapley Additive Explanations (SHAP), use cooperative game theory to quantify each feature’s contribution to a prediction across many possible input combinations. This transforms explainability from a “nice picture” into a tool for hypothesis generation, biomarker prioritization, and patient stratification strategies backed by rigorous statistics.
We also see the rise of concept-level explanations, where techniques like Concept Activation Vectors (CAVs) link internal model representations to human-understandable biological concepts (e.g., pathway activation, molecular motifs). Similarly, prototypical parts models identify representative “cases” the model uses for comparison, enabling researchers to trace a prediction back to a meaningful biological pattern or known clinical case.
This evolution means explainability is no longer just a transparency layer added at the end of model training. In drug discovery, that shift allows researchers to see what the model is focusing on, test and validate whether those focal points are mechanistically relevant. This leads to faster and more confident decision-making in preclinical and clinical research.
How Ardigen approaches the trade-off between model performance and interpretability
We recognize that high predictive accuracy alone is not enough. Models must also provide insights scientists can trust. To achieve this, the team integrates interpretability into the model development process rather than treating it as an afterthought.
In chemistry-focused projects, predictive models (including deep learning and R-MAT algorithms) are trained with cluster-based data splitting. This prevents the model from “memorizing” specific chemotypes and ensures it learns features that generalize to unseen structures, which is critical for discovering truly novel compounds. Once trained, a custom interpretability pipeline extracts the molecular substructures most responsible for prediction differences, enabling chemists to connect these patterns to underlying experimental observations.
In proteomics, Ardigen applies multi-task learning (MTL) to model multiple disease indications jointly. This architecture boosts statistical power by sharing information across conditions while retaining disease-specific layers for precision. The resulting low-dimensional embeddings are interrogated with SHAP values to quantify the influence of each protein feature. These biologically grounded signatures support downstream tasks like disease subtyping, biomarker prioritization, and mechanistic hypothesis generation.
By deliberately accepting small trade-offs in raw accuracy to preserve interpretability, Ardigen delivers models that perform well and produce actionable, mechanistically relevant insights.
To sum up
Rather than accepting an “accuracy vs interpretability” dilemma, we engineer both:
- When accuracy gains are real and stable, we keep the complex model but insist on faithful, testable explanations (SHAP for features; prototypes/concepts for human reasoning).
- When complexity adds opacity without utility, we step down to simpler, inherently interpretable models, especially for narrow decisions or where post‑hoc methods would strain credibility.
- Across settings, cluster‑split evaluation is our guardrail: if a method can’t generalize under that regime, we don’t ship it, no matter how high the random‑split AUC.
By integrating explainability into every stage – from model design to reporting – we help partners move from “the model works” to “we understand why it works”. That understanding is what turns AI predictions into actionable science.
Want to know more? Book a meeting with our experts.
Author: Martyna Piotrowska |
Technical editing: Ardigen expert Dawid Rymarczyk, Phd
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