Artificial intelligence is becoming more visible in pharmaceutical formulation development, particularly for predicting performance, screening formulation options and identifying useful next experiments.
Recent reviews describe machine learning as increasingly relevant to formulation design, optimization and drug-delivery development, especially where composition, process variables and product performance interact in complex ways. A September 2026 review in the European Journal of Pharmaceutics and Biopharmaceutics highlights formulation complexity, optimization efficiency and controlled drug release as key areas where AI may support development.
The opportunity is clear.
The complication is that a tablet, oral liquid and injectable product do not fail for the same reasons.
That means an AI model cannot simply be given “formulation data” and expected to understand which result actually matters.
The Formulation Objective Changes With the Dosage Form
Consider an immediate-release tablet.
A development team may be balancing compression behavior, hardness, friability, disintegration, dissolution and manufacturability. Increasing binder level may improve mechanical strength while slowing disintegration. Higher lubricant concentration may help processing while reducing wettability or dissolution performance.
The optimization problem is already multi-objective.
Now compare that with an oral liquid.
The formulation scientist is thinking about solubility, precipitation risk, pH, preservative effectiveness, dose volume, viscosity, taste and stability. A composition that appears acceptable immediately after preparation may become unacceptable after dilution, storage or repeated opening.
An injectable formulation introduces a different set of constraints again. Composition, pH, osmolality, particulate risk, compatibility, precipitation, stability and administration conditions can become critical.
The same machine-learning method may technically be usable across all three.
The inputs, constraints and definition of success cannot be the same.
That is where formulation knowledge becomes essential.
A High Model Score Is Not the Same as a Useful Formulation
One of the biggest risks in AI-supported formulation development is placing too much emphasis on predictive accuracy.
A model may perform well statistically while still recommending a scientifically weak candidate.
This can happen when the dataset contains only a narrow range of compositions, when experiments from related batches leak across training and validation sets, or when important material and process variables have not been captured.
The model may also identify a candidate that optimizes one response while violating another product requirement.
For a tablet, predicted dissolution might improve while compression performance becomes unacceptable. For an oral liquid, solubility may increase while chemical stability deteriorates. For an injectable, the predicted composition may appear favorable until precipitation or compatibility constraints are considered.
Recent literature on AI-guided drug delivery repeatedly identifies data scarcity, interpretability and experimentally constrained optimization as major challenges.
For formulation teams, that means model evaluation should begin with a different question:
Does this recommendation make pharmaceutical sense?
Existing Formulation Data Can Be More Useful Than It Looks
Many pharmaceutical R&D groups already have years of formulation data, but those records were not created for machine learning.
Some batches may contain complete material and process information. Others may record only the final composition and a few test results. Raw-material grades may have changed during development. Certain formulations may have been tested for dissolution but not stability, while others contain process-history information that was never standardized.
This does not automatically make the dataset unusable.
It means the data need to be structured around the development question.
A 2026 study describing a structured oral-formulation database for machine learning showed how formulation records can be organized around drug properties, release objectives and functional excipient roles to support data-informed development.
For industrial teams, this is often the first practical AI task: not building the most advanced model, but deciding which historical variables actually carry formulation meaning.
AI Becomes More Valuable When It Helps Choose the Next Experiment
Predicting a response is useful.
Deciding what to test next may be even more valuable.
If a model identifies three promising candidates but uncertainty remains high in one region of the formulation space, the best next experiment may not be the formulation with the highest predicted score. It may be the experiment that reduces uncertainty or clarifies a trade-off between competing product attributes.
This is where approaches such as Bayesian optimization, active learning and predictive modeling become especially relevant.
The 2026 literature on AI-guided drug delivery increasingly emphasizes adaptive optimization, multi-objective decision-making and efficient use of experimental resources rather than replacing experiments altogether.
For formulation scientists, this is an important distinction.
AI should not eliminate experimental confirmation.
It should help make the next experiment more purposeful.
The Same Algorithm Needs Different Pharmaceutical Reasoning
This is perhaps the most important point.
A machine-learning algorithm does not inherently understand that a tablet must survive compression and still dissolve appropriately. It does not know that a clear oral liquid can later precipitate after dilution. It does not automatically recognize that an injectable composition may be unacceptable because of particulate or compatibility risk.
Those constraints have to be represented in the problem.
That is why AI-supported pharmaceutical formulation is unlikely to become a purely data-science exercise.
The strongest workflows will combine formulation science, experimental design and predictive modeling so that the model explores only scientifically meaningful regions and recommendations are checked against product-specific requirements.
The Real Value Starts After the Prediction
The OnlyTRAININGS advanced session AI for Pharmaceutical Formulation Development: Solid Oral, Oral Liquids and Injectables focuses on these practical decisions.
The training examines how formulation data should be structured, how AI and Design of Experiments can work together, how multiple product attributes can be evaluated simultaneously and how model recommendations should be tested before laboratory resources are committed. It uses separate solid oral, oral liquid and injectable examples precisely because the same modeling method needs different inputs, constraints and success criteria across dosage forms.
For formulation teams already exploring AI, the most useful question may no longer be:
“Can we predict formulation performance?”
It is:
“Can we recognize when the prediction is useful enough to change the next experiment?”
Explore the AI for Pharmaceutical Formulation Development Training:
https://www.onlytrainings.com/course/ai-pharmaceutical-formulation-development-solid-oral-liquids-injectables/
AI in pharmaceutical formulation, pharmaceutical formulation development, machine learning drug formulation, AI formulation optimization, tablet formulation AI, oral liquid formulation AI, injectable formulation AI, pharmaceutical R&D AI, formulation prediction, formulation optimization, Bayesian optimization pharma, Design of Experiments pharma, AI drug product development, pharmaceutical machine learning, AI formulation development
