Models need complete, quality data

As AI has accelerated demand for data-rich lab systems, it has also highlighted a fundamental need for better, more complete data. Many earlier AI models were trained on publicly available datasets and are now hitting what Belcher calls a data wall. Because models have access to the same data, they all reach similar conclusions, with diminishing returns over time. Additionally, the datasets weren’t built with AI in mind, meaning they lack the structure, labeling, and diversity needed to keep models accurate and free of bias. Publication bias reinforces the problem.

“Most publicly available datasets and scientific publications focus exclusively on positive results,” says Belcher. “No one wants to share their failures. This bias is almost like having one hand tied behind your back. AI models can identify patterns associated with success, but they lack the comprehensive understanding of failures that would make predictions more reliable. ” The data Belcher believes would markedly improve models—the failed experiments, the compounds that don’t bind—remains frustratingly difficult to come by.

“We often joke that there should be a journal of negative data,” he says. “It’s often buried in lab notebooks, and it’s never used to inform or guide future research. ” This lack of negative data creates a fundamental problem: Without access to a broad range of data, models can’t be adequately trained to avoid bias. “In all machine learning applications, the model’s performance relies heavily on the quality and scope of the training data,” notes Belcher. Fabrication has also become much easier with AI, compounding concerns around data integrity.

Take Western blots, for example. These are part of a standard technique for identifying proteins in blood or tissue samples, and they are among the most common targets for manipulation in biomedical research.