While AI models are effective for developability properties like stability, they fall short on predicting function. Sanofi's Norbert Furtman notes that generalized affinity prediction is a 'holy grail' problem, and predicting interference with a biological pathway is even harder, as function is not solely explained by structure.

Public datasets primarily show successful protein interactions, starving AI models of crucial "negative data"—plausible but incorrect interactions. A-Alpha Bio finds that providing this data on what fails is just as important for training predictive and generalizable models.

The explosion of AI-generated molecular candidates creates a new problem: physically testing them at scale. A significant portion of R&D effort should be redirected from AI model development to inventing novel, high-throughput instrumentation and assays to experimentally validate the flood of predictions AI produces.

A major cause of clinical trial failure is unforeseen toxicity. By creating AI-powered models based on single-cell atlases, researchers can predict which unintended cells express a drug's target receptor. This allows them to anticipate side effects, like kidney toxicity, in silico, saving billions in failed drug development.

While AI excels at screening vast compound libraries for potential drug candidates, it cannot overcome the ultimate bottleneck: the messy, complex, and poorly documented reality of human biology. The need for physical clinical trials remains the fundamental constraint on medical progress.

The bottleneck for AI in drug discovery is not the algorithm but the lack of high-quality, large-scale biological data. New platforms are needed to generate this necessary "substrate" for AI models to learn from, challenging the narrative that better models alone are the solution.

While AI promises to design therapeutics computationally, it doesn't eliminate the need for physical lab work. Even if future models require no training data, their predicted outputs must be experimentally validated. This ensures a continuous, inescapable cycle where high-throughput data generation remains critical for progress.

Despite the buzz, a clinical development expert cautions that AI's impact in drug development is limited. The primary bottleneck isn't the algorithms but the lack of sufficient, high-quality human biological data that can be translated into reliable predictions, as animal models often fail to provide it.

The bottleneck for AI in drug development isn't the sophistication of the models but the absence of large-scale, high-quality biological data sets. Without comprehensive data on how drugs interact within complex human systems, even the best AI models cannot make accurate predictions.