Position: AI for Drug Discovery Models Often Do Not Learn as Expected and How to Diagnose These Failure Modes

Nikhil Branson, Aaron Wenteler, Guy Durant, Charlotte Deane

Keywords: AIDD, MLDD, AI for Drug Discovery

Abstract: We argue that in multiple areas of AI for drug discovery (AIDD), deep learning models are not learning the meaningful biological or chemical features they were hypothesised to capture, but instead learn non-generalisable features, for example, from dataset biases. To address this, we propose the systematic use of misaligned baselines. We define misaligned baselines as models that rely on signals purposely misaligned with the intended learning objective. Rather than modelling the underlying biology or chemistry directly, these baselines draw on reductionist sources of information, heavily perturbed input features, or heuristic models. Competitive performance of such baselines reveals when models are not learning the biologically meaningful representations they were designed to learn. By examining case studies across multiple branches of AIDD, we demonstrate that misaligned baselines have consistently exposed such failure modes and crucially informed the evaluation and improvement of these models. We argue that adopting them as standard practice will help to ensure that progress in AIDD reflects genuine advances rather than artefacts of data-generating processes or evaluation design.

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Submission Number: 104