Quantifying sources of uncertainty in drug discovery predictions with probabilistic models

TL;DR

This paper emphasizes the importance of quantifying uncertainty in drug discovery predictions using probabilistic models, which improve decision-making and risk communication over traditional single-estimate models.

Contribution

It introduces the application of probabilistic predictive models to drug discovery, demonstrating how they incorporate uncertainty and enhance prediction reliability.

Findings

PPMs provide a distribution of predicted values, capturing uncertainty.

Ignoring uncertainty can lead to over-confident and risky decisions.

PPMs are compatible with existing machine learning workflows.

Abstract

Knowing the uncertainty in a prediction is critical when making expensive investment decisions and when patient safety is paramount, but machine learning (ML) models in drug discovery typically provide only a single best estimate and ignore all sources of uncertainty. Predictions from these models may therefore be over-confident, which can put patients at risk and waste resources when compounds that are destined to fail are further developed. Probabilistic predictive models (PPMs) can incorporate uncertainty in both the data and model, and return a distribution of predicted values that represents the uncertainty in the prediction. PPMs not only let users know when predictions are uncertain, but the intuitive output from these models makes communicating risk easier and decision making better. Many popular machine learning methods have a PPM or Bayesian analogue, making PPMs easy to fit into current workflows. We use toxicity prediction as a running example, but the same principles apply for all prediction models used in drug discovery. The consequences of ignoring uncertainty and how PPMs account for uncertainty are also described. We aim to make the discussion accessible to a broad non-mathematical audience. Equations are provided to make ideas concrete for mathematical readers (but can be skipped without loss of understanding) and code is available for computational researchers (https://github.com/stanlazic/ML_uncertainty_quantification).