  Introduction: Computational modeling has rapidly advanced over the last
decades, especially to predict molecular properties for chemistry, material
science and drug design. Recently, machine learning techniques have emerged as
a powerful and cost-effective strategy to learn from existing datasets and
perform predictions on unseen molecules. Accordingly, the explosive rise of
data-driven techniques raises an important question: What confidence can be
assigned to molecular property predictions and what techniques can be used for
that purpose?
  Areas covered: In this work, we discuss popular strategies for predicting
molecular properties relevant to drug design, their corresponding uncertainty
sources and methods to quantify uncertainty and confidence. First, our
considerations for assessing confidence begin with dataset bias and size,
data-driven property prediction and feature design. Next, we discuss property
simulation via molecular docking, and free-energy simulations of binding
affinity in detail. Lastly, we investigate how these uncertainties propagate to
generative models, as they are usually coupled with property predictors.
  Expert opinion: Computational techniques are paramount to reduce the
prohibitive cost and timing of brute-force experimentation when exploring the
enormous chemical space. We believe that assessing uncertainty in property
prediction models is essential whenever closed-loop drug design campaigns
relying on high-throughput virtual screening are deployed. Accordingly,
considering sources of uncertainty leads to better-informed experimental
validations, more reliable predictions and to more realistic expectations of
the entire workflow. Overall, this increases confidence in the predictions and
designs and, ultimately, accelerates drug design.
