In this study, we design and carry out a survey, asking human experts to predict the aqueous solubility of druglike
organic compounds. We investigate whether these experts, drawn largely from the pharmaceutical industry and
academia, can match or exceed the predictive power of algorithms. Alongside this, we implement 10 typical machine
learning algorithms on the same dataset. The best algorithm, a variety of neural network known as a multi-layer
perceptron, gave an RMSE of 0.985 log S units and an R2 of 0.706. We would not have predicted the relative success of
this particular algorithm in advance. We found that the best individual human predictor generated an almost identical prediction quality with an RMSE of 0.942 log S units and an R2 of 0.723. The collection of algorithms contained
a higher proportion of reasonably good predictors, nine out of ten compared with around half of the humans. We
found that, for either humans or algorithms, combining individual predictions into a consensus predictor by taking
their median generated excellent predictivity. While our consensus human predictor achieved very slightly better
headline fgures on various statistical measures, the diference between it and the consensus machine learning predictor was both small and statistically insignifcant. We conclude that human experts can predict the aqueous solubility of druglike molecules essentially equally well as machine learning algorithms. We fnd that, for either humans or
algorithms, combining individual predictions into a consensus predictor by taking their median is a powerful way of
beneftting from the wisdom of crowds.

• Can human experts predict solubility better than computers?