Additional file 4. The ethics approval letter

Anne Osbourn (376776)

John Mitchell (3314499)

Samuel Boobier (4540012)

Journal of Cheminformatics

Additional file 2. The names and structures of the 25 compounds in the test set, and a literature source for each solubility value

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MOESM2 of Can human experts predict solubility better than computers?

Abstract 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 figures on various statistical measures, the difference between it and the consensus machine learning predictor was both small and statistically insignificant. We conclude that human experts can predict the aqueous solubility of druglike molecules essentially equally well as machine learning algorithms. We find that, for either humans or algorithms, combining individual predictions into a consensus predictor by taking their median is a powerful way of benefitting from the wisdom of crowds

Samuel Boobier

Anne Osbourn

John B. O. Mitchell

Directory of Open Access Journals

Can human experts predict solubility better than computers?

MOESM4 of Can human experts predict solubility better than computers?

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 figures on various statistical measures, the difference between it and the consensus machine learning predictor was both small and statistically insignificant. We conclude that human experts can predict the aqueous solubility of druglike molecules essentially equally well as machine learning algorithms. We find that, for either humans or algorithms, combining individual predictions into a consensus predictor by taking their median is a powerful way of benefitting from the wisdom of crowds

Boobier, Samuel

Osbourn, Anne

Mitchell, John B. O.

University of St. Andrews - Pure

Additional file 8. The human expertsâ solubility predictions

MOESM8 of Can human experts predict solubility better than computers?

Additional file 7. Ten python scripts implementing the different machine learning algorithms

MOESM7 of Can human experts predict solubility better than computers?

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Additional file 3. The name, structure and solubility data in electronic form for both training and test sets, including SMILES representations of the chemical structures. This DLS-100 dataset may be reused freely with appropriate citation of our work, no further permission is required. It is also available from the University of St Andrews research portal [14] with https://doi.org/10.17630/3a3a5abc-8458-4924-8e6c-b804347605e8 and from figshare [15] with https://doi.org/10.6084/m9.figshare.5545639 

MOESM3 of Can human experts predict solubility better than computers?

https://doi.org/10.1186/s13321-017-0250-y

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