One of the key requirements for incorporating machine learning into the drug discovery process is complete reproducibility and traceability of the model building and evaluation process. With this in mind, we have developed an end-to-end modular and extensible software pipeline for building and sharing machine learning models that predict key pharma-relevant parameters. The ATOM Modeling PipeLine, or AMPL, extends the functionality of the open source library DeepChem and supports an array of machine learning and molecular featurization tools. We have benchmarked AMPL on a large collection of pharmaceutical datasets covering a wide range of parameters. Our key findings include: Physicochemical descriptors and deep learning-based graph representations are significantly better than traditional fingerprints to characterize molecular featuresDataset size is directly correlated to performance of prediction : single-task deep learning models only outperform shallow learners if there is enough data. Likewise, data set size has a direct impact of model predictivityindependently of comprehensive hyperparameter model tuning. Our findings point to the need for public dataset integration or multi-task/transfer learning approaches. DeepChem uncertainty quantification (UQ) analysis may help identify model error; however, efficacy of UQ to filter predictions varies considerably between datasets and  model types. This software is open source and available for download at URL
One of the key requirements for incorporating machine learning into the drug discovery process is complete reproducibility and traceability of the model building and evaluation process. With this in mind, we have developed an end-to-end modular and extensible software pipeline for building and sharing machine learning models that predict key pharma-relevant parameters. The ATOM Modeling PipeLine, or AMPL, extends the functionality of the open source library DeepChem and supports an array of machine learning and molecular featurization tools. We have benchmarked AMPL on a large collection of pharmaceutical datasets covering a wide range of parameters. As a result of these comprehensive experiments, we have found that physicochemical descriptors and deep learning-based graph representations significantly outperform traditional fingerprints in the characterization of molecular features. We have also found that dataset size is directly correlated to prediction performance, and that single-task deep learning models only outperform shallow learners if there is sufficient data. Likewise, dataset size has a direct impact on model predictivity, independent of comprehensive hyperparameter model tuning. Our findings point to the need for public dataset integration or multi-task/transfer learning approaches. Lastly, we found that uncertainty quantification (UQ) analysis may help identify model error; however, efficacy of UQ to filter predictions varies considerably between datasets and featurization/ model types. AMPL is open source and available for download at URL
R
Our key findings include: Physicochemical
As a result of these comprehensive experiments, we have found that physicochemical
R
are significantly better than traditional fingerprints to characterize molecular featuresDataset
significantly outperform traditional fingerprints in the characterization of molecular features. We have also found that dataset
R
performance of prediction :
prediction performance, and that
R
enough
sufficient
R
data set
dataset
R
of model predictivityindependently
on model predictivity, independent
R
DeepChem
Lastly, we found that
A
featurization/
R
This software
AMPL