AMPL: A Data-Driven Modeling Pipeline for Drug Discovery.

ABSTRACT: One of the key requirements for incorporating machine learning (ML) into the drug discovery process is complete traceability and reproducibility 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 ML 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 ML and molecular featurization tools. We have benchmarked AMPL on a large collection of pharmaceutical data sets covering a wide range of parameters. Our key findings indicate that traditional molecular fingerprints underperform other feature representation methods. We also find that data set size correlates dir

SUBMITTER: Minnich AJ 

PROVIDER: S-EPMC7189366 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature