Naga, D., Muster, W., Musvasva, E. et al. Off-targetP ML: an open source machine learning framework for off-target panel safety assessment of small molecules. J Cheminform 14, 27 (2022).
DOI
https://doi.org/10.1186/s13321-022-00603-w
Abstract
Unpredicted drug safety issues constitute the majority of failures in the pharmaceutical industry according to several studies. Some of these preclinical safety issues could be attributed to the non-selective binding of compounds to targets other than their intended therapeutic target, causing undesired adverse events. Consequently, pharmaceutical companies routinely run in-vitro safety screens to detect off-target activities prior to preclinical and clinical studies. Hereby we present an open source machine learning framework aiming at the prediction of our in-house 50 off-target panel activities for ~ 4000 compounds, directly from their structure. This framework is intended to guide chemists in the drug design process prior to synthesis and to accelerate drug discovery. We also present a set of ML approaches that require minimum programming experience for deployment. The workflow incorporates different ML approaches such as deep learning and automated machine learning. It also accommodates popular issues faced in bioactivity predictions, as data imbalance, inter-target duplicated measurements and duplicated public compound identifiers. Throughout the workflow development, we explore and compare the capability of Neural Networks and AutoML in constructing prediction models for fifty off-targets of different protein classes, different dataset sizes, and high-class imbalance. Outcomes from different methods are compared in terms of efficiency and efficacy. The most important challenges and factors impacting model construction and performance in addition to suggestions on how to overcome such challenges are also discussed.
Data and code availability
The Off target-ML workflow is freely available for use and download at the Github repository: https://github.com/PharminfoVienna/Off-target-P-ML. The workflow is demonstrated on the public Excape datasets which can be directly downloaded from the following link: https://solr.ideaconsult.net/search/excape/ (compiled Excape datasets and demonstration results are also deposited in the Github folder). Detailed instructions on how to use the workflow and download the required packages is provided. The code required for the AutoML(data preparation and model construction) and Random Forest are also available in the repository. The deep learning models implemented in the workflow can be directly downloaded in the h5 format from the repository.
Funding
This project has received funding from the Innovative Medicines 2 Joint Undertaking under grant agreement No 777365 (“eTRANSAFE”). This Joint Undertaking receives support from the European Union’s Horizon 2020 research and innovation programme and EFPIA companies in-kind contribution.
Rights and Permissions
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
Keywords
Drug discovery, Safety screening, Of-target panel, Class imbalance, Deep learning, Automated machine learning (AutoML), Ensembling methods
