Yinyin Wang

TL;DR: It was shown that linear regression approaches, when considering chemical fingerprints as predictors, have the potential to achieve high accuracy of predicting the sensitivity of drug combinations and are freely available in DrugComb.

TL;DR: In this article, a network-based method was proposed to quantify the interactions in herb pairs by retrieving the associated ingredients and protein targets, and determined multiple networkbased distances including the closest, shortest, center, kernel, and separation, both at the ingredient and at the target levels.

TL;DR: DrugComb as mentioned in this paper is a web-based portal for the deposition and analysis of drug combination screening datasets, including manual curation and harmonization of more comprehensive drug combination and monotherapy screening data, not only for cancers but also for other diseases such as malaria and COVID-19.

TL;DR: DrugComb as discussed by the authors is a web-based portal for the deposition and analysis of drug combination screening datasets, including manual curation and harmonization of more comprehensive drug combination and monotherapy screening data, not only for cancers but also for other diseases such as malaria and COVID-19.

TL;DR: The molecule features for 646 herbs and their active components including structure-based fingerprints and ADME properties (absorption, distribution, metabolism and excretion), and found that the Meridian can be predicted by machine learning approaches with a top accuracy of 0.83.