DrugComb: an integrative cancer drug combination data portal

Zagidullin, Bulat; Aldahdooh, Jehad; Zheng, Shuyu; Wang, Wenyu; Wang, Yinyin; Saad, Joseph; Malyutina, Alina; Jafari, Mohieddin; Tanoli, Ziaurrehman; Pessia, Alberto; Tang, Jing

doi:10.1093/nar/gkz337

Cited by 179 publications

(149 citation statements)

References 41 publications

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“…In this study, for the first time, we made use of the DrugComb dataset [20], which contains close to half a million drug combination experiments. This is an invaluable source for training complex models.…”

Section: Discussionmentioning

confidence: 99%

“…We use the synergy scores provided in the DrugComb [20] database. The data were obtained from https://drugcomb.fimm.fi/ (version v1.4, downloaded on Dec 2019).…”

Section: Drug Combination Datasetmentioning

confidence: 99%

“…That is, we binarize the actual synergy scores (y ∈ R) such that those larger than a positive Loewe score threshold, t, constitute the positive class (synergistic) pairs (ȳ = 1). In contrast, those less than −t constitute the negative class (ȳ = 0), where t ∈ (0, 10,20). We evaluate the predictive performance of the models on this binary classification task by the area under the receiver operating characteristics (ROC) curve.…”

Section: Performance Comparisonmentioning

confidence: 99%

“…We show that this approach improves over state-of-the-art performance. Moreover, for the first time in the literature, we utilize the largest drug combination dataset released to this date [20] and benchmark the performances of the state-of-the-art methods on this dataset.…”

Section: Introductionmentioning

confidence: 99%

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MatchMaker: A Deep Learning Framework for Drug Synergy Prediction

Kuru

Taştan

Çiçek

2020

Preprint

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Drug combination therapies have been a viable strategy for the treatment of complex diseases such as cancer due to increased efficacy and reduced side effects. However, experimentally validating all possible combinations for synergistic interaction even with highthroughout screens is intractable due to vast combinatorial search space. Computational techniques can reduce the number of combinations to be evaluated experimentally by prioritizing promising candidates. We present MatchMaker that predicts drug synergy scores using drug chemical structure information and gene expression profiles of cell lines in a deep learning framework. For the first time, our model utilizes the largest known drug combination dataset to date, DrugComb. We compare the performance of MatchMaker with the state-of-the-art models and observe up to ∼ 20% correlation and ∼ 40% mean squared error (MSE) improvements over the next best method. We investigate the cell types and drug pairs that are relatively harder to predict and present novel candidate pairs. MatchMaker is built and available at https://github.com/tastanlab/matchmaker

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Section: Discussionmentioning

confidence: 99%

“…We use the synergy scores provided in the DrugComb [20] database. The data were obtained from https://drugcomb.fimm.fi/ (version v1.4, downloaded on Dec 2019).…”

Section: Drug Combination Datasetmentioning

confidence: 99%

Section: Performance Comparisonmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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MatchMaker: A Deep Learning Framework for Drug Synergy Prediction

Kuru

Taştan

Çiçek

2020

Preprint

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“…Drug sensitivity and synergy. We used a publicly available large-scale drug synergy database from DrugComb Portal, 28 which combines 23 drug synergy studies into one integrated database.…”

Section: Methodsmentioning

confidence: 99%

Anti-cancer Drug Synergy Prediction in Understudied Tissues using Transfer Learning

Kim

Zheng

Tang

et al. 2020

Preprint

Self Cite

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Motivation: Exploring an exponentially increasing yet more promising space, high-throughput combinatorial drug screening has advantages in identifying cancer treatment options with higher efficacy without degradation in terms of safety. A key challenge is that accumulated number of observations in in-vitro drug responses varies greatly among different cancer types, where some tissues (such as bone and prostate) are understudied than the others. Thus, we aim to develop a drug synergy prediction model for understudied data-poor tissues as overcoming data scarcity problem.Results: We collected a comprehensive set of genetic, molecular, phenotypic features for cancer cell lines from six different databases. We developed a drug synergy prediction model based on deep neural networks to integrate multi-modal input and utilize transfer learning from data-rich tissues to data-poor tissues. We showed improved accuracy in predicting drug synergy in understudied tissues without enough drug combination screening data nor after-treatment transcriptome. Our synergy prediction model can be used to rank synergistic drug combinations in understudied tissues and thus help prioritizing future in-vitro experiments.Availability and Implementation: Our algorithm will be publicly available via https://github.com/yejinjkim/drug-synergy-prediction

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ScaffComb: A Phenotype‐Based Framework for Drug Combination Virtual Screening in Large‐Scale Chemical Datasets

Chen

Zeng

et al. 2021

Advanced Science

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Combinational therapy is used for a long time in cancer treatment to overcome drug resistance related to monotherapy. Increased pharmacological data and the rapid development of deep learning methods have enabled the construction of models to predict and screen drug pairs. However, the size of drug libraries is restricted to hundreds to thousands of compounds. The ScaffComb framework, which aims to bridge the gaps in the virtual screening of drug combinations in large‐scale databases, is proposed here. Inspired by phenotype‐based drug design, ScaffComb integrates phenotypic information into molecular scaffolds, which can be used to screen the drug library and identify potent drug combinations. First, ScaffComb is validated using the US food and drug administration dataset and known drug combinations are successfully reidentified. Then, ScaffComb is applied to screen the ZINC and ChEMBL databases, which yield novel drug combinations and reveal an ability to discover new synergistic mechanisms. To our knowledge, ScaffComb is the first method to use phenotype‐based virtual screening of drug combinations in large‐scale chemical datasets.

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DrugComb: an integrative cancer drug combination data portal

Cited by 179 publications

References 41 publications

MatchMaker: A Deep Learning Framework for Drug Synergy Prediction

MatchMaker: A Deep Learning Framework for Drug Synergy Prediction

Anti-cancer Drug Synergy Prediction in Understudied Tissues using Transfer Learning

ScaffComb: A Phenotype‐Based Framework for Drug Combination Virtual Screening in Large‐Scale Chemical Datasets

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