SimBoost: a read-across approach for predicting drug–target binding affinities using gradient boosting machines

He, Tong; Heidemeyer, Marten; Ban, Fuqiang; Cherkasov, Artem; Ester, Martin

doi:10.1186/s13321-017-0209-z

Cited by 273 publications

(338 citation statements)

References 41 publications

Supporting

Mentioning

337

Contrasting

Order By: Relevance

“…This deep learningbased approach is particularly useful, since it does not require protein structural information, which can be a bottleneck for identifying drugs targeted for uncharacterized proteins with traditional three-dimensional (3D) structure-based docking approaches (20). Neverthless, MT-DTI showed the best performance (12) when compared to a deep learning-based (DeepDTA) approach (21) and two traditional machine learning-based algorithms SimBoost (22), and KronRLS (23), with the KIBA (24) and DAVIS (25) data sets. Taking advantage of this sequence-based drug-target affinity prediction approach, binding affinities of 3,410 FDAapproved drugs against 3C-like proteinase, RdRp, helicase, 3'-to-5' exonuclease, endoRNAse, and 2'-O-ribose methyltransferase of SARS-CoV-2 were predicted.…”

Section: Resultsmentioning

confidence: 99%

Predicting commercially available antiviral drugs that may act on the novel coronavirus (SARS-CoV-2) through a drug-target interaction deep learning model

Beck

Shin

Choi

et al. 2020

Computational and Structural Biotechnology Journal

616

479

View full text Add to dashboard Cite

The infection of a novel coronavirus found in Wuhan of China (SARS-CoV-2) is rapidly spreading, and the incidence rate is increasing worldwide. Due to the lack of effective treatment options for SARS-CoV-2, various strategies are being tested in China, including drug repurposing. In this study, we used our pre-trained deep learning-based drug-target interaction model called Molecule Transformer-Drug Target Interaction (MT-DTI) to identify commercially available drugs that could act on viral proteins of SARS-CoV-2. The result showed that atazanavir, an antiretroviral medication used to treat and prevent the human immunodeficiency virus (HIV), is the best chemical compound, showing an inhibitory potency with Kd of 94.94 nM against the SARS-CoV-2 3C-like proteinase, followed by remdesivir (113.13 nM), efavirenz (199.17 nM), ritonavir (204.05 nM), and dolutegravir (336.91 nM). Interestingly, lopinavir, ritonavir, and darunavir are all designed to target viral proteinases. However, in our prediction, they may also bind to the replication complex components of SARS-CoV-2 with an inhibitory potency with Kd < 1,000 nM. In addition, we also found that several antiviral agents, such as Kaletra (lopinavir/ritonavir), could be used for the treatment of SARS-CoV-2. Overall, we suggest that the list of antiviral drugs identified by the MT-DTI model should be considered, when establishing effective treatment strategies for SARS-CoV-2.

show abstract

Section: Resultsmentioning

confidence: 99%

Predicting commercially available antiviral drugs that may act on the novel coronavirus (SARS-CoV-2) through a drug-target interaction deep learning model

Beck

Shin

Choi

et al. 2020

Computational and Structural Biotechnology Journal

616

479

View full text Add to dashboard Cite

show abstract

“…Their study was restricted to the kinases protein family, and therefore, was far from covering the protein diversity required to predict drugs unexpected targets. On this focused dataset, they obtained better or similar performance compared to Simboost [49] (a gradient boosting method) and KronRLS [12] (analogue of KronSVM but with kernelised Recursive Least Square) in terms of Mean Square Error (MSE). When "binarising" the outputs based on a threshold in pKd value to distinguish actives from inactives, their method performed slightly better than the considered shallow methods, in terms of AUPR.…”

Section: Related Workmentioning

confidence: 95%

Evaluation of deep and shallow learning methods in chemogenomics for the prediction of drugs specificity

Playe

Stoven

2020

J Cheminform

View full text Add to dashboard Cite

Chemogenomics, also called proteochemometrics, covers a range of computational methods that can be used to predict protein-ligand interactions at large scales in the protein and chemical spaces. They differ from more classical ligand-based methods (also called QSAR) that predict ligands for a given protein receptor. In the context of drug discovery process, chemogenomics allows to tackle the question of predicting off-target proteins for drug candidates, one of the main causes of undesirable side-effects and failure within drugs development processes. The present study compares shallow and deep machine-learning approaches for chemogenomics, and explores data augmentation techniques for deep learning algorithms in chemogenomics. Shallow machine-learning algorithms rely on expertbased chemical and protein descriptors, while recent developments in deep learning algorithms enable to learn abstract numerical representations of molecular graphs and protein sequences, in order to optimise the performance of the prediction task. We first propose a formulation of chemogenomics with deep learning, called the chemogenomic neural network (CN), as a feed-forward neural network taking as input the combination of molecule and protein representations learnt by molecular graph and protein sequence encoders. We show that, on large datasets, the deep learning CN model outperforms state-of-the-art shallow methods, and competes with deep methods with expert-based descriptors. However, on small datasets, shallow methods present better prediction performance than deep learning methods. Then, we evaluate data augmentation techniques, namely multi-view and transfer learning, to improve the prediction performance of the chemogenomic neural network. We conclude that a promising research direction is to integrate heterogeneous sources of data such as auxiliary tasks for which large datasets are available, or independently, multiple molecule and protein attribute views.

show abstract

“…The affinity available in training is also used, accompanied with similarities among drugs as well as among targets, to build features in [13] (SimBoost). The features are then are the input to gradient boosting machines to predict the binding affinity for unknown drug-target couples.…”

Section: Prediction Of Drug-target Binding Affinity 221 Affinity Simentioning

confidence: 99%

GraphDTA: Predicting drug–target binding affinity with graph neural networks

Nguyen

Quinn

et al. 2019

Preprint

137

248

View full text Add to dashboard Cite

Background: While the development of new drugs is costly, time consuming, and often accompanied with safety issues, drug repurposing, where old drugs with established safety are used for medical conditions other than originally developed, is an attractive alternative. Then, how the old drugs work on new targets becomes a crucial part of drug repurposing and gains much of interest. Several statistical and machine learning models have been proposed to estimate drug-target binding affinity and deep learning approaches have been shown to be among state-of-the-art methods. However, drugs and targets in these models have been commonly represented in 1D strings, regardless the fact that molecules are by nature formed by the chemical bonding of atoms. Method: In this work, we propose GraphDTA to capture the structural information of drugs, possibly enhancing the predictive power of the affinity. In particular, unlike competing methods, drugs are represented as graphs and graph convolutional networks are used to learn drug-target binding affinity. We trial our method on two benchmark datasets of drug-target binding affinity and compare the performance with state-of-the-art models in the research field. Results: The results show that our proposed method can not only predict the affinity better than non-deep learning models, but also outperform competing deep learning approaches. This demonstrates the practical advantages of graph-based representation for molecules in providing accurate prediction of drug-target binding affinity. The application may also include any recommendation systems where either or both of the user-and product-like sides can be represented in graphs. Availability and implementation: The proposed models are implemented in Python. Related data, pre-trained models, and source code are publicly available at https://github.com/thinng/GraphDTA.

show abstract

SimBoost: a read-across approach for predicting drug–target binding affinities using gradient boosting machines

Cited by 273 publications

References 41 publications

Predicting commercially available antiviral drugs that may act on the novel coronavirus (SARS-CoV-2) through a drug-target interaction deep learning model

Predicting commercially available antiviral drugs that may act on the novel coronavirus (SARS-CoV-2) through a drug-target interaction deep learning model

Evaluation of deep and shallow learning methods in chemogenomics for the prediction of drugs specificity

GraphDTA: Predicting drug–target binding affinity with graph neural networks

Contact Info

Product

Resources

About