Analysis of multiple compound–protein interactions reveals novel bioactive molecules

Yabuuchi, Hiroaki; Niijima, Satoshi; Takematsu, Hiromu; Ida, Takanori; Hirokawa, Takatsugu; Hara, Takafumi; Ogawa, Teppei; Minowa, Yohsuke; Tsujimoto, Gozoh; Okuno, Yasushi

doi:10.1038/msb.2011.5

Cited by 142 publications

(145 citation statements)

References 51 publications

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“…The diversity of compounds included in a chemogenomic model will directly affect the amount of scaffold hopping achievable for prospective applications [36,79]. Figure 4 (2D histograms) and Supplementary Figure 3 demonstrate that the chemical space explored was dependent on the picking strategy.…”

Section: Diversity and Properties Of Selected Compoundsmentioning

confidence: 99%

“…As a number of recent studies have indeed validated that the computational chemogenomic concept can lead to prospective discovery of interactions [36][37][87][88], we anticipate that actively learned models will be capable of similar novel discovery [48,49,51] . Given the increasing applicability of chemogenomics to uncover untested ligand-target pairs, many different exciting applications come to mind.…”

Section: Implications and Future Directionsmentioning

confidence: 99%

“…They can efficiently use the available data to predict links between the provided sets of bioactive compounds and their biomacromolecular targets (termed 'Class I' prediction in [33], see Box 1 for terminology). Two noteworthy prospective applications of computational chemogenomics have applied the concept to discover ligands for simulated orphan targets [36] ('Class III' prediction) as well as study resistance development through point mutations in HIV non-nucleoside reverse transcriptase mutants [37]. These studies specifically highlight how computational chemogenomics methods are able to explore potential pharmacological relationships both from a ligand-as well as a target-centric perspective [38].…”

Section: Introductionmentioning

confidence: 99%

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Active Learning for Computational Chemogenomics

et al. 2017

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Aim: Computational chemogenomics models the compound–protein interaction space, typically for drug discovery, where existing methods predominantly either incorporate increasing numbers of bioactivity samples or focus on specific subfamilies of proteins and ligands. As an alternative to modeling entire large datasets at once, active learning adaptively incorporates a minimum of informative examples for modeling, yielding compact but high quality models. Results/methodology: We assessed active learning for protein/target family-wide chemogenomic modeling by replicate experiment. Results demonstrate that small yet highly predictive models can be extracted from only 10–25% of large bioactivity datasets, irrespective of molecule descriptors used. Conclusion: Chemogenomic active learning identifies small subsets of ligand–target interactions in a large screening database that lead to knowledge discovery and highly predictive models.

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Section: Diversity and Properties Of Selected Compoundsmentioning

confidence: 99%

Section: Implications and Future Directionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Active Learning for Computational Chemogenomics

et al. 2017

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“…Potential application areas of chemogenomic approaches therefore also include assessment of target selectivities, receptor deorphanising, and drug repurposing. The slow but steady increase in retro-and prospective studies using chemogenomic methodologies hints at its utility and benefit for different applications in medicinal chemistry and chemical biology [14,[19][20][21][22][23]. Nevertheless, the sheer data volume, and sparseness and complexity of the compound-protein matrix often necessitate complex chemogenomic machine learning approaches.…”

Section: Introductionmentioning

confidence: 99%

Small Random Forest Models for Effective Chemogenomic Active Learning

Rakers

Reker

Brown

2017

J. Comput. Aided Chem.

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The identification of new compound-protein interactions has long been the fundamental quest in the field of medicinal chemistry. With increasing amounts of biochemical data, advanced machine learning techniques such as active learning have been proven to be beneficial for building high-performance prediction models upon subsets of such complex data. In a recently published paper, chemogenomic active learning had been applied to the interaction spaces of kinases and G protein-coupled receptors featuring over 150,000 compound-protein interactions. Prediction models were actively trained based on random forest classification using 500 decision trees per experiment. In a new direction for chemogenomic active learning, we address the question of how forest size influences model evolution and performance. In addition to the original chemogenomic active learning findings that highly predictive models could be constructed from a small fraction of the available data, we find here that that model complexity as viewed by forest size can be reduced to one-fourth or one-fifth of the previously investigated forest size while still maintaining reliable prediction performance. Thus, chemogenomic active learning can yield predictive models with reduced complexity based on only a fraction of the data available for model construction.

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“…Now it is possible for us to quickly and inexpensively identify potential DTIs and repurpose existing drugs [23][24][25][26][27] through the developments of computational methods. These methods are mainly divided into three categories, including basic network-based models, machine learningbased models, and other approaches based on similarity [28].…”

Section: Introductionmentioning

confidence: 99%

SDTRLS: Predicting Drug-Target Interactions for Complex Diseases Based on Chemical Substructures

Cheng

Lan

et al. 2017

Complexity

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It is well known that drug discovery for complex diseases via biological experiments is a time-consuming and expensive process. Alternatively, the computational methods provide a low-cost and high-efficiency way for predicting drug-target interactions (DTIs) from biomolecular networks. However, the current computational methods mainly deal with DTI predictions of known drugs; there are few methods for large-scale prediction of failed drugs and new chemical entities that are currently stored in some biological databases may be effective for other diseases compared with their originally targeted diseases. In this study, we propose a method (called SDTRLS) which predicts DTIs through RLS-Kron model with chemical substructure similarity fusion and Gaussian Interaction Profile (GIP) kernels. SDTRLS can be an effective predictor for targets of old drugs, failed drugs, and new chemical entities from large-scale biomolecular network databases. Our computational experiments show that SDTRLS outperforms the state-of-the-art SDTNBI method; specifically, in the G protein-coupled receptors (GPCRs) external validation, the maximum and the average AUC values of SDTRLS are 0.842 and 0.826, respectively, which are superior to those of SDTNBI, which are 0.797 and 0.766, respectively. This study provides an important basis for new drug development and drug repositioning based on biomolecular networks.

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Analysis of multiple compound–protein interactions reveals novel bioactive molecules

Abstract: The authors use machine learning of compound-protein interactions to explore drug polypharmacology and to efficiently identify bioactive ligands, including novel scaffold-hopping compounds for two pharmaceutically important protein families: G-protein coupled receptors and protein kinases.

Cited by 142 publications

References 51 publications

Active Learning for Computational Chemogenomics

Active Learning for Computational Chemogenomics

Small Random Forest Models for Effective Chemogenomic Active Learning

SDTRLS: Predicting Drug-Target Interactions for Complex Diseases Based on Chemical Substructures

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