Kinase inhibitor data set for systematic analysis of representative kinases across the human kinome

Laufkötter, Oliver; Laufer, Stefan; Bajorath, Jürgen

doi:10.1016/j.dib.2020.106189

Cited by 6 publications

(7 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Using the Molecular Substructure Miner (MoSS), 27 we found two chemical fragments, sulfanilamide (16.2% support) and 2‐( N‐Anilino ) pyrimidine (10.1% support) that occurred more frequently in CDK2 inhibitors compared to non‐inhibitors (Figure 2 ), and appeared together with 2% support. Moreover, to analyze whether these two fragments are selective for CDK2, we searched them against a library of more than 36,000 known multi‐kinase inhibitors for 420 human kinases (with pKi, pKd, or pIC50 ≥ 6), 28 and found they occur much less frequently in other kinase inhibitors and even other types of CDK2 inhibitors (<1% support). Atoms in these enriched and selective groups include hydrogen bond donors and acceptors, which can form interactions with the linker and hinge region in CDK2.…”

Section: Resultsmentioning

confidence: 99%

kinCSM: Using graph‐based signatures to predict small molecule CDK2 inhibitors

et al. 2022

View full text Add to dashboard Cite

Protein phosphorylation acts as an essential on/off switch in many cellular signaling pathways. This has led to ongoing interest in targeting kinases for therapeutic intervention. Computer‐aided drug discovery has been proven a useful and cost‐effective approach for facilitating prioritization and enrichment of screening libraries, but limited effort has been devoted providing insights on what makes a potent kinase inhibitor. To fill this gap, here we developed kinCSM, an integrative computational tool capable of accurately identifying potent cyclin‐dependent kinase 2 (CDK2) inhibitors, quantitatively predicting CDK2 ligand–kinase inhibition constants (pKi) and classifying different types of inhibitors based on their favorable binding modes. kinCSM predictive models were built using supervised learning and leveraged the concept of graph‐based signatures to capture both physicochemical properties and geometry properties of small molecules. CDK2 inhibitors were accurately identified with Matthew's Correlation Coefficients (MCC) of up to 0.74, and inhibition constants predicted with Pearson's correlation of up to 0.76, both with consistent performances of 0.66 and 0.68 on a nonredundant blind test, respectively. kinCSM was also able to identify the potential type of inhibition for a given molecule, achieving MCC of up to 0.80 on cross‐validation and 0.73 on the blind test. Analyzing the molecular composition of revealed enriched chemical fragments in CDK2 inhibitors and different types of inhibitors, which provides insights into the molecular mechanisms behind ligand–kinase interactions. kinCSM will be an invaluable tool to guide future kinase drug discovery. To aid the fast and accurate screening of CDK2 inhibitors, kinCSM is freely available at https://biosig.lab.uq.edu.au/kin_csm/.

show abstract

Section: Resultsmentioning

confidence: 99%

kinCSM: Using graph‐based signatures to predict small molecule CDK2 inhibitors

et al. 2022

View full text Add to dashboard Cite

show abstract

“…We apply one-shot learning models on two data sets: the kinase–inhibitor and GPCR–ligand data sets. The kinase–inhibitor data set includes 420 unique kinase targets, 36 628 inhibitors, and 123 005 kinase–inhibitor interactions . The GPCR–ligand data set is downloaded from GPCRdb ().…”

Section: Methodsmentioning

confidence: 99%

“…The kinase−inhibitor data set includes 420 unique kinase targets, 36 628 inhibitors, and 123 005 kinase−inhibitor interactions. 26 The GPCR−ligand data set is downloaded from GPCRdb (https://gpcrdb.org). As the largest subfamily of GPCR, 525 class A GPCRs, 132 354 ligands, and 215 684 GPCR−ligand bindings are retrieved from GPCRdb.…”

Section: ■ Introductionmentioning

confidence: 99%

Predicting the Activities of Drug Excipients on Biological Targets using One-Shot Learning

Shukla

2022

J. Phys. Chem. B

View full text Add to dashboard Cite

Excipients are major components of drugs and are used to improve drug attributes such as stability and appearance. Excipients approved by the U.S. Food and Drug Administration (FDA) are regarded as safe for humans in allowed concentrations, but their potential interactions with drug targets have not been investigated systematically, which might influence a drug’s efficacy. Deep learning models have been used for the identification of ligands that could bind to the drug targets. However, due to the limited available data, it is challenging to reliably estimate the likelihood of a ligand–protein interaction. One-shot learning techniques provide a potential approach to address this low data problem as these techniques require only one or a few examples to classify the new data. In this study, we apply one-shot learning models to data sets that include ligands binding to G-protein-coupled receptors (GPCRs) and kinases. The predicted results suggest that one-shot learning could be used for predicting ligand–protein interactions, and the models attain better performance when protein targets contain conserved binding pockets. The trained models are also used to predict interactions between excipients and drug targets, which provides a potential efficient strategy to explore the activities of drug excipients. We find that a large number of drug excipients could interact with biological targets and influence their function. The results demonstrate how one-shot learning can be used to make accurate predictions for excipient–protein interactions, and these methods could be used for selecting excipients with limited drug–protein interactions.

show abstract

“…The kinase-inhibitor dataset include 420 unique kinase targets, 36, 628 inhibitors and 123, 005 kinase-inhibtor interactions. 26 The GPCR-ligand dataset was downloaded from GPCRdb (https://gpcrdb.org). As the largest sub-family of GPCR, 525 class A GPCRs, 132, 354 ligands and 215, 684 GPCR-ligand bindings were retrieved from GPCRdb.…”

Section: Methods Datasets Curationmentioning

confidence: 99%

Predicting the Activities of Drug Excipients on Biological Targets using One-Shot Learning

Shukla

2021

Preprint

View full text Add to dashboard Cite

Excipients are a major component of drugs and are used to improve drugs attributes such as stability and appearance. Excipients approved by Food and Drug Administration (FDA) are regarded as safe for human in allowed concentration, but their potential interaction with drug targets have not been investigated systematically, which might influence drugs efficacy. Deep learning models have been used for identification of ligands that could bind to the drug targets. However, due to the limited available data, it is challenging to reliably estimate the likelihood of a ligand-protein interaction. One-shot learning techniques provide a potential approach to address this low-data problem as these techniques require only one or a few examples to classify the new data. In this study, we apply one-shot learning models on datasets that include ligands binding to G-Protein Coupled Receptors (GPCRs) and Kinases. The predicted results suggest that one-shot learning models could be used for predicting ligand-protein interaction and the models attain better performance when protein targets contain conserved binding pockets. The trained models are also used to predict interactions between excipients and drug targets, which provides a potential efficient strategy to explore the activities of drug excipients. We find that a large number of drug excipients could interact with biological targets and influence their function. The results demonstrate how one-shot learning models can be used to make accurate prediction for excipient-protein interactions and these methods could be used for selecting excipients with limited drug-protein interactions.

show abstract

Kinase inhibitor data set for systematic analysis of representative kinases across the human kinome

Cited by 6 publications

References 14 publications

kinCSM: Using graph‐based signatures to predict small molecule CDK2 inhibitors

kinCSM: Using graph‐based signatures to predict small molecule CDK2 inhibitors

Predicting the Activities of Drug Excipients on Biological Targets using One-Shot Learning

Predicting the Activities of Drug Excipients on Biological Targets using One-Shot Learning

Contact Info

Product

Resources

About