Promiscuous Compounds Research Articles

An Expanded Substrate Spectrum of Sulfide:Quinone Oxidoreductase Found in Pollutant-Degrading Bacteria.

Sulfide:quinone oxidoreductase (Sqr) catalyzes the initial procedure on sulfide transformation, alongside sulfide (H2S, S2-) oxidization coupled with coenzyme Q (CoQ) reducing and reactive sulfur species (RSS) production. Here, we assessed the reactivity of propanethiol (PT) as an alternative substrate for Sqr to maintain intracellular homeostasis in strain S-1 capable of degrading emerging sulfur-containing pollutants. We deleted a gene encoding Sqr, and serial transcriptional difference induced by RSS dynamics was therefore revealed. Next, the reaction properties of two Sqr homologs from strains JMP134 and S-1 were comparatively characterized, respectively. As a result, an additional role of Sqr in yielding RSS from PT was found in reaction mixture prepared by cell-free extracts or purified enzymes. Interestingly, the transformation velocity of PT by Sqr was slower than that of sulfides. From this scenario, it was a rate-determining step that PT as a nucleophilic compound can be added into Sqr cysteine to form disulfide bond and likely serve nonoptimal sulfur recipient. In addition, the role of persulfidation driven by RSS in combating oxidative and sulfur stresses required to be further clarified. Nevertheless, this promiscuity of Sqr-binding organosulfur compounds and its catalytic modulation underscored that expanded substrates might benefit sulfide homeostasis in thiol-degrading bacteria.

Evaluating the Utility of the MSTI Assay in Predicting Compound Promiscuity and Cytotoxicity.

Nonspecific reactive chemicals often interfere with the interpretation of high-throughput assay results because of their promiscuity and/or cytotoxicity. Using a high-throughput assay to identify such compounds is necessary to efficiently rule out potential assay artifacts. The MSTI, (E)-2-(4-mercaptostyryl)-1,3,3-trimethyl-3H-indol-1-ium, assay uses a thiol-containing fluorescent probe to screen for electrophile reactivity and could potentially be used to determine nonspecific reactive compounds. The Tox21 10K compound library was previously screened against a panel of ∼80 cell-based and biochemical assays, including the biochemical MSTI assay. In this study, we compared the MSTI assay activity of the Tox21 10K compounds with their promiscuity and cytotoxicity as reflected by their activities across the Tox21 assay panel to determine: (1) if this assay is predictive of a compound's promiscuity and cytotoxicity and (2) what chemical features create inconsistent results between the MSTI assay activity and promiscuity/cytotoxicity (false negatives and false positives). We found that the MSTI assay can predict a chemical's promiscuity/cytotoxicity with a 0.55 sensitivity and 0.97 specificity. Out of 3,407 unique compounds evaluated, we identified 92 false positive and 227 false negative results. Several structural features such as carboxamides and alkyl halides were found to be apparent in 53% (p = 2.4 × 10-07) and 19% (p = 4.3 × 10-06) of the false positives and negatives, respectively. The results of this analysis will help identify the potential challenges of this high-throughput assay and allow researchers to identify if a compound will be cytotoxic or promiscuous in an efficient manner.

ChemFH: an integrated tool for screening frequent false positives in chemical biology and drug discovery.

High-throughput screening rapidly tests an extensive array of chemical compounds to identify hit compounds for specific biological targets in drug discovery. However, false-positive results disrupt hit compound screening, leading to wastage of time and resources. To address this, we propose ChemFH, an integrated online platform facilitating rapid virtual evaluation of potential false positives, including colloidal aggregators, spectroscopic interference compounds, firefly luciferase inhibitors, chemical reactive compounds, promiscuous compounds, and other assay interferences. By leveraging a dataset containing 823391 compounds, we constructed high-quality prediction models using multi-task directed message-passing network (DMPNN) architectures combining uncertainty estimation, yielding an average AUC value of 0.91. Furthermore, ChemFH incorporated 1441 representative alert substructures derived from the collected data and ten commonly used frequent hitter screening rules. ChemFH was validated with an external set of 75 compounds. Subsequently, the virtual screening capability of ChemFH was successfully confirmed through its application to five virtual screening libraries. Furthermore, ChemFH underwent additional validation on two natural products and FDA-approved drugs, yielding reliable and accurate results. ChemFH is a comprehensive, reliable, and computationally efficient screening pipeline that facilitates the identification of true positive results in assays, contributing to enhanced efficiency and success rates in drug discovery. ChemFH is freely available via https://chemfh.scbdd.com/.

The Thin Line between Promiscuous and Privileged Structures in Medicinal Chemistry

The concept of privileged structures in medicinal chemistry refers to commonly found substructures in approved drugs and lead small molecules, presenting a broad profile of pharmacological action, i.e., participating in the recognition by several classes of pharmacological targets or by modulating physicochemical properties. Privileged structures are also related to nontoxic effects, which make their use in the design of new drug candidates very attractive. In contrast, another concept also refers to structures or substructures capable of presenting a pleiotropic profile for several pharmacological targets, referred to as promiscuous compounds. Worth mentioning, more recently, a great majority of promiscuous compounds have been classified as Pan Assay Interference Compounds (PAINS). In its great majority, PAINS are electrophilic in nature, capable of covalently reacting indiscriminately with several pharmacological targets, and are associated with specific substructures, which are, in turn, used as an exclusion filter during screening campaigns. This work aims to critically discuss the thin line that separates the two concepts, clarifying their differences from a molecular and pharmacological point of view. Moreover, special considerations regarding PAINS and exclusion filters will be made.

Ligand flexibility and binding pocket malleability cooperate to allow selective PXR activation by analogs of a promiscuous nuclear receptor ligand

The human nuclear receptor (NR) family of transcription factors contains 48 proteins that bind lipophilic molecules. Approved NR therapies have had immense success treating various diseases, but lack of selectivity has hindered efforts to therapeutically target the majority of NRs due to unpredictable off-target effects. The synthetic ligand T0901317 was originally discovered as a potent agonist of liver X receptors (LXRα/β) but subsequently found to target additional NRs, with activation of pregnane X receptor (PXR) being as potent as that of LXRs. We previously showed that directed rigidity reduces PXR binding by T0901317 derivatives through unfavorable protein remodeling. Here, we use a similar approach to achieve selectivity for PXR over other T0901317-targeted NRs. One molecule, SJPYT-318, accomplishes selectivity by favorably utilizing PXR's flexible binding pocket and surprisingly binding in a new mode distinct from the parental T0901317. Our work provides a structure-guided framework to achieve NR selectivity from promiscuous compounds.

Mining the Protein Data Bank to inspire fragment library design.

The fragment approach has emerged as a method of choice for drug design, as it allows difficult therapeutic targets to be addressed. Success lies in the choice of the screened chemical library and the biophysical screening method, and also in the quality of the selected fragment and structural information used to develop a drug-like ligand. It has recently been proposed that promiscuous compounds, i.e., those that bind to several proteins, present an advantage for the fragment approach because they are likely to give frequent hits in screening. In this study, we searched the Protein Data Bank for fragments with multiple binding modes and targeting different sites. We identified 203 fragments represented by 90 scaffolds, some of which are not or hardly present in commercial fragment libraries. By contrast to other available fragment libraries, the studied set is enriched in fragments with a marked three-dimensional character (download at 10.5281/zenodo.7554649).

An emerging flavone glycoside from Phyllanthus emblica L.: A promiscuous enzyme inhibitor and potential therapeutic in chronic diseases

Nowadays, the designing of a single drug molecule interacting simultaneously and specifically with multiple targets (exhibits compound promiscuity) is gaining major consideration in drug discovery. Our previous screening research of 54 medicinal plants for possible multi-target inhibitory activity revealed that the methanolic extract of Phylanthus emblica L. leaves exhibited promising inhibitory activity with 98.37 ±1.09, 52.84±1.39, and 86.90±0.27 % against alpha amylase, tyrosinase, and hyaluronidase enzymes, respectively. Subsequently, bioassay-guided fractionation revealed that ethyl acetate fraction retained the highest inhibitory activity against all the three tested enzymes with 99.72±1.79, 59.09±1.93, and 87.36±0.48 %, respectively. Further fractionation of ethyl acetate fraction resulted in the isolation of two flavone glycosides; luteolin-7-O-α-L-rhamnoside (1) and apigenin-7-O-α-L-rhamnoside (2). Compound 1 exhibited strong inhibitory activity with 89.79, 41.50, and 67.25% against alpha amylase, tyrosinase, and hyaluronidase, respectively. Kinetics studies and molecular docking simulation revealed and confirmed that compound 1 is a non-competitive inhibitor for both alpha amylase and tyrosinase but a competitive inhibitor for hyaluronidase. Outcome of this study suggests that luteolin-7-O-α-L-rhamnoside from Phyllanthus emblica L. could be used as a potential promiscuous enzyme inhibitor in the management and treatment of some chronic diseases.

Atropisomerism in the Pharmaceutically Relevant Realm.

ConspectusAtropisomerism is a conformational chirality that occurs when there is hindered rotation about a σ-bond. While atropisomerism is exemplified by biaryls, it is observed in many other pharmaceutically relevant scaffolds including heterobiaryls, benzamides, diarylamines, and anilides. As bond rotation leads to racemization, atropisomers span the gamut of stereochemical stability. LaPlante has classified atropisomers based on their half-life of racemization at 37 °C: class 1 (t1/2 < 60 s), class 2 (60 s < t1/2 < 4.5 years), and class 3 (t1/2 > 4.5 years). In general, class-3 atropisomers are considered to be suitable for drug development. There are currently four FDA-approved drugs that exist as stable atropisomers, and many others are in clinical trials or have recently appeared in the drug discovery literature. Class-1 atropisomers are more prevalent, with ∼30% of recent FDA-approved small molecules possessing at least one class-1 axis. While class-1 atropisomers do not possess the requisite stereochemical stability to meet the classical definition of atropisomerism, they often bind a given target in a specific set of chiral conformations.Over the past decade, our laboratory has embarked on a research program aimed at leveraging atropisomerism as a design feature to improve the target selectivity of promiscuous lead compounds. Our studies initially focused on introducing class-3 atropisomerism into promiscuous kinase inhibitors, resulting in a proof of principle in which the different atropisomers of a compound can have different selectivity profiles with potentially improved target selectivity. This inspired a careful analysis of the binding conformations of diverse ligands bound to different target proteins, resulting in the realization that the sampled dihedral conformations about a prospective atropisomeric axis played a key role in target binding and that preorganizing the prospective atropisomeric axis into a desired target’s preferred conformational range can lead to large gains in target selectivity.As atropisomerism is becoming more prevalent in modern drug discovery, there is an increasing need for strategies for atropisomerically pure samples of pharmaceutical compounds. This has led us and other groups to develop catalytic atroposelective methodologies toward pharmaceutically privileged scaffolds. Our laboratory has contributed examples of atroposelective methodologies toward heterobiaryl systems while also exploring the chirality of less-studied atropisomers such as diarylamines and related scaffolds.This Account will detail recent encounters with atropisomerism in medicinal chemistry and how atropisomerism has transitioned from a “lurking menace” into a leverageable design strategy in order to modulate various properties of biologically active small molecules. This Account will also discuss recent advances in atroposelective synthesis, with a focus on methodologies toward pharmaceutically privileged scaffolds. We predict that a better understanding of the effects of conformational restriction about a prospective atropisomeric axis on target binding will empower chemists to rapidly “program” the selectivity of a lead molecule toward a desired target.

Phenotypic screening of low molecular weight compounds is rich ground for repurposed, on-target drugs.

A target-based drug discovery strategy has led to a bias away from low molecular weight (MWT) drug discovery. Analysis of the ACS chemistry registration system shows that most low MWT drugs were first made in the time era before target-based drug discovery. Therapeutic activity among most low MWT drugs was identified in the era of phenotypic drug discovery when drugs were selected based on their phenotypic effects and before in vitro screening, mechanism of action considerations and experiences with fragment screening became known. The common perception that drugs cannot be found among low MWT compounds is incorrect based on both drug discovery history and our own experience with MLR-1023. The greater proportion of low MWT compounds that are commercially available compared to higher MWT compounds is a factor that should facilitate biology study. We posit that low MWT compounds are more suited to identification of new therapeutic activity using phenotypic screens provided that the phenotypic screening method has enough screening capacity. On-target and off-target therapeutic activities are discussed from both a chemistry and biology perspective because of a concern that either phenotypic or low MWT drug discovery might bias towards promiscuous compounds that combine on-target and off-target effects. Among ideal drug repositioning candidates (late-stage pre-clinical or clinically-experience compounds), pleiotropic activity (multiple therapeutic actions) is far more likely due to on-target effects arising where a single target mediates multiple therapeutic benefits, a desirable outcome for drug development purposes compared to the off-target alternative. Our exemplar of a low MWT compound, MLR-1023, discovered by phenotypic screening and subsequently found to have a single mechanism of action would have been overlooked based on current era medicinal chemistry precedent. The diverse therapeutic activities described for this compound by us, and others arise from the same pleiotropic lyn kinase activation molecular target. MLR-1023 serves as a proof-of-principle that potent, on target, low MWT drugs can be discovered by phenotypic screening.

Keystone Malaria Symposium 2022: a vibrant discussion of progress made and challenges ahead from drug discovery to treatment

Keystone Malaria Symposium 2022: a vibrant discussion of progress made and challenges ahead from drug discovery to treatment

Docking-Based Virtual Screening Enables Prioritizing Protein Kinase Inhibitors With In Vitro Phenotypic Activity Against Schistosoma mansoni.

Schistosomiasis is a parasitic neglected disease with praziquantel (PZQ) utilized as the main drug for treatment, despite its low effectiveness against early stages of the worm. To aid in the search for new drugs to tackle schistosomiasis, computer-aided drug design has been proved a helpful tool to enhance the search and initial identification of schistosomicidal compounds, allowing fast and cost-efficient progress in drug discovery. The combination of high-throughput in silico data followed by in vitro phenotypic screening assays allows the assessment of a vast library of compounds with the potential to inhibit a single or even several biological targets in a more time- and cost-saving manner. Here, we describe the molecular docking for in silico screening of predicted homology models of five protein kinases (JNK, p38, ERK1, ERK2, and FES) of Schistosoma mansoni against approximately 85,000 molecules from the Managed Chemical Compounds Collection (MCCC) of the University of Nottingham (UK). We selected 169 molecules predicted to bind to SmERK1, SmERK2, SmFES, SmJNK, and/or Smp38 for in vitro screening assays using schistosomula and adult worms. In total, 89 (52.6%) molecules were considered active in at least one of the assays. This approach shows a much higher efficiency when compared to using only traditional high-throughput in vitro screening assays, where initial positive hits are retrieved from testing thousands of molecules. Additionally, when we focused on compound promiscuity over selectivity, we were able to efficiently detect active compounds that are predicted to target all kinases at the same time. This approach reinforces the concept of polypharmacology aiming for “one drug-multiple targets”. Moreover, at least 17 active compounds presented satisfactory drug-like properties score when compared to PZQ, which allows for optimization before further in vivo screening assays. In conclusion, our data support the use of computer-aided drug design methodologies in conjunction with high-throughput screening approach.

Insights into estrogen receptor alpha modulation by cholestenoic acids

Oxysterols are a family of over 25 cholesterol metabolites naturally produced by enzymatic or radical oxidation. They are involved in many physiological and pathological pathways. Although their activity has been mainly attributed to the modulation of the Liver X Receptors (LXR), it is currently accepted that oxysterols are quite promiscuous compounds, acting at several targets at the same time. The promiscuity of the oxysterols with the Estrogen Receptor α (ERα) is crucial in several pathologies such as ER+ breast cancer, inflammation and atherosclerosis. Regarding this matter, we have previously reported the synthesis, LXR activity and binding mode of a family of cholestenoic acid analogs with a modified side chain. Here we report the transcriptional activity on the ERα triggered by these compounds and details on the molecular determinants involved in their activities in order to establish structure-activity relationships to shed light over the molecular basis of the promiscuity of these compounds on ER/LXR responses. Our results show that 3β-hydroxy-5-cholestenoic acid can interact with the ERα receptor in a way similar to 26-hydroxycholesterol and is an agonist of the receptor. Using molecular dynamics simulations, we were able to predict the ERα activity of a set of cholestenoic acid analogs with changes in the flexibility and/or steric requirements of the side chain, some of which exhibited selective activation of ERα or LXR.

Investigation of the specificity and mechanism of action of the ULK1/AMPK inhibitor SBI-0206965.

SBI-0206965, originally identified as an inhibitor of the autophagy initiator kinase ULK1, has recently been reported as a more potent and selective AMP-activated protein kinase (AMPK) inhibitor relative to the widely used, but promiscuous inhibitor Compound C/Dorsomorphin. Here, we studied the effects of SBI-0206965 on AMPK signalling and metabolic readouts in multiple cell types, including hepatocytes, skeletal muscle cells and adipocytes. We observed SBI-0206965 dose dependently attenuated AMPK activator (991)-stimulated ACC phosphorylation and inhibition of lipogenesis in hepatocytes. SBI-0206965 (≥25 μM) modestly inhibited AMPK signalling in C2C12 myotubes, but also inhibited insulin signalling, insulin-mediated/AMPK-independent glucose uptake, and AICA-riboside uptake. We performed an extended screen of SBI-0206965 against a panel of 140 human protein kinases in vitro, which showed SBI-0206965 inhibits several kinases, including members of AMPK-related kinases (NUAK1, MARK3/4), equally or more potently than AMPK or ULK1. This screen, together with molecular modelling, revealed that most SBI-0206965-sensitive kinases contain a large gatekeeper residue with a preference for methionine at this position. We observed that mutation of the gatekeeper methionine to a smaller side chain amino acid (threonine) rendered AMPK and ULK1 resistant to SBI-0206965 inhibition. These results demonstrate that although SBI-0206965 has utility for delineating AMPK or ULK1 signalling and cellular functions, the compound potently inhibits several other kinases and critical cellular functions such as glucose and nucleoside uptake. Our study demonstrates a role for the gatekeeper residue as a determinant of the inhibitor sensitivity and inhibitor-resistant mutant forms could be exploited as potential controls to probe specific cellular effects of SBI-0206965.

Toward an Understanding of Pan-Assay Interference Compounds and Promiscuity: A Structural Perspective on Binding Modes.

Pan-assay interference compounds (PAINS) are promiscuous compound classes that produce false positive hits in high-throughput screenings. Yet, the mechanisms of PAINS activity are poorly understood. Although PAINS are often associated with protein reactivity, several recent studies have shown that they also mediate noncovalent interactions. Aiming at a deep understanding of PAINS promiscuity, we performed an analysis of the Protein Data Bank to characterize the binding modes of PAINS. We explored the binding mode conservation of 34 PAINS classes present in 871 ligands and among 517 protein targets. The two major findings of this work are the following: First, different PAINS classes exhibit different levels of binding mode conservation. Our novel classification of PAINS based on binding mode similarity enables a rational assessment of PAINS from a structural perspective. Second, PAINS classes with variable binding modes can bind with high affinity. The evaluation of noncovalent binding modes of PAINS-like compounds sheds light on the mechanisms of promiscuous binding. Our findings could facilitate the decisions on how to deal with PAINS and help scientists to understand why PAINS produce hits in their screenings.

Machine learning reveals that structural features distinguishing promiscuous and non-promiscuous compounds depend on target combinations

Compounds with defined multi-target activity (promiscuity) play an increasingly important role in drug discovery. However, the molecular basis of multi-target activity is currently only little understood. In particular, it remains unclear whether structural features exist that generally characterize promiscuous compounds and set them apart from compounds with single-target activity. We have devised a test system using machine learning to systematically examine structural features that might characterize compounds with multi-target activity. Using this system, more than 860,000 diagnostic predictions were carried out. The analysis provided compelling evidence for the presence of structural characteristics of promiscuous compounds that were dependent on given target combinations, but not generalizable. Feature weighting and mapping identified characteristic substructures in test compounds. Taken together, these findings are relevant for the design of compounds with desired multi-target activity.

Analysis of Biological Screening Compounds with Single- or Multi-Target Activity via Diagnostic Machine Learning.

Predicting compounds with single- and multi-target activity and exploring origins of compound specificity and promiscuity is of high interest for chemical biology and drug discovery. We present a large-scale analysis of compound promiscuity including two major components. First, high-confidence datasets of compounds with multi- and corresponding single-target activity were extracted from biological screening data. Positive and negative assay results were taken into account and data completeness was ensured. Second, these datasets were investigated using diagnostic machine learning to systematically distinguish between compounds with multi- and single-target activity. Models built on the basis of chemical structure consistently produced meaningful predictions. These findings provided evidence for the presence of structural features differentiating promiscuous and non-promiscuous compounds. Machine learning under varying conditions using modified datasets revealed a strong influence of nearest neighbor relationship on the predictions. Many multi-target compounds were found to be more similar to other multi-target compounds than single-target compounds and vice versa, which resulted in consistently accurate predictions. The results of our study confirm the presence of structural relationships that differentiate promiscuous and non-promiscuous compounds.

Systematic Data Analysis and Diagnostic Machine Learning Reveal Differences between Compounds with Single- and Multitarget Activity.

Small molecules with multitarget activity are capable of triggering polypharmacological effects and are of high interest in drug discovery. Compared to single-target compounds, promiscuity also affects drug distribution and pharmacodynamics and alters ADMET characteristics. Features distinguishing between compounds with single- and multitarget activity are currently only little understood. On the basis of systematic data analysis, we have assembled large sets of promiscuous compounds with activity against related or functionally distinct targets and the corresponding compounds with single-target activity. Machine learning predicted promiscuous compounds with surprisingly high accuracy. Molecular similarity analysis combined with control calculations under varying conditions revealed that accurate predictions were largely determined by structural nearest-neighbor relationships between compounds from different classes. We also found that large proportions of promiscuous compounds with activity against related or unrelated targets and corresponding single-target compounds formed analog series with distinct chemical space coverage, which further rationalized the predictions. Moreover, compounds with activity against proteins from functionally distinct classes were often active against unique targets that were not covered by other promiscuous compounds. The results of our analysis revealed that nearest-neighbor effects determined the prediction of promiscuous compounds and that preferential partitioning of compounds with single- and multitarget activity into structurally distinct analog series was responsible for such effects, hence providing a rationale for the presence of different structure-promiscuity relationships.

Prediction of Promiscuity Cliffs Using Machine Learning.

Compounds with the ability to interact with multiple targets, also called promiscuous compounds, provide the basis for polypharmacological drug discovery. In recent years, a plethora of structural analogs with different promiscuity has been identified. Nevertheless, the molecular origins of promiscuity remain to be elucidated. In this study, we systematically extracted different structural analogs with varying promiscuity using the matched molecular pair (MMP) formalism from public biological screening and medicinal chemistry data. Care was taken to eliminate all compounds with potential false‐positive activity annotations from the analysis. Promiscuity predictions were then attempted at the level of compound pairs representing promiscuity cliffs (PCs; formed by analogs with large promiscuity differences) and corresponding non‐PC MMPs (analog pairs without significant promiscuity differences). To address this prediction task, different machine learning models were generated and the results were compared with single compound predictions. PCs encoding promiscuity differences were found to contain more structure‐promiscuity relationship information than sets of individual promiscuous compounds. In addition, feature analysis was carried out revealing key contributions to the correct prediction of PCs and non‐PC MMPs via machine learning.

Scopy: an integrated negative design python library for desirable HTS/VS database design

High-throughput screening (HTS) and virtual screening (VS) have been widely used to identify potential hits from large chemical libraries. However, the frequent occurrence of 'noisy compounds' in the screened libraries, such as compounds with poor drug-likeness, poor selectivity or potential toxicity, has greatly weakened the enrichment capability of HTS and VS campaigns. Therefore, the development of comprehensive and credible tools to detect noisy compounds from chemical libraries is urgently needed in early stages of drug discovery. In this study, we developed a freely available integrated python library for negative design, called Scopy, which supports the functions of data preparation, calculation of descriptors, scaffolds and screening filters, and data visualization. The current version of Scopy can calculate 39 basic molecular properties, 3 comprehensive molecular evaluation scores, 2 types of molecular scaffolds, 6 types of substructure descriptors and 2 types of fingerprints. A number of important screening rules are also provided by Scopy, including 15 drug-likeness rules (13 drug-likeness rules and 2 building block rules), 8 frequent hitter rules (four assay interference substructure filters and four promiscuous compound substructure filters), and 11 toxicophore filters (five human-related toxicity substructure filters, three environment-related toxicity substructure filters and three comprehensive toxicity substructure filters). Moreover, this library supports four different visualization functions to help users to gain a better understanding of the screened data, including basic feature radar chart, feature-feature-related scatter diagram, functional group marker gram and cloud gram. Scopy provides a comprehensive Python package to filter out compounds with undesirable properties or substructures, which will benefit the design of high-quality chemical libraries for drug design and discovery. It is freely available at https://github.com/kotori-y/Scopy.

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

A large set of multi-kinase inhibitors with high-confidence activity data was assembled and used to generate network representations revealing kinase relationships based upon shared inhibitors [1]. Compounds and activity annotations were originally selected from public repositories and organized in an in-house database from which the data set was extracted and curated. The new data set comprises more than 36,000 inhibitors with multiple activity annotations for a total of 420 human kinases (providing 81% coverage of the human kinome), representing a total of ∼127,000 kinase-inhibitor interactions. Use of the data is not limited to the network application reported in [1]. It can also be used, for example, for different types of compound promiscuity analysis or machine learning (such a multi-task modeling). In addition, the data set provides a large resource for complementing kinase drug discovery projects with external compound information.