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Atte Oksanen 1 , Bryan L. Drugs are sold on both dark web services and on social media, but research investigating these drug purchases online is still emerging. The aim of this study is to analyze risk factors associated with buying drugs online. Utilizing theories of criminology and addiction research, it was hypothesized that social bonds, low levels of self-control, and poor mental health are associated with buying drugs online. Additionally, it was predicted that purchases of drugs online would mediate the relationship between low self-control and regular drug use. Measures of impulsivity, a sense of mastery, social belonging, psychological distress, excessive behaviors drinking, gambling and internet use were utilized to predict purchasing drugs online. Two percent of the U. Results from multinomial logistic regression, penalized maximum-likelihood logistic regression, and binary mediation regression models indicated that buying drugs online was associated with lower self-control, higher psychological distress, and excessive gambling behavior and excessive Internet use. Having online friends was not a risk factor, but having strong social bonds with offline friends served as a protective factor. Additionally, buying drugs online mediated the relationship between low self-control and regular use of drugs. Results indicate that more focus should be placed on mainstream social media services as sources of drug acquisition as online drug buyers have multiple self-control and mental health problems. Este estudio tiene como objetivo examinar los factores de riesgo vinculados a la compra de drogas online. Cite this article as: Oksanen, A. Illegal drug use and trade are persistent public health and safety issues with serious consequences for individuals and societies. The strategies and contexts for the purchase of drugs evolved greatly in the past decade. Nowadays, the Internet offers easy access to legal and illegal activities taking place on open social media services and encrypted services that use, for example, the Tor network. Online drug dealing first caught public attention with the rise of the Silk Road, an online cryptomarket, which operated in Tor, in Barratt et al. Although the Internet is now a popular context for drug trade, research on this topic is still in its very early stages. It is crucial to discover if buying drugs online is prevalent and to describe risk and protective factors that can potentially be addressed to prevent this emerging problem behavior. Studies have shown that users of cryptomarkets are most commonly males in their 20s. Users of cryptomarkets prioritize them over street markets for quality reasons and personal safety Barratt et al. Cryptomarket buyers have been considered a technological drug subculture that practices online activism and libertarian ideology Maddox et al. Despite much of the focus on cryptomarkets, some indications show that people might use mainstream social media sites, such as Instagram, to buy drugs Marsh, Recently, a Nordic project used qualitative interviews and a digital ethnographic approach to analyze the phenomenon in Denmark, Finland, Iceland, Norway, and Sweden. Currently, the literature lacks studies using national datasets and our study aims to fill this gap by investigating buying drugs online from a cross-national perspective. Investigating this phenomenon is important due to the potential of online drug markets for easier access to drugs, which can amplify the various risks that substance use has for young people. Moreover, risk and protective factors for buying drugs online still need to be discovered. We argue that a combination of theories from criminology and addiction research could help researchers to understand the psychological and social risk factors related to buying drugs online. These perspectives include self-control, social bonds, and mental health. Thus, we expect that easy access to social media sources for acquiring drugs could lead to more opportunities to engage in daily use of drugs in individuals with low self-control. Furthermore, we expect that buying drugs online would mediate the relationship between low self-control and regular drug use. Learning takes place in different environments, including friendships of differing quality, frequency, and intensity. Perceived closeness or belonging to friends can have an influence on deviant behaviors. This is particularly the case on the Internet, where it is very easy to get access to harmful and deviant content and form social contacts with like-minded peers Keipi et al. However, strong ties with offline friends have been shown to buffer risky online behavior Kaakinen, Keipi, et al. Mental health refers to psychological, emotional, and social stability and wellbeing of individuals. Addiction research widely recognizes that mental health problems coexist and develop with excessive drug use Orford, One example of these problems is psychological distress i. Issues with mental health are also manifested in other types of addictive behaviors, which could further influence drug use. Drug use has high comorbidity with excessive alcohol consumption Grant et al. All these excessive behaviors are relatively prevalent among young people. In this study, we focused on factors related to buying drugs online, an understudied and emerging problem behavior among young people. Our first aim was to evaluate the prevalence of social media drug acquisition in a population of the United States and Spanish young people. These countries were selected due to their high percentage of drug use among young people ESPAD, ; Savolainen, These countries are otherwise comparable in the usage of Internet among young people Savolainen et al, and provide a good starting point for comparative research on buying drugs online. Our second aim was to investigate how drug acquisition online is associated with self-control, social bonds, and issues with mental health. We predicted that strong social bonds online, low self-control, and mental health issues such as psychological distress and excessive behavior, including excessive drinking, gambling and internet use, would be associated with drug use and buying drugs online. We also expected strong bonds offline to function as a protective factor against drug use and buying drugs online. The participants of the study were year-olds from the U. We recruited the U. Using such panels has become commonplace in social sciences and they are considered a good alternative due to the difficulty of recruiting participants by traditional means. The limitation of such datasets is that they are mostly restricted to people using the Internet Lehdonvirta et al. However, research panels have the benefit of getting access to hard-to-reach populations such as emerging adults in many countries. In our case, the data were collected using similar procedure via Dynata in both countries to guarantee the comparability of data samples. Comparisons of the country datasets to the U. Both samples were part of a larger international comparative project on young people and addictions. A survey designed in English and translated into Spanish included validated measures that have been widely used in comparative research. Additional items were translated by proficient English and Spanish speakers. The accuracy and comparability of items was guaranteed by the back-translation process in December The surveys were pre-tested with university students and mechanical Turk respondents. The surveys were conducted with LimeSurvey software that was run on the Tampere University server. Survey format and layout was identical for all respondents and optimized for both computers and mobile devices. Median response time for the survey was minutes in the U. Participation was anonymous, voluntary, and participants were informed about their right to withdraw from the survey at any time. The participants were informed about the project web page including all the necessary information in case the participants had any concerns regarding the study. The participants gave consent to make data open access and available for research purposes. All the participants that finalized the survey were included in the study and there were no missing data on the items used in the study. Drug use. We then asked to specify the types of drugs used and the frequency of the use. Drug types included 1 cannabis, 2 synthetic cannabinoids, LSD, magic mushrooms, or other comparable hallucinogens, 3 amphetamines, ecstasy, cocaine or other stimulants, 4 opiates, 6 pharmaceutical opioids, 7 gamma, GBL, and other similar drugs, and 8 other pharmaceuticals. The user types were then categorized into regular cannabis users and regular users of other drugs e. Drug purchases online. Next, respondents were asked to identify different online resources for purchasing drugs, including darknet marketplaces and various social media platforms such as Facebook, Instagram, online dating services, and general discussion forums. Self-control was measured with two different scales. Response options were in 4-point Likert scale giving scores from 1 to 4 per each item. Response options were EIS were no 0 and yes 1 in all questions. However, omega for impulsivity in Spain was only. All regression models are adjusted for age, gender, social media activity and country. Social bonds. We used belonging to friends online and offline as measures of social bonds. We asked respondents three questions about how strongly they felt they belonged to friendship groups, groups of school or work friends, or online communities. The scale was from 1 not at all to 10 very strongly. Question on belonging to online communities was used as a single item for online friends. These questions have been previously validated in studies on deviant online behavior Minkkinen et al. Additionally, we used the nine-item Identity Bubble Reinforcement scale IBRS-9 to measure perceived similarity and identification with other social media users Kaakinen, Sirola, et al. Mental health. We measured psychological distress with the item General Health Questionnaire GHQ , which has been widely used in general population studies Goldberg et al. All of these scales had good inter-item reliability see Table 1. Control factors. We used gender, age, and social media activity as controls. We measured social media activity with a set of 12 questions involving how often respondents used the most popular social media sites. Analyses for this study were run with Stata A multinomial regression analysis was carried out to examine the associations among the covariates, drug use, and buying drugs online. We used an aggregated U. The group of people who had not used drugs was set as the reference category for those who had used drugs but not bought them online and for those who had also bought them online. Table 3 reports additional analyses that were run including only the participants who had experimented with drugs U. These analyses were conducted by using penalized maximum likelihood logistic regression i. Using the Firth method provides more robust findings in cases when either sample size or events are low. Despite this, we aimed to keep the estimation strategy as robust as possible and utilized the Firth method. The analyses were run with the Firthlogit-command Coveney, and age, gender, and social media activity were used as controls. We also report chi-square tests for categorical variables and mean comparison based on Kruskal-Wallis test. OR s are based on penalized maximum likelihood logistic regression models. All regression models are adjusted for age, gender and social media activity. Mediation analysis Figures 1 and 2 was conducted with binary mediation command with a replication bootstrap. We used aggregated US-Spain data here due to the low number of people buying drugs online. Impulsivity and sense of mastery were independent variables, buying drugs online was the mediating variable, and regular drug use was the dependent variable. Mediation analysis included age and gender as controls. Of the respondents, about every fifth Cannabis was clearly the drug most experimented with by respondents with fewer respondents reporting use of other types of drugs. Out of the United States young people, 7. In Spain the numbers were slightly lower with respective figures of 6. In the U. Additionally, respondents were given the opportunity to indicate several services where they purchased drugs online. About half of all the respondents in both the U. Multinomial logistic regression analysis revealed that those buying drugs online reported more self-control issues a lower sense of mastery and higher impulsivity compared with non-users as well as those who had used drugs but not bought them online Table 2. Buying drugs online was associated with psychological distress and excessive forms of drinking, gambling, and Internet use. Additional analyses were conducted in order to check the robustness of the results. Table 3 shows the descriptive statistics comparing those who have bought drugs online and those who have only experimented with drugs. These results further confirm the findings shown in Table 2 , comparing only the participants who experimented with drugs with the participants who reported buying drugs online. Self-control factors were only statistically significant in the U. In Spain, belonging to online friends was associated with buying drugs online. All mental health factors remained significant in the descriptive findings Kruskal-Wallis test and in penalized maximum likelihood logistic regression models. The last part of the analysis investigated buying drugs online as a mediator between the relationship of low self-control and regular drug use see Figures 1 and 2. Statistically significant mediation was found. Figure 1 presents the coefficients when treating impulsivity as an independent variable. The indirect effect was statistically significant p Figure 2 presents the coefficients when treating sense of mastery as an independent variable. The indirect effect was statistically significant p This study analyzed the behavior of buying drugs online among young people in the U. Still, on average every tenth person who had experience using drugs had bought them online. Thus, a low percentage of users purchasing drugs online could represent a more developed drug trade, especially given that current research on online buying indicates that a large share is intended for reselling Demant et al. The most remarkable finding is that mainstream social media services, such as Facebook and Instagram, were used for buying drugs in both countries. The results underline that research on online drugs sales should not only focus on darknet services. From a broader perspective, the results are in line with current social media and cybercrime research underlining that mainstream public Internet platforms give easy access to varying types of illicit and harmful content Keipi et al. In addition to drugs, communities and contents that promote other forms of harmful or addictive behaviors, such as problem gambling, disordered eating, or self-harm, are easily accessible and among the most visited social media sites by youth Keipi et al. In our study, both impulsivity and a low sense of mastery were associated with both drug use and buying drugs online, especially in the U. This result highlights that researchers should continue investigating impulsivity in an online setting. This could partially explain why our results were stronger in the U. Also, results indicated that online drug purchases mediated the relationship between low self-control and regular drug use. These results are an important contribution to the literature, as previous studies described online buyers as technologically savvy users who can regulate themselves Barratt et al. In contrast to these studies, our results indicate that existing self-control problems can lead to spontaneous drug purchases that may later on worsen the potential problems with regular use of drugs. Social norms and group processes within online social networks could be important in many ways. However, in our study we did not find results related to the potential influence of friend groups online. The only exception was the result from Spain indicating that those who bought drugs online expressed higher belonging to online friends. Due to this difference from the U. For example, scholarship on online cliques and bubbles has shown that they vary culturally and topically Keipi et al. We found, however, evidence that strong offline social ties were a protective factor against both drug use and buying drugs online. This finding is in line with previous studies showing that positive offline social ties can buffer potential risks encountered online Kaakinen, Keipi, et al. These findings are also consistent with social control aspects noted in criminology LaFree et al. Those buying drugs online had multiple mental health problems, as they reported psychological distress as well as excessive forms of gambling and Internet use. These findings confirmed previous research results on the associations of drug use in general Edlund et al. Therefore, it would be misleading to portray users of online drug markets as only a technologically savvy and a self-controlled sub-culture. Our results indicate that these youth may have many mental health issues and comorbidity of different addictions. Our analysis was cross-sectional and limited to two countries. Future studies should continue investigating this phenomenon in other countries as well. Although our models included risk and protective factors, and a mediation analysis, on a strong theoretical basis, causal relations need to be confirmed in future longitudinal studies. Additionally, stronger measures of impulsivity should be explored. The strength of the study was that it used two nationwide samples, but additional studies in other cultures and contexts are needed. This is one of the first studies focused on buying drugs online, an emerging problem behavior that might be especially harmful given that it is very difficult to control online behaviors. Online drug buyers have multiple self-control and mental health problems, and drug availability online might worsen their situations. Impulsive decisions are especially easy to make on social media. In light of this, more focus should be placed on youth behavior on mainstream social media services. Implications for policy and practice underline the need to work with youth on their social media use, since young people spend a considerable amount of time online. Social media platforms are linked to a wide variety of deviant behavior Nasaescu et al. The wide availability of illicit drugs is a larger problem area that needs to be tackled through legal enforcement efforts, especially online. Most importantly, the results suggest that there is a need to provide therapeutic interventions and support for those youth buying drugs online. As strong offline social ties could help protect from drug-related risks and harms, it is necessary to promote face-to-face interactions among young people. Comprehensive school-based interventions against substance use should include components related to buying drugs online, increasing its protective factors and decreasing risks. 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Peer group identification as determinant of youth behavior and the role of perceived social support in problem gambling. Journal of Gambling Studies, 35 1 , Schieman, S. Religiosity, socioeconomic status, and the sense of mastery. Social Psychology Quarterly, 66 3 , Slatcher, R. A social psychological perspective on the links between close relationships and health. Current Directions in Psychological Science, 26 1 , Van Hout, M. International Journal of Drug Policy, 24 6 , Vazsonyi, A. Its time: A meta-analysis on the self-control-deviance link. Journal of Criminal Justice, 48 , Your request has been saved. The data we compile is analysed to improve the website and to offer more personalized services. By continuing to browse, you are agreeing to our use of cookies. For more information, see our cookies policy. January Pages 29 - Abstract Drugs are sold on both dark web services and on social media, but research investigating these drug purchases online is still emerging. Introduction Illegal drug use and trade are persistent public health and safety issues with serious consequences for individuals and societies. Mental Health Mental health refers to psychological, emotional, and social stability and wellbeing of individuals. This Study In this study, we focused on factors related to buying drugs online, an understudied and emerging problem behavior among young people. Method Participants The participants of the study were year-olds from the U. Table 1 Descriptive Statistics. Conflict of Interest The authors of this article declare no conflict of interest. References Akers, R. Orford, J. Excessive appetites: A psychological view of addictions. Introduction Method Results Discussion. Go top. PlumX Metrics. Your request has been saved Notify me when a new issue is online I have read and accept the information about Privacy. For more information, see our cookies policy Aceptar. Table 1 Descriptive Statistics Note. Procedure Both samples were part of a larger international comparative project on young people and addictions. Instruments Drug use. Data Analysis Analyses for this study were run with Stata Results Of the respondents, about every fifth The indirect effect was statistically significant p Discussion This study analyzed the behavior of buying drugs online among young people in the U.
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The authors wish it to be known that, in their opinion, the first two authors should be regarded as Joint First Authors. Drug discovery relies on the knowledge of not only drugs and targets, but also the comparative agents and targets. These include poor binders and non-binders for developing discovery tools, prodrugs for improved therapeutics, co-targets of therapeutic targets for multi-target strategies and off-target investigations, and the collective structure-activity and drug-likeness landscapes of enhanced drug feature. However, such valuable data are inadequately covered by the available databases. In this study, a major update of the Therapeutic Target Database, previously featured in NAR, was therefore introduced. This update includes a 34 poor binders and 12 non-binders of targets; b prodrug-drug pairs for targets; c co-targets of targets regulated by approved and clinical trial drugs; d the collective structure-activity landscapes of active agents of targets; e the profiles of drug-like properties of 33 agents of targets. Moreover, a variety of additional data and function are provided, which include the cross-links to the target structure in PDB and AlphaFold, and newly emerged targets and drugs, and the advanced search function for multi-entry target sequences or drug structures. Therapeutic target database update facilitating drug discovery with enriched comparative data of targeted agents. Drug discovery is promoted by not only the knowledge of drugs 1 and their therapeutic targets 2—4 , but also the comparative data with respect to other bioactive agents and other targets. Particularly, there is a rapid trend of the discovery of Artificial Intelligence AI tools for the drug discovery 14 , 15 , including the AI tools for identifying bioactive compounds, and the construction of such tools requires data of poor binders and non-binders of a specific target Thus, such comparative data above are urgently needed by researchers in drug discovery community. Moreover, the data of target's 3D structure are the key information for drug discovery 5. Apart from the increasing number of experimentally-resolved target crystal structures 17 , advanced AI technologies e. AlphaFold have enabled the prediction of target's crystal structures of high-confidence 18 , 19 , which requires the target-related databases, especially TTD, to include such valuable data. While the established databases provide the comprehensive information of both drugs and targets 20—24 , there is an inadequate coverage of the comparative data for the targeted agents and high-confidence 3D structures of human targets. These STs and CTs are targeted by approved, and clinical trial drugs, respectively. Meanwhile, additional structural data were updated, which included the cross-links to experimentally-resolved PDB structures and AlphaFold-generated structures; and and newly emerged targets and drugs were also collected. Table 1 gave the statistics of targets and drugs among different database versions, and Table 2 summarized the new features and their corresponding statistics updated to the latest database. Moreover, the schema, search engine, and adopted ontology of this database were also provided in the TTD website. Accumulation of drugs and their corresponding targets in the latest and previous versions of TTD database. New features and their corresponding statistics added to the TTD. These new features included structure-based activity landscape of targets, profile of drug-like properties of studied targets, prodrugs together with their parent drug and target, co-targets modulated by approved or clinical trial drugs, and the poor binders and non-binders of targets. Molecular docking is a widely-used structure-based drug discovery method 17 , which employs scoring functions for scoring the binding of molecules to a target site Poor binders and non-binders are useful decoy molecules for the development of the scoring functions 6. AI methods have also been extensively explored to develop bioactive molecule and pharmaceutical property screening tools, which have been primarily trained by actives e. In other words, it is essential to have a conveniently-accessible resource for poor binders and non-binders of the therapeutic targets. Second, these PubMed literatures were manually checked to discover those containing the molecule with experimentally measured quantitative activity against any target of interest. Using the above criteria, a total of 34 poor binders and 12 non-binders were collected for and STs, and CTs, and 91 PTs, and RTs, respectively. Good therapeutic drugs possess not only potent activities but also desirable pharmacokinetic and toxicological properties In some cases, the drug leads may possess potent activity but poor pharmacokinetic property, which could be overcome using the prodrug strategy 8. Prodrugs are molecules modified from the parent drugs, with little or no activity but the good pharmacokinetic property, which are converted into active parent drugs inside human body via enzymatic or other process 8. Such strategy helps overcome drug discovery challenges that limit pharmacokinetic performances and drug formulation option. For instance, the prodrugs Ivemend and Gilenya were reported to improve solubility and enhance permeation, respectively 5. Second, these literatures were manually checked to discover those containing the information of prodrug and its parent drug. Third, detailed data of a prodrug were retrieved from the literatures, which included disease indication, clinical status, prodrug strategy, improved property, bioconversion mechanism, etc. Fourth, the structures of the prodrug and its parent drug were drawn using ChemDraw based on the structures reported in each corresponding literature. As shown in Figure 1 , both the detailed data and structures of prodrugs were explicitly described in the TTD prodrug page. A typical page in TTD providing prodrug information. The structures of both prodrug and its parent drug are provided along with the bioconversion enzyme or condition. Structural variation between prodrug and parent drug is highlighted in orange. The strategy for prodrug design, and the enhancement in the pharmaceutical property from parent drug to its prodrug are also described. Many drugs are known to interact with more macromolecular targets than their intended primary therapeutic target. In particular, a multi-target drug produces its therapeutic effect by modulating multiple targets 9. Some clinical trial drugs have been found to produce their therapeutic effects via interacting with off-targets, i. On the one hand, such beneficial effects of off-target have been explored for drug repurposing against complex diseases 36—39 ; on the other hand, off-target activity may in some instances lead to undesirable effect Based on multiple targets of drugs, one can define the co-targets of a therapeutic target as the additional targets of all drugs targeting the therapeutic target. In other words, these co-targets represent both the targets co-modulated by a multi-target drug 5 and the off-target of a drug Second, all these literatures were manually checked to discover those having the information of co-targets, and the drugs of clinical importance approved or clinical trials that co-regulating a therapeutic target and its co-targets were also identified from literatures, company reports, and other official resources providing drug-target information. Third, detailed data of each co-target were collected to TTD and cross-linked to other reputable databases e. As a result, co-targets of STs and CTs co-modulated by approved and clinical trial drugs were identified and collected for this update. In the design of drugs against individual target, the molecular structure of the hit against a target first molecule found to bind to the target should be modified to optimize target binding activity 42 , Those modified molecules, particularly the structural derivatives of a hit, largely follow certain structure-activity relationship 44 , and can also lead to the dramatical activity variations, namely activity cliff 12 , 45 , Such structure-activity relationships can be further evaluated by the collective structure-activity landscape of all known binders of studied target. As described in Figure 2 , all known binders of a target were clustered based on their structural similarities, each binder was represented by a colored bar with its height proportional to the level of target binding activity —log IC 50 , —log Ki, etc. The clustering of all binders of target was constructed using the sequential steps as follows. First, the molecular fingerprints of all binders were computed using R package ChemmineR Second, the Tanimoto coefficient-based similarities among binders were computed by ChemmineR Third, the complete linkage hierarchical clustering based on Euclidean distance 48 was adopted to cluster all target binders. A typical plot in TTD showing the chemical structure-based activity landscape for a target. All known drugs of a target are clustered based on their structural similarity. Moreover, the binding activity e. The color of the bar indicates the highest clinical status of the corresponding drug approved, clinical trial, etc. Users can move the mouse over the bar to get the basic information status, PubChem CID, activity, etc. The detailed drug data can be found by clicking that particular drug. Such collective structure-activity landscape of individual target is, to the best of our knowledge, unique in the following aspects. First, each landscape in TTD is dedicated to all drugs and other binders of individual therapeutic target. Such target-specific landscape provides the overview of the structural similarity among all target-specific binders, which could help the readers to gain a quick understanding of all available binding scaffolds of a studied target. Second, such landscape gives the activities of all drugs and binders for a target along with their structural characteristics, which is useful for describing QSARs and activity cliffs. Third, this provided landscape includes the valuable information of each drug's clinical status, which demonstrated a unique perspective illustrating the relationships between drug structures and clinical development stages. Therefore, such collective structure-activity landscape of individual therapeutic target provided in TTD was of great merit for modern drug discovery. The potential of a bioactive molecule to become a drug is partly judged by the evaluation of its drug-like properties 13 , Such rules exploit drug's distinguished physicochemical property, including molecular weight and the number of hydrogen bond donors, as the basis for drug-likeness evaluations The value of these drug-like properties may vary from the drugs of one target to those of another. Therefore, target-specific profiles of drug-like property may be useful for facilitating the analysis of the landscape of drug-like property for targeted therapeutics As illustrated in Figure 3 , the 2D profiles of the target-specific drug-like properties for those targets in TTD were provided. Particularly, all known drugs of a target were clustered based on multiple the top plot in Figure 3 or single six plots at the bottom of Figure 3 drug-like properties, which was displayed using the hierarchical clustering map, heatmap and bar plot. The bar color indicates the highest clinical status of the corresponding drugs approved, clinical trial, etc. Users can move the mouse over the bar to find the basic information status, PubChem CID, property, etc. Within each graph, the known drugs of a target were clustered according to their similarities in drug-like properties, which was constructed by a process similar to that described in previous section. Each drug was represented by a vertical line with the amplitude proportional to the values of drug-like property. A typical plot in TTD providing the information of the drug-like property-based profile for a target. All known drugs of a target are clustered based on multiple the top plot or single six plots at the bottom drug-like properties, which is displayed using the hierarchical clustering map, heatmap and bar plot. The detailed drug data can be found by clicking that drug. The structures of macromolecules are important for drug discovery 56 and protein engineering or design With the availability of target's 3D structures, one can employ the structure-based drug discovery methods such as molecular docking 56 , 58 , 3D QSAR 59 , 60 , structure-based pharmacophore 61 and molecular dynamics simulation 62 to identify the binders of specific target Recent progress of AI technique like AlphaFold have enabled high-confidence prediction of protein 3D structures for most human proteins AlphaFold employs a deep learning architecture to predict the 3D structure of a protein from its sequence Thus, the AlphaFold-generated 3D structures could greatly expand the range of targets covered by structure-based drug discovery methods To have a convenient access of the structures for each TTD target, the crosslinks to PDB providing experimentally-resolved crystal structure and AlphaFold describing the predicted 3D structure were reviewed and provided in TTD, which helped to link targets to their structure data. Sequence similarity searching is the search of proteins with similar sequences to a known target, which is useful for identifying potential targets 65 and tracing protein evolution It is based on the hypothesis that proteins of similar sequences have similar functions Drug similarity searching is the search of small molecules with similar structures as that of a known drug, which is useful for finding molecules with similar activities or drug-like properties TTD and other databases 41 , 69 have already provided target similarity and drug similarity searching facilities. Nonetheless, during practical applications, multiple proteins or chemical libraries are frequently searched and analyzed for the potential target and bioactive molecule. In other words, there is a need for the facilities that can support multi-entry target and drug similarity searching. Therefore, a multi-entry target similarity searching and a multi-entry drug similarity searching facility was introduced, where the users can upload a file of multiple protein sequences or multiple molecular structures for finding TTD targets or drugs that are similar in sequence or structure. Input with one protein sequence or a batch upload of multiple sequences for similarity search is now available in the latest version of TTD. Moreover, the drug similarity searching is based on Tanimoto coefficients. With the rapid advances in modern drug discovery 71—75 , there is an explosion of publications on revealing the mechanism underlying both disease and therapeutics 76—78 , which in turn lead to the accumulation of huge amount of data for drug discovery. The expanded coverage of these data in TTD and other established databases collectively provide the enriched resources for drug discovery and the development of drug identification tool. The enriched data further enhance the ability to analyze and explore these derived data. Drug discovery efforts have benefited from this cycle of technology advancements, expanded knowledge and data, enhanced capabilities for the exploration of these derived data, and the advancements to the next round of the cycle. TTD and other established databases 79—81 will continue to update the new pharmaceutical data and play enhanced facilitating roles in current drug discovery efforts. Shih H. Drug discovery effectiveness from the standpoint of therapeutic mechanisms and indications. Drug Discov. Google Scholar. Santos R. A comprehensive map of molecular drug targets. Licursi V. BMC Bioinformatics. Yin J. Identification of the key target profiles underlying the drugs of narrow therapeutic index for treating cancer and cardiovascular disease. Bajusz D. Exploring protein hotspots by optimized fragment pharmacophores. Seeliger D. Aided Mol. Xue W. 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Protein functional annotation of simultaneously improved stability, accuracy and false discovery rate achieved by a sequence-based deep learning. Shen W. Out-of-the-box deep learning prediction of pharmaceutical properties by broadly learned knowledge-based molecular representations. Burley S. RCSB Protein Data Bank: powerful new tools for exploring 3D structures of biological macromolecules for basic and applied research and education in fundamental biology, biomedicine, biotechnology, bioengineering and energy sciences. Nucleic Acids Res. Jumper J. Highly accurate protein structure prediction with AlphaFold. Tunyasuvunakool K. Highly accurate protein structure prediction for the human proteome. Wishart D. DrugBank 5. Wang Y. Therapeutic target database enriched resource for facilitating research and early development of targeted therapeutics. Avram S. DrugCentral supports drug discovery and repositioning. Armstrong J. Yang Q. Warren G. A critical assessment of docking programs and scoring functions. Xue Y. Effect of molecular descriptor feature selection in support vector machine classification of pharmacokinetic and toxicological properties of chemical agents. Han L. A support vector machines approach for virtual screening of active compounds of single and multiple mechanisms from large libraries at an improved hit-rate and enrichment factor. Zhavoronkov A. Deep learning enables rapid identification of potent DDR1 kinase inhibitors. Bender A. Artificial intelligence in drug discovery: what is realistic, what are illusions? Part 2: a discussion of chemical and biological data. Thorndike J. Inhibition of glycinamide ribonucleotide formyltransferase and other folate enzymes by homofolate polyglutamates in human lymphoma and murine leukemia cell extracts. Cancer Res. Wang B. Specific inhibition of cyclic AMP-dependent protein kinase by warangalone and robustic acid. Beckmann-Knopp S. Inhibitory effects of silibinin on cytochrome P enzymes in human liver microsomes. Kwon J. Cis-fumagillin, a new methionine aminopeptidase type 2 inhibitor produced by Penicillium sp. Sayers E. Database resources of the national center for biotechnology information. Roberts S. Drug metabolism and pharmacokinetics in drug discovery. Fiscon G. PLoS Comput. Kumar S. Repurposing of FDA approved ring systems through bi-directional target-ring system dual screening. Tang J. Simultaneous improvement in the precision, accuracy, and robustness of label-free proteome quantification by optimizing data manipulation chains. Lounkine E. Large-scale prediction and testing of drug activity on side-effect targets. UniProt, C. UniProt: the universal protein knowledgebase in Imidazolopiperazines: hit to lead optimization of new antimalarial agents. Teli M. In silico identification of prolyl hydroxylase inhibitor by per-residue energy decomposition-based pharmacophore approach. Martinez A. SAR and 3D-QSAR studies on thiadiazolidinone derivatives: exploration of structural requirements for glycogen synthase kinase 3 inhibitors. Systematic exploration of activity cliffs containing privileged substructures. Activity cliffs produced by single-atom modification of active compounds: Systematic identification and rationalization based on X-ray structures. Cao Y. ChemmineR: a compound mining framework for R. Kim M. Association between fever pattern and clinical manifestations of adult-onset Still's disease: unbiased analysis using hierarchical clustering. Bostock M. D 3 : data-driven documents. IEEE Trans. Leeson P. The influence of drug-like concepts on decision-making in medicinal chemistry. Lipinski C. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Lead- and drug-like compounds: the rule-of-five revolution. Today Technol. SSizer: determining the sample sufficiency for comparative biological study. Drug-like properties and the causes of poor solubility and poor permeability. Gorgulla C. An open-source drug discovery platform enables ultra-large virtual screens. Taujale R. Deep evolutionary analysis reveals the design principles of fold A glycosyltransferases. Friesner R. Extra precision glide: docking and scoring incorporating a model of hydrophobic enclosure for protein-ligand complexes. Verma J. Huang L. Quantitative structure-mutation-activity relationship tests QSMART model for protein kinase inhibitor response prediction. Rella M. Structure-based pharmacophore design and virtual screening for novel angiotensin converting enzyme 2 inhibitors. Herrera-Nieto P. Small molecule modulation of intrinsically disordered proteins using molecular dynamics simulations. Lee S. Comparing a query compound with drug target classes using 3D-chemical similarity. Skalic M. From target to drug: generative modeling for the multimodal structure-based ligand design. Clinical trials, progression-speed differentiating features and swiftness rule of the innovative targets of first-in-class drugs. Kwon A. Tracing the origin and evolution of pseudokinases across the tree of life. Whisstock J. Prediction of protein function from protein sequence and structure. Azad A. A comprehensive integrated drug similarity resource for in-silico drug repositioning and beyond. Kim S. PubChem in new data content and improved web interfaces. Yap C. PaDEL-descriptor: an open source software to calculate molecular descriptors and fingerprints. Failli M. Paananen J. An omics perspective on drug target discovery platforms. Fortino V. Machine-learning-driven biomarker discovery for the discrimination between allergic and irritant contact dermatitis. ANPELA: analysis and performance assessment of the label-free quantification workflow for metaproteomic studies. Naveja J. Exploration of target synergy in cancer treatment by cell-based screening assay and network propagation analysis. Jimenez J. PathwayMap: molecular pathway association with self-normalizing neural networks. Consistent gene signature of schizophrenia identified by a novel feature selection strategy from comprehensive sets of transcriptomic data. Therapeutic target database update enriched resource for facilitating bench-to-clinic research of targeted therapeutics. Oxford University Press is a department of the University of Oxford. It furthers the University's objective of excellence in research, scholarship, and education by publishing worldwide. Sign In or Create an Account. Sign in through your institution. NAR Journals. Advanced Search. Search Menu. Article Navigation. Close mobile search navigation Article Navigation. Volume Article Contents Abstract. Journal Article. Ying Zhou , Ying Zhou. Oxford Academic. Yintao Zhang. College of Pharmaceutical Sciences, Zhejiang University. Xichen Lian. Fengcheng Li. Chaoxin Wang. Feng Zhu. To whom correspondence should be addressed. Yunqing Qiu. Correspondence may also be addressed to Yunqing Qiu. Email: qiuyq zju. Yuzong Chen. Correspondence may also be addressed to Yuzong Chen. Email: chenyuzong sz. Revision received:. Select Format Select format. Permissions Icon Permissions. Abstract Drug discovery relies on the knowledge of not only drugs and targets, but also the comparative agents and targets. Graphical Abstract. Open in new tab Download slide. Table 1. Open in new tab. TTD statistics for targets and drugs. Table 2. Figure 1. Figure 2. Figure 3. Google Scholar Crossref. Search ADS. For commercial re-use, please contact journals. Issue Section:. Download all slides. Comments 0. Add comment Close comment form modal. I agree to the terms and conditions. You must accept the terms and conditions. Add comment Cancel. Submit a comment. Comment title. You have entered an invalid code. Submit Cancel. 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