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However, such invaluable data were inadequately covered by existing databases. The efficacy and safety of a drug are substantially determined by its pharmaceutical properties and interactions with various pharmacologically important molecules 1—3. The emergence of acquired drug resistance has amplified the need for new therapeutics, which is challenged by the rising costs and failure rates in drug discovery 5—7. In this context, there are great research interests in the discovery of combinatorial therapies 8—10 , realization of drug repurposing 11—13 , and explanation of off-target adverse drug reactions 14—16 , which highlights the demands for the invaluable molecular atlas and pharma-information of drug combinations, repurposed drugs and ADR-associated off-targets 17— Till now, several reputable data repositories related to the aforementioned topic have been developed. However, none of the existing databases provides the interacting atlas of either combinatorial or repurposed drugs at the molecular level, and the detailed pharma-information of these drugs is largely absent from these databases. Moreover, the molecular mechanism especially, the off-target information of the studied drug underlying each ADR has not been described by any of those available databases including the original version of DrugMAP. First, a total of combinatorial drugs CBDs , including approved, clinical trials, and investigative ones, were collected, and their interacting atlases with molecules of clinical importance were provided. Third, a total of off-targets DOTs of literature-reported ADR for drugs, including approved, clinical trial, and investigative ones, were also covered, and their corresponding molecular atlas was described. Finally, diverse pharma-information was added during this update, which included: a diverse ADMET absorption, distribution, metabolism, excretion, and toxicity properties, such as bioavailability, Lipinski Role of 5 RO5 violation, half-life, clearance, elimination, and distribution; b versatile disease webpages illustrating disease hierarchy based on ICD of World Health Organization , and MONDO of U. An overview of this update of DrugMAP. In previous version, the molecular atlas was primarily composed of the pharmacokinetic data of medications blue and their interactions with three types of molecules: drug therapeutic target DTT, orange , drug transporter DTP, red , and drug-metabolizing enzyme DME, green. The use of combinatorial drug, which involves the co-administration of multiple agents targeting different pathways, can extensively elevate therapeutic efficacy 40 , reduce dosage requirement 41 , and delay the onset of resistance This approach has become a standard clinical strategy for treating multifactorial disease that involves multiple pathways, like cancer 43 , autoimmune 44 and cardiovascular conditions However, the large space of possible drug combinations and the high costs and resources required to test these therapeutic strategies in clinical trial, make it impractical to experimentally assess all possible combinations Moreover, the overlapping toxicity of drug combinations and drug-drug interactions were common problems currently faced in discovering combinatorial therapies 47 , To bridge the gaps, various combinatorial drugs as well as their interacting atlas were collected and normalized based on different data resources. The drug combinations approved by the U. FDA are definitely supported by successful clinical trial data 55 , while those that have been or are currently being tested in clinical trials must also have enough experimental evidence to justify their applications. These drug combinations were systematically curated for DrugMAP using the following procedures. All drug combinations were filtered according to the types of ingredients, dosage forms, routes of administration, and strengths, finally resulting in a total of approved combinations. Second, the search for combinatorial drugs in clinical trial was performed based on ClinicalTrials. Third, the retrieved combinations were refined based on the names of the components as well as the disease indications, leading to approved and clinical trial combinations. In addition to those drug combinations catalogued by the U. Food and Drug Administration U. FDA and ClinicalTrials. To further expand the coverage of drug combinations in DrugMAP , investigative combinations were systematically collected by the following procedure. Second, the drug combinations of clearly reported therapeutic effects were manually collected from the literature, and the biochemical assays were recorded. Third, based on the dose-response landscapes of biochemical assays, quantitative synergy scores were calculated using a variety of models, including the Highest Single Agent , Loewe additivity , Bliss independence and Zero Interaction Potency The synergy score was normalized using Min-Max Scaling and visualized with unified heatmaps as illustrated in Figure 2B. Finally, the interacting atlas of all CBDs were mapped and provided based on the molecular interactions of component drugs as shown in Figure 2C. A general information of drug, such as drug name, synonyms and therapeutic class; B the list of CBDs containing this drug, at the top of this module, a heatmap of the normalized CBD synergy score is provided, the color of which was determined by the synergy score calculated from the HSA, Loewe, Bliss and ZIP models normalized via Min-Max Scaling; C detailed CBD page, opened by clicking on the CBD ID, provided details on the molecular atlas and the structures of the constituent drugs. Drug repurposing that identifies new therapeutic use for existing drugs, holds significant promise in accelerating the drug developments process and reducing associated costs and timeframes This strategy leverages the known safety profile and pharmacokinetic property of approved drugs 63 , 64 , thus circumventing the initial stages of drug development that involve substantial time and financial resources However, the discovery of repurposed drugs is greatly challenged by various potential therapeutic targets and unclear disease mechanisms 66— Moreover, the repurposed drugs must be comprehensively evaluated using interaction networks among multiple diseases 69 , to mitigate the risks of unforeseen ADRs and interactions Thus, the molecular atlas of existing repurposed drugs can facilitate the discovery of new ones. The information on repurposed drugs and their disease was collected and confirmed through the following steps. First, drug indications in DrugMAP are comprehensively reviewed, and the list of drugs treating multiple diseases was collected. Second, these drugs were then strictly screened and de-emphasized based on the disease class and the ICD code the first two digits to ensure that the remaining drugs were the repurposed ones. Finally, the repositioning profiles of approved drugs were collected and shown, and a total of drugs that were tested for multiple diseases in clinical trial were also described. A list of repurposed diseases and the molecular atlas of studied repurposed drugs were provided on a separate webpage as shown in Figure 3A. A a typical RPD page provided the general information of drugs, and illustrated a list of repurposed diseases as well as the molecular atlas of RPD consisting of repurposed drugs; B a disease page, accessed by clicking on disease ID, which not only provided the name, definitions, and hierarchy of disease, but also described information about the mapping of the disease ontology to reputable existing databases. The molecular interaction atlas of the disease was also provided. Disease was undoubtedly a crucial part of drug repositioning Therefore, diseases were added as one of the key nodes into DrugMAP , which substantially enriched the molecular atlas together with disease-molecule association. First, disease ontologies were mapped to available databases, such as Orphanet 72 , MedGen 73 , and HPO 74 , through text mining and keyword matching. Second, the literature-reported associations between disease and proteins were also curated from multiple resources, such as GenCC 44 , DisGeNET 75 and KEGG 76 , and further integrated into the molecular atlas of repurposed drug. Each association between diseases and proteins was determined by considering multiple factors, including data sources, expert assessment, the quantity and quality of supporting literature, and the presence of conflicting results. These factors work together to help researchers assess the credibility and clinical relevance of gene-disease associations. Third, the protein-protein interactions related to the DrugMAP molecules were collected to enrich the molecule atlas of repurposed drug. The disease information such as disease name, definition, hierarchy, ontology mapping, and interacting atlas was also provided on a separate webpage for disease as illustrated in Figure 3B. This highlighted the necessity of capitalizing on the broad target selectivity of drugs and harnessing beneficial off-target effects In other words, the valuable information on the ADRs of drugs and their associated off-targets would provide strong support for the discovery of novel therapeutic targets and the avoidance of undesirable adverse drug reactions 83— Second, the newly identified studies were systematically validated, and reliable interactions were extracted. Third, off-targets influencing their own post-translational modification, biochemical pathway, and cellular process through interactions with drugs were also collected. As a result, a total of DOTs associated with literature-reported ADR for drugs including approved, in clinical trials and investigative were compiled. Moreover, 15 DOTs with a clearly defined mode of action were included. Comprehensive information on these DOTs, including their functions, structures, and associated pathways, was provided on a separate webpage for DOTs as illustrated in Figure 4A. The corresponding molecular atlases are also presented as shown in Figure 4B. A the general information of each DOT; B the molecular atlas module of DOT, which not only provided a detailed list of drugs affected by the DOT and those drugs affecting the DOT and their modes of action, but also provided an interactive interaction network where the user could hover the mouse over the nodes and the interactions to view detailed information about the molecules and interactions. A variety of emerging drugs were systematically integrated into the latest DrugMAP. First, drugs approved during the past two years DrugMAP was first released in were manually curated from recent official reports 86 , Second, the latest information on clinical trial drugs together with their clinical status was gathered and updated using timely data from ClinicalTrials. Third, for each drug, detailed information on its interacting molecules was further compiled. As a result, the number of drugs collected to DrugMAP had expanded from to , and the number of drug-interacting molecules had increased from to Furthermore, the additional pharma-information of drug was significantly enriched in this update. Specifically, the RO5 violations for drugs were confirmed using the PubChem 88 database and visualized using a radar chart to facilitate quick overview. The ADMET absorption, distribution, metabolism, excretion and toxicity characteristics of drugs had also been substantially enriched Particularly, a total of 15 ADME characteristics bioavailability, metabolism, clearance, elimination, half-life, etc. Additionally, comprehensive information on ADRs and ADR-associated off-targets had been integrated into the enriched drug pharma-information. Detailed descriptions of the general and pharma-information of drugs in DrugMAP were illustrated in Figure 5. The schematic representation of the enriched pharma-information of drug in DrugMAP. A the general information of a medication, which included drug name, synonym, structure and so on; B a comprehensive list of diverse ADMET features of the drugs, providing an exhaustive description of the drug's 15 ADMET attributes such as absorption, bioavailability, clearance and so on, along with the corresponding literatures; C detailed information regarding the ADRs and ADR-associated DOTs of this particular drug. The DrugMAP has been previously developed to provide comprehensive and accurate molecular atlas for all drugs. By leveraging this extensive network, AI algorithms and models can more effectively analyze and predict the interactions, synergistic effects and potential repurposing opportunities of drugs, ultimately enhancing the efficiency and success rates of drug discovery endeavors. For instance, some studies have successfully applied the executable signaling network model to predict novel combinatorial drugs for treating COVID 90 , and some others have constructed AI tools to discover potential repurposing and combinatorial drug for Alzheimer's disease Due to the great demand for interacting networks in drug combination and repositioning 92—94 , the DrugMAP further expanded the richness and availability of the molecular atlas by introducing various additional nodes of CBDs, RPDs, DOTs and diseases, which was expected to provide insights for the development of novel combinatorial therapeutics and drug repositioning strategies at the molecular and network level 95— The pharma-information for all drugs in DrugMAP was crucial for drug developments. Therefore, DrugMAP was committed to keep updating and enriching its pharma-information. Furthermore, drug disease information had also been incorporated, providing invaluable insights into target discovery and drug repurposing, which encompassed disease synonyms, classification, definitions, hierarchies, ontology mappings and protein associations and so on so forth. All in all, considering the growing demand for the discovery of combinatorial therapies and drug repurposing, as well as the rapid developments of AI-assisted drug discovery technologies, drug-centered multimolecular interaction network promised to be an indispensable and important data source. Therefore, DrugMAP was expected to continually emerge as a popular data resource, and to serve as an essential supplement to existing pharmaceutical databases. Labanieh L. Enhanced safety and efficacy of protease-regulated CAR-T cell receptors. Google Scholar. Zhou Y. Nucleic Acids Res. Yin J. DrugMAP: molecular atlas and pharma-information of all drugs. Jin H. 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Data availability. Journal Article. DrugMAP 2. Fengcheng Li , Fengcheng Li. Oxford Academic. Minjie Mou. Xiaoyi Li. Weize Xu. Jiayi Yin. Correspondence may also be addressed to Jiayi Yin. Email: yinjiayi zju. Yang Zhang. School of Pharmacy, Hebei Medical University. Correspondence may also be addressed to Yang Zhang, Email: zhangyang hebmu. Feng Zhu. To whom correspondence should be addressed. The first two authors should be regarded as Joint First Authors. Revision received:. Select Format Select format. Permissions Icon Permissions. Graphical Abstract. Open in new tab Download slide. Figure 1. Figure 2. Figure 3. Figure 4. Figure 5. Google Scholar Crossref. Search ADS. Hechtelt Jonker. Google Scholar PubMed. Lotfi Shahreza. For commercial re-use, please contact reprints oup. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site-for further information please contact journals. Issue Section:. Download all slides. 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