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      DrugMAP: molecular atlas and pharma-information of all drugs

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          Abstract

          The efficacy and safety of drugs are widely known to be determined by their interactions with multiple molecules of pharmacological importance, and it is therefore essential to systematically depict the molecular atlas and pharma-information of studied drugs. However, our understanding of such information is neither comprehensive nor precise, which necessitates the construction of a new database providing a network containing a large number of drugs and their interacting molecules. Here, a new database describing the molecular atlas and pharma-information of drugs (DrugMAP) was therefore constructed. It provides a comprehensive list of interacting molecules for >30 000 drugs/drug candidates, gives the differential expression patterns for >5000 interacting molecules among different disease sites, ADME (absorption, distribution, metabolism and excretion)-relevant organs and physiological tissues, and weaves a comprehensive and precise network containing >200 000 interactions among drugs and molecules. With the great efforts made to clarify the complex mechanism underlying drug pharmacokinetics and pharmacodynamics and rapidly emerging interests in artificial intelligence (AI)-based network analyses, DrugMAP is expected to become an indispensable supplement to existing databases to facilitate drug discovery. It is now fully and freely accessible at: https://idrblab.org/drugmap/

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          Graphical Abstract

          DrugMAP provides a comprehensive list of interacting molecules for >30 000 drugs/drug candidates, illustrates the differential expression pattern of >5000 interacting molecules among disease sites, ADME-related organs or physiological tissues, and weaves a comprehensive and precise network containing >200 000 interactions among drugs and molecules.

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          Most cited references127

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          limma powers differential expression analyses for RNA-sequencing and microarray studies

          limma is an R/Bioconductor software package that provides an integrated solution for analysing data from gene expression experiments. It contains rich features for handling complex experimental designs and for information borrowing to overcome the problem of small sample sizes. Over the past decade, limma has been a popular choice for gene discovery through differential expression analyses of microarray and high-throughput PCR data. The package contains particularly strong facilities for reading, normalizing and exploring such data. Recently, the capabilities of limma have been significantly expanded in two important directions. First, the package can now perform both differential expression and differential splicing analyses of RNA sequencing (RNA-seq) data. All the downstream analysis tools previously restricted to microarray data are now available for RNA-seq as well. These capabilities allow users to analyse both RNA-seq and microarray data with very similar pipelines. Second, the package is now able to go past the traditional gene-wise expression analyses in a variety of ways, analysing expression profiles in terms of co-regulated sets of genes or in terms of higher-order expression signatures. This provides enhanced possibilities for biological interpretation of gene expression differences. This article reviews the philosophy and design of the limma package, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously described.
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            Highly accurate protein structure prediction with AlphaFold

            Proteins are essential to life, and understanding their structure can facilitate a mechanistic understanding of their function. Through an enormous experimental effort 1 – 4 , the structures of around 100,000 unique proteins have been determined 5 , but this represents a small fraction of the billions of known protein sequences 6 , 7 . Structural coverage is bottlenecked by the months to years of painstaking effort required to determine a single protein structure. Accurate computational approaches are needed to address this gap and to enable large-scale structural bioinformatics. Predicting the three-dimensional structure that a protein will adopt based solely on its amino acid sequence—the structure prediction component of the ‘protein folding problem’ 8 —has been an important open research problem for more than 50 years 9 . Despite recent progress 10 – 14 , existing methods fall far short of atomic accuracy, especially when no homologous structure is available. Here we provide the first computational method that can regularly predict protein structures with atomic accuracy even in cases in which no similar structure is known. We validated an entirely redesigned version of our neural network-based model, AlphaFold, in the challenging 14th Critical Assessment of protein Structure Prediction (CASP14) 15 , demonstrating accuracy competitive with experimental structures in a majority of cases and greatly outperforming other methods. Underpinning the latest version of AlphaFold is a novel machine learning approach that incorporates physical and biological knowledge about protein structure, leveraging multi-sequence alignments, into the design of the deep learning algorithm. AlphaFold predicts protein structures with an accuracy competitive with experimental structures in the majority of cases using a novel deep learning architecture.
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              NCBI GEO: archive for functional genomics data sets—update

              The Gene Expression Omnibus (GEO, http://www.ncbi.nlm.nih.gov/geo/) is an international public repository for high-throughput microarray and next-generation sequence functional genomic data sets submitted by the research community. The resource supports archiving of raw data, processed data and metadata which are indexed, cross-linked and searchable. All data are freely available for download in a variety of formats. GEO also provides several web-based tools and strategies to assist users to query, analyse and visualize data. This article reports current status and recent database developments, including the release of GEO2R, an R-based web application that helps users analyse GEO data.
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                Author and article information

                Contributors
                Journal
                Nucleic Acids Res
                Nucleic Acids Res
                nar
                Nucleic Acids Research
                Oxford University Press
                0305-1048
                1362-4962
                06 January 2023
                16 October 2022
                16 October 2022
                : 51
                : D1
                : D1288-D1299
                Affiliations
                College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University , Hangzhou 310058, China
                Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba–Zhejiang University Joint Research Center of Future Digital Healthcare , Hangzhou 330110, China
                College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University , Hangzhou 310058, China
                College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University , Hangzhou 310058, China
                College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University , Hangzhou 310058, China
                Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba–Zhejiang University Joint Research Center of Future Digital Healthcare , Hangzhou 330110, China
                Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba–Zhejiang University Joint Research Center of Future Digital Healthcare , Hangzhou 330110, China
                State Key Laboratory of Chemical Oncogenomics, Key Laboratory of Chemical Biology, Tsinghua Shenzhen International Graduate School, Tsinghua University , Shenzhen 518055, China
                Qian Xuesen Collaborative Research Center of Astrochemistry and Space Life Sciences, Institute of Drug Discovery Technology, Ningbo University , Ningbo 315211, China
                Qian Xuesen Collaborative Research Center of Astrochemistry and Space Life Sciences, Institute of Drug Discovery Technology, Ningbo University , Ningbo 315211, China
                College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University , Hangzhou 310058, China
                College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University , Hangzhou 310058, China
                College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University , Hangzhou 310058, China
                State Key Laboratory of Chemical Oncogenomics, Key Laboratory of Chemical Biology, Tsinghua Shenzhen International Graduate School, Tsinghua University , Shenzhen 518055, China
                Qian Xuesen Collaborative Research Center of Astrochemistry and Space Life Sciences, Institute of Drug Discovery Technology, Ningbo University , Ningbo 315211, China
                College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University , Hangzhou 310058, China
                Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba–Zhejiang University Joint Research Center of Future Digital Healthcare , Hangzhou 330110, China
                Author notes
                To whom correspondence should be addressed. Email: zhufeng@ 123456zju.edu.cn
                Correspondence may also be addressed to Yuzong Chen. Email: chenyuzong@ 123456sz.tsinghua.edu.cn
                Correspondence may also be addressed to Su Zeng. Email: zengsu@ 123456zju.edu.cn

                The authors wish it to be known that, in their opinion, the first two authors should be regarded as Joint First Authors.

                Author information
                https://orcid.org/0000-0001-7619-2975
                https://orcid.org/0000-0002-5473-8022
                https://orcid.org/0000-0001-8069-0053
                Article
                gkac813
                10.1093/nar/gkac813
                9825453
                36243961
                f518ea1f-58b2-4125-afb6-ec273c1c1bb8
                © The Author(s) 2022. Published by Oxford University Press on behalf of Nucleic Acids Research.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License ( https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@ 123456oup.com

                History
                : 12 October 2022
                : 30 August 2022
                : 01 August 2022
                Page count
                Pages: 12
                Funding
                Funded by: Natural Science Foundation of Zhejiang Province, DOI 10.13039/501100004731;
                Award ID: LR21H300001
                Funded by: National Natural Science Foundation of China, DOI 10.13039/501100001809;
                Award ID: 81872798
                Award ID: U1909208
                Funded by: Leading Talent of the ‘Ten Thousand Plan’—National High-Level Talents Special Support Plan of China;
                Funded by: Fundamental Research Fund of Central University;
                Award ID: 2018QNA7023
                Funded by: Key R&D Program of Zhejiang Province;
                Award ID: 2020C03010
                Funded by: National Key R&D Program of China Synthetic Biology Research;
                Award ID: 2019YFA0905900
                Funded by: ‘Double Top-Class’ University;
                Award ID: 181201*194232101
                Funded by: Space Exploration Breeding Grant of Qian Xuesen Lab;
                Award ID: TKTSPY-2020-04-03
                Funded by: Scientific Research Grant of Ningbo University;
                Award ID: 215–432000282
                Funded by: Ningbo Top Talent Proj;
                Award ID: 215–432094250
                Funded by: Shenzhen Municipal Government grant;
                Award ID: JCYJ20170413113448742
                Funded by: Department of Science and Technology of Guangdong Province, DOI 10.13039/501100007162;
                Award ID: 2017B030314083
                Funded by: Westlake Laboratory;
                Funded by: Alibaba–Zhejiang University Joint Research Center of Future Digital Healthcare;
                Funded by: Alibaba Cloud;
                Funded by: Information Technology Center of Zhejiang University;
                Categories
                AcademicSubjects/SCI00010
                Database Issue

                Genetics
                Genetics

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