{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,29]],"date-time":"2025-10-29T19:38:08Z","timestamp":1761766688896,"version":"3.37.3"},"reference-count":8,"publisher":"Oxford University Press (OUP)","issue":"11","license":[{"start":{"date-parts":[[2020,3,14]],"date-time":"2020-03-14T00:00:00Z","timestamp":1584144000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["81630103"],"award-info":[{"award-number":["81630103"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100017582","name":"Beijing National Research Center For Information Science And Technology","doi-asserted-by":"crossref","award":["BNR2019TD01020","BNR2019RC01012"],"award-info":[{"award-number":["BNR2019TD01020","BNR2019RC01012"]}],"id":[{"id":"10.13039\/501100017582","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Project of Tsinghua-Fuzhou Institute for Data Technology","award":["TFIDT2018001"],"award-info":[{"award-number":["TFIDT2018001"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,6,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Summary<\/jats:title>\n                  <jats:p>Although many quantitative structure\u2013activity relationship (QSAR) models are trained and evaluated for their predictive merits, understanding what models have been learning is of critical importance. However, the interpretation and visualization of QSAR model results remain challenging, especially for \u2018black box\u2019 models such as deep neural network (DNN). Here, we take a step forward to interpret the learned chemical features from DNN QSAR models, and present VISAR, an interactive tool for visualizing the structure\u2013activity relationship. VISAR first provides functions to construct and train DNN models. Then VISAR builds the activity landscapes based on a series of compounds using the trained model, showing the correlation between the chemical feature space and the experimental activity space after model training, and allowing for knowledge mining from a global perspective. VISAR also maps the gradients of the chemical features to the corresponding compounds as contribution weights for each atom, and visualizes the positive and negative contributor substructures suggested by the models from a local perspective. Using the web application of VISAR, users could interactively explore the activity landscape and the color-coded atom contributions. We propose that VISAR could serve as a helpful tool for training and interactive analysis of the DNN QSAR model, providing insights for drug design, and an additional level of model validation.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>The source code and usage instructions for VISAR are available on github https:\/\/github.com\/qid12\/visar.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Contact<\/jats:title>\n                  <jats:p>shaoli@mail.tsinghua.edu.cn<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Supplementary information<\/jats:title>\n                  <jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btaa187","type":"journal-article","created":{"date-parts":[[2020,3,12]],"date-time":"2020-03-12T12:43:58Z","timestamp":1584017038000},"page":"3610-3612","source":"Crossref","is-referenced-by-count":11,"title":["VISAR: an interactive tool for dissecting chemical features learned by deep neural network QSAR models"],"prefix":"10.1093","volume":"36","author":[{"given":"Qingyang","family":"Ding","sequence":"first","affiliation":[{"name":"MOE Key Laboratory of Bioinformatics , TCM-X Centre\/Bioinformatics Division, BNRIST, Tsinghua University, Beijing 10084, China"},{"name":"School of Pharmaceutical Sciences , MOE Key Laboratory of Bioorganic Phosphorus Chemistry and Chemical Biology, Tsinghua University, 100084 Beijing, China"}]},{"given":"Siyu","family":"Hou","sequence":"additional","affiliation":[{"name":"MOE Key Laboratory of Bioinformatics , TCM-X Centre\/Bioinformatics Division, BNRIST, Tsinghua University, Beijing 10084, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9753-5623","authenticated-orcid":false,"given":"Songpeng","family":"Zu","sequence":"additional","affiliation":[{"name":"MOE Key Laboratory of Bioinformatics , TCM-X Centre\/Bioinformatics Division, BNRIST, Tsinghua University, Beijing 10084, China"}]},{"given":"Yonghui","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Pharmaceutical Sciences , MOE Key Laboratory of Bioorganic Phosphorus Chemistry and Chemical Biology, Tsinghua University, 100084 Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8709-9167","authenticated-orcid":false,"given":"Shao","family":"Li","sequence":"additional","affiliation":[{"name":"MOE Key Laboratory of Bioinformatics , TCM-X Centre\/Bioinformatics Division, BNRIST, Tsinghua University, Beijing 10084, China"}]}],"member":"286","published-online":{"date-parts":[[2020,3,14]]},"reference":[{"key":"2023062312021825800_btaa187-B1","doi-asserted-by":"crossref","first-page":"D1083","DOI":"10.1093\/nar\/gkt1031","article-title":"The ChEMBL bioactivity database: an update","volume":"42","author":"Bento","year":"2014","journal-title":"Nucleic Acids Res"},{"key":"2023062312021825800_btaa187-B2","doi-asserted-by":"crossref","first-page":"4977","DOI":"10.1021\/jm4004285","article-title":"QSAR modeling: where have you been? 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