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Machine learning (ML) and deep learning (DL) approaches are widely used to identify lead compounds using both chemical property and experimental information. However, ML or DL methods rarely consider compound similarity information directly since ML and DL models use abstract representation of molecules for model construction. Alternatively, data mining approaches are also used to explore chemical space with drug candidates by screening undesirable compounds. A major challenge for data mining approaches is to develop efficient data mining methods that search large chemical space for desirable lead compounds with low false positive rate.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec sec-type=\"results\">\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      In this work, we developed a network propagation (NP) based data mining method for lead identification that performs search on an ensemble of chemical similarity networks. We compiled 14 fingerprint-based similarity networks. Given a target protein of interest, we use a deep learning-based drug target interaction model to narrow down compound candidates and then we use network propagation to prioritize drug candidates that are highly correlated with drug activity score such as IC\n                      <jats:sub>50<\/jats:sub>\n                      . In an extensive experiment with BindingDB, we showed that our approach successfully discovered intentionally unlabeled compounds for given targets. To further demonstrate the prediction power of our approach, we identified 24 candidate leads for CLK1. Two out of five synthesizable candidates were experimentally validated in binding assays. In conclusion, our framework can be very useful for lead identification from very large compound databases such as ZINC.\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1016\/j.csbj.2023.08.016","type":"journal-article","created":{"date-parts":[[2023,8,25]],"date-time":"2023-08-25T11:26:20Z","timestamp":1692962780000},"page":"4187-4195","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":10,"title":["Exploring chemical space for lead identification by propagating on chemical similarity network"],"prefix":"10.34133","volume":"21","author":[{"given":"Jungseob","family":"Yi","sequence":"first","affiliation":[{"name":"Interdisciplinary Program in Artificial Intelligence, Seoul National University, Gwanak-ro 1, Gwanak-gu, Seoul, 08826, South Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sangseon","family":"Lee","sequence":"additional","affiliation":[{"name":"Institute of Computer Technology, Seoul National University, Gwanak-ro 1, Gwanak-gu, Seoul, 08826, South Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sangsoo","family":"Lim","sequence":"additional","affiliation":[{"name":"School of AI Software Convergence, Dongguk University, Pildong-ro 1-gil, Jung-gu, Seoul, South Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Changyun","family":"Cho","sequence":"additional","affiliation":[{"name":"Interdisciplinary Program in Bioinformatics, Seoul National University, Gwanak-ro 1, Gwanak-gu, Seoul, 08826, South Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yinhua","family":"Piao","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Seoul National University, Gwanak-ro 1, Gwanak-gu, Seoul, 08826, South Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marie","family":"Yeo","sequence":"additional","affiliation":[{"name":"PHARMGENSCIENCE CO., LTD., 216, Dongjak-daero, Seocho-gu, Seoul, 06554, South Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dongkyu","family":"Kim","sequence":"additional","affiliation":[{"name":"PHARMGENSCIENCE CO., LTD., 216, Dongjak-daero, Seocho-gu, Seoul, 06554, South Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sun","family":"Kim","sequence":"additional","affiliation":[{"name":"Interdisciplinary Program in Artificial Intelligence, Seoul National University, Gwanak-ro 1, Gwanak-gu, Seoul, 08826, South Korea"},{"name":"Interdisciplinary Program in Bioinformatics, Seoul National University, Gwanak-ro 1, Gwanak-gu, Seoul, 08826, South Korea"},{"name":"Department of Computer Science and Engineering, Seoul National University, Gwanak-ro 1, Gwanak-gu, Seoul, 08826, South Korea"},{"name":"AIGENDRUG CO., LTD., Gwanak-ro 1, Gwanak-gu, Seoul, 08826, South Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sunho","family":"Lee","sequence":"additional","affiliation":[{"name":"AIGENDRUG CO., LTD., Gwanak-ro 1, Gwanak-gu, Seoul, 08826, South Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"221","published-online":{"date-parts":[[2023,8,29]]},"reference":[{"key":"e_1_3_5_2_2","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.2000585117"},{"key":"e_1_3_5_3_2","volume-title":"What are the current challenges for machine learning in drug discovery and repurposing?","author":"Aittokallio T.","year":"2022","unstructured":"Aittokallio T.: What are the current challenges for machine learning in drug discovery and repurposing?. : 2022."},{"key":"e_1_3_5_4_2","doi-asserted-by":"crossref","DOI":"10.1016\/j.vascn.2019.106609","article-title":"Safety screening in early drug discovery: an optimized assay panel","volume":"99","author":"Bendels S.","year":"2019","unstructured":"Bendels S., Bissantz C., Fasching B., Gerebtzoff G., Guba W., Kansy M. et al: Safety screening in early drug discovery: an optimized assay panel. 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