- 성과
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논문 (학술지)
Exploring chemical space for lead identification by propagating on chemical similarity network
| 등록번호 | - |
SCI 구분
※구분 : SCI(SCIE포함), 비SCI
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SCI |
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| 저자명 (주·공동저자) | 이정섭; 김동규; 여말희; 이상선; 조창연; PIAOYINHUA; 김선; 임상수; 이선호 ※ 과제 참여정보와 일치하는 연구자 상세정보로 정확하지 않을 수 있습니다. 동일저자 논문보기 |
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| 논문구분 | 국외전문학술지 | 학술지명 | Computational and Structural Biotechnology Journal |
| ISSN | 2001-0370 | 학술지 출판일자 | 2023-08-29 |
| 학술지 볼륨번호 | 21(n) | 논문페이지 | 4187 ~ 4195 |
| 학술지 임팩트팩터 | 6.33 | 기여율 | 100 % |
| DOI | https://doi.org/10.1016/j.csbj.2023.08.016 | ||
- 초록
- Motivation Lead identification is a fundamental step to prioritize candidate compounds for downstream drug discovery process. 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. Results 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 IC50. 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.
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