Development of Novel Modulators for Drug Target Proteins via Computer-Aided Drug Discovery
- Abstract
- The application of computer aid to drug discovery, which is a high-cost and labor-intensive process, has been persistent. This dissertation reports on the use of computer science to accelerate the hit compound discovery and hit-to-lead optimization stages of drug discovery. Part I describes the search for novel antagonists of the P2X3 receptor (P2X3R), which is involved in various neuropathological diseases. With a new deep learning model (Highlight on Target Sequence; HoTS), which suggested a new binding site for P2X3R, and predicted the druggability of the binding site through molecular dynamics and molecular docking simulation. Both pharmacophore-based virtual screening based on the predicted binding site and HoTS recommendation were separately used to select top compound sets from library, and these were verified through in vitro assay, resulting in the discovery of 16 novel hit compounds. The hit rate was found to be 10-fold higher than that from previous random screening and this work is expected to be useful in the early stages of the future drug discovery. In Part II, computer science was used to accelerate the hit-to-lead optimization process in developing a new antiviral agent against SARS-CoV-2. The SARS-CoV-2 main protease (Mpro) was considered a promising drug candidate, and structure-based drug design was conducted to improve the inhibitory activity of carmofur, a previously reported hit compound. Newly designed potential inhibitors were synthesized and evaluated biologically, resulting in the discovery of novel derivatives 21h and 21i, which showed the best potency for Mpro inhibition and antiviral activity. These compounds have the potential to act as lead compounds for the development of anti-SARS-CoV-2 agents.
- Author(s)
- Koon Mook Kang
- Issued Date
- 2023
- Type
- Thesis
- URI
- https://scholar.gist.ac.kr/handle/local/19159
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