Targeting programmed -1 ribosomal frameshifting in RNA viruses by small molecules using AI-assisted computational chemistry tools

• Project/Area Number: 24K23894
• Research Category: Grant-in-Aid for Research Activity Start-up
• Allocation Type: Multi-year Fund
• Review Section: 1002:Human informatics, applied informatics and related fields
• Research Institution: Kyushu University
• Principal Investigator: IVONINA Mariia 九州大学, エネルギー研究教育機構, 学術研究員 (11005886)
• Project Period (FY): 2024-07-31 – 2026-03-31
• Project Status: Granted (Fiscal Year 2024)
• Budget Amount: ¥2,730,000 (Direct Cost: ¥2,100,000、Indirect Cost: ¥630,000); Fiscal Year 2025: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000); Fiscal Year 2024: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
• Keywords: SARS-CoV-2 / RNA drug / TS/TB-EDA / Molecular Dynamics / Binding energy

Outline of Research at the Start

The goal of this work is to design novel drug candidates in silico to inhibit replication of SARS-CoV-2 and similar viruses. To predict molecular pattern of novel drug, original Through-space/bond Energy Decomposition Analysis in combination with Molecular Dynamics and Machine Learning will be used.