Affiliation

Lecturer, Department of Biochemistry, Yong Loo Lin School of Medicine, NUS

Education

Professional Experience

Research Interests

My research harnesses the power of computational techniques to revolutionize drug discovery. By integrating high-performance computing and machine learning with conventional computer-aided methods, my lab aims to streamline the drug discovery pipeline, potentially reducing the time and cost associated with bringing new therapeutics to market. A key area of our work involves developing cutting-edge ultra-large virtual screening platforms. These innovative tools efficiently explore vast chemical spaces, dramatically accelerating the process of uncovering novel inhibitors for a range of diseases. We primarily focus on antimicrobials, cancer therapeutics, and compounds that could impact the aging process. Additionally, I’m also interested in computational techniques to design small molecules targeting RNA, an emerging and exciting field in pharmaceutical research. This work not only pushes the boundaries of computational chemistry but also holds the promise of developing more effective treatments for some of society’s most pressing health challenges. Through these advanced computational approaches, my lab hopes to advance the field of computational drug discovery and pave the way for faster, more efficient development of life-changing medicines.

Major research interests:

1. Computer-aided drug design
2. Small molecule therapeutics development
3. RNA-based therapeutics

Selected Publications

1. Gunasinghe, K. K. J., Ginjom, I. R. H., San, H. S., Rahman, T. & Wezen, X. C. Can Current Molecular Docking Methods Accurately Predict RNA Inhibitors? J. Chem. Inf. Model. 64, 5954–5963 (2024).
2. Gunasinghe, K. K. J., Rahman, T. & Chee Wezen, X. Unraveling the Behavior of Intrinsically Disordered Protein c-Myc: A Study Utilizing Gaussian-Accelerated Molecular Dynamics. ACS Omega 9, 2250–2262 (2024).
3. Cheng, Z., Bhave, M., Hwang, S. S., Rahman, T. & Chee, X. W. Identification of Potential p38γ Inhibitors via In Silico Screening, In Vitro Bioassay and Molecular Dynamics Simulation Studies. Int. J. Mol. Sci. 24, 7360 (2023).
4. Koh, C. M. M. et al. A data-driven machine learning approach for discovering potent LasR inhibitors. Bioengineered 14, 2243416 (2023).
5. Cheng, Z., Hwang, S. S., Bhave, M., Rahman, T. & Chee Wezen, X. Combination of QSAR Modeling and Hybrid-Based Consensus Scoring to Identify Dual-Targeting Inhibitors of PLK1 and p38γ. J. Chem. Inf. Model. 63, 6912–6924 (2023).
6. Chee Wezen, X. et al. Structure-Based Discovery of Lipoteichoic Acid Synthase Inhibitors. J. Chem. Inf. Model. 62, 2586–2599 (2022).