Restuan Lubis
Postdoctoral Associate
Affiliation: NYU Abu Dhabi
Education: BPharm Universitas Muslim Indonesia; MRes University of College London; PhD Lead Discovery Center and University of Duisburg-Essen
Research Websites: Center for Quantum and Topological Systems (CQTS)
Research Areas: Drug discovery; high-throughput screening; mechanism-of-action studies
Restuan Lubis is a Postdoctoral Researcher at the Centre for Genomics and Systems Biology at New York University Abu Dhabi. His research focuses on high-throughput and high-content screening, assay development, phenotypic profiling, and mechanism-of-action studies using small molecules and genetic libraries. Previously, he worked at the Lead Discovery Center GmbH, a German biotech company focused on translational drug discovery, where he developed biochemical and cellular assays and contributed to large-scale drug discovery screening campaigns.
Restuan earned his PhD through the Lead Discovery Center and the University of Duisburg-Essen, with a dissertation titled “Discovery of First-in-Class Covalent BLM Helicase Inhibitors.” He also holds an MRes in Drug Design from University College London. His honors include the 2025 NYUAD Grad Slam Runner-up award, the 2024 SLAS Tony B Award, and the 2023 ELRIG Early Career Professional Impact Award. He has presented his work at major international drug discovery conferences, including SLAS, ELRIG, and Discovery Europe.
Summary of Research
Lubis’s research focuses on drug discovery through high-throughput screening, high-content screening, assay development, and phenotypic profiling. His work integrates biochemical, cellular, and image-based screening approaches to identify, validate, and characterize small-molecule hits and investigate their mechanisms of action. He has extensive experience developing robust assays for large-scale screening campaigns, including biochemical and cell-based formats for potency, target engagement, cytotoxicity, signaling, and phenotypic outcomes.
At NYU Abu Dhabi, Lubis contributes to the development and application of high-content screening and phenotypic profiling platform for mammalian cells. This work aims to improve the biological resolution of image-based screening by integrating advanced Cell Painting approaches, single-cell phenotypic analysis, feature extraction, clustering, and visualization. His research supports the expansion of HCS toward broader chemical libraries, disease-relevant cell models, and integration with genetic perturbation strategies such as CRISPR and RNAi screens.
A major goal of his current research is to connect phenotypic screening data with mechanism-of-action discovery and compound prioritization in drug discovery. By combining HCS with molecular profiling, functional genomics, and machine learning-based analysis, his work seeks to identify biologically meaningful phenotypic signatures, uncover target pathways, and guide the selection of promising compounds for future screening and optimization campaigns.