About the role
AI summarisedSenior Principal Scientist II leading AI-driven drug design at A*STAR's AIDD LeadFactory in Singapore. The role involves strategic leadership, research excellence, team building, and interdisciplinary collaboration to integrate computational design with experimental validation for small molecule therapeutics.
ResearchFull-timeAI in Drug Discovery
Key Responsibilities
- Help develop and execute the scientific vision and strategy for the AIDD LeadFactory's AI-driven drug design, identifying new research avenues and emerging trends to maximize tangible impact.
- Lead a research group focusing on innovative AI methodologies for drug discovery, including de novo design, virtual screening, binding pose and binding affinity prediction, ADMET prediction, and hit-to-lead optimization, actively integrating experimental validation through the AIDD LeadFactory.
- Recruit, mentor, and inspire a team of talented researchers (postdocs, PhD students, and scientific staff) in AI and drug discovery, including those focused on the experimental aspects enabled by the LeadFactory.
- Strengthen collaborations with experimental and clinical groups both within A*STAR and with external academic and industry partners, leveraging the unique capabilities of the AIDD LeadFactory.
- Publish high-impact research in top-tier journals and present at international conferences, enhancing the programme's global visibility and showcasing the innovations stemming from the AIDD LeadFactory.
Requirements
- A PhD in Computational Chemistry, Medicinal Chemistry, Chemoinformatics, Bioinformatics, Computer Science, Machine Learning, or a closely related field.
- A strong, internationally recognised research track record in AI-driven drug design, evidenced by high-impact publications and distinguished scholarly contributions, including invited talks, conference presentations, and scientific outreach activities.
- Extensive industrial experience leading research teams and managing complex interdisciplinary projects, ideally at the interface of computational and experimental drug discovery.
- Deep expertise and demonstrated ability to design and develop novel neural network architectures and machine learning methodologies, tailored to address complex problems in molecular design, virtual screening, and predictive modelling.
- Proven experience or a strong vision for integrating computational design with experimental validation, particularly in the context of automated synthesis and high-throughput screening platforms.