Principal Scientist – Computational Drug Discovery (A*STAR BII)

Position Summary

Lead the application of computational structuralbiology, molecular modeling, and artificial intelligence to acceleratetherapeutic discovery across diverse target classes. Integrate physics-basedsimulations with AI-driven methodologies to support target identification, hitdiscovery, lead optimization, and mechanism-of-action studies.

Key Responsibilities:

• Develop andexecute structure-based drug discovery strategies for small molecules,peptides, covalent inhibitors, molecular glues, and PROTACs.
• Perform proteinstructure prediction, refinement, and protein–protein interaction modelingusing AI-based methods.
• Apply moleculardocking, virtual screening, molecular dynamics simulations, free-energycalculations, and binding affinity prediction.
• Design andoptimize ligands using AI-assisted molecular generation, generative models, andcheminformatics workflows.
• Characterizeprotein conformational landscapes, cryptic binding pockets, allostericmechanisms, and target druggability through ensemble modeling and enhancedsampling simulations.
• Conductcomputational protein engineering, mutational analysis, and binding interfaceoptimization.
• Predict ADMETproperties, developability, and physicochemical characteristics to guide leadoptimization.
• Collaborate withmedicinal chemists, structural biologists, and experimental scientists toprioritize compounds and interpret biological data.
• Contribute toscientific publications, patents, grant proposals, and technology development.

Requirements:

• PhD in Computational Chemistry, Structural Biology,Biophysics, Bioinformatics, Pharmaceutical Sciences, or a related field, withextensive experience in computational drug discovery and a demonstrated trackrecord in translating computational insights into experimentally validatedtherapeutic programs.
• Proven record of generating IP and spin offs will beadvantageuous.
• At least 8 years of relevant experience

Scientific Expertise:

• Structure-BasedDrug Design (SBDD)  &  AI-Driven Drug Discovery
• StructuralBiology & Protein Engineering, Protein–Protein Interaction Modeling,
• Peptide, Macrocycle & Biologic Design
• Molecular Docking& Virtual Screening, Covalent Drug Design
• MolecularDynamics & Enhanced Sampling, Free-Energy Calculations, Ensemble Modeling& Conformational Analysis
• Experience withAlphaFold, RoseTTAFold, Chai, Boltz,  Schrödinger Suite, GOLD, AutoDock, GNINA, GROMACS, AMBER,  GPU Computing, Machine Learning &Generative AI for Molecular Design

The above eligibility criteria are not exhaustive. A*STAR may include additional selection criteria based on its prevailing recruitment policies. These policies may be amended from time to time without notice. We regret that only shortlisted candidates will be notified.