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A*STAR

Scientist/Senior Scientist, Computational Oligonucleotide Design

A*STAR
ResearchSingaporeOnsitePosted 16 hours ago
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About the role

AI summarised

Computational scientist role focused on designing synthetic oligonucleotides for RNA therapeutics using molecular dynamics and AI/ML. The position involves building predictive models, analyzing large biological datasets, and collaborating with chemists and biologists to advance drug candidates from discovery to development.

ResearchOnsite

Key Responsibilities

  • Develop automated data processing and visualization pipelines to support high-throughput oligonucleotide design and performance analysis (e.g., target specificity, stability, pharmacological properties)
  • Establish and implement molecular dynamics (MD) simulations to study RNA-protein interactions and elucidate mechanism(s) of target engagement
  • Develop and apply AI/ML frameworks to explore novel chemical space and predict oligo performance across diverse RNA targets
  • Analyze large biological datasets (e.g., transcriptomics, miRNA profiles, structural data) to inform oligo design and modification strategies
  • Develop models that integrate sequence, secondary/tertiary structure, and oligo chemistry to predict target engagement and guide binding assay design
  • Collaborate with oligonucleotide chemists, biologists, and pharmacologists to integrate computational designs with experimental workflows towards the identification of a Drug Candidate
  • Stay current with emerging computational tools, algorithms, and scientific literature relevant to the development of differentiated RNA therapeutics
  • Contribute to NATi's intellectual property (IP) portfolio through computational tools and novel drug candidates
  • Prepare appropriate summary reports and presentations to update NATi leadership and stakeholders

Requirements

  • Ph.D. in Computational Chemistry, Biophysics, Bioinformatics, Biomedical Engineering, or a related field
  • Minimum of 4 (Scientist) or 8 (Senior Scientist) years of hands-on industry experience in computational oligonucleotide design or RNA-targeting therapeutics
  • Hands-on experience with MD simulation tools (e.g., GROMACS, AMBER, OpenMM) and applying machine learning frameworks (e.g., Python, TensorFlow, PyTorch) to biological/chemical datasets
  • Proficiency in RNA structure prediction and interaction modelling tools (e.g., RNAstructure, ViennaRNA, IntaRNA, RNAhybrid)
  • Hands-on experience with transcriptome analysis tools (e.g., Bowtie, BLAST, STAR, Salmon) and oligo design platforms (e.g., siDirect, ASO Designer, CRISPRoff)
  • Strong programming skills in Python and familiarity with scientific computing libraries; experience with Git and reproducible research practices
  • Strong understanding of RNA biology, including alternative splicing, polyadenylation, RNA editing, and RNA-binding proteins
  • Experience with nucleotide modification strategies (e.g., 2"-O-methyl, phosphorothioate, LNA) and their integration into computational design workflows to optimize oligonucleotide performance, including stability, specificity, and selective target engagement
  • Prior experience in applying free energy calculation methods (e.g., FEP, TI, MM/PBSA, MM/GBSA) for binding affinity prediction or selectivity profiling is desirable
  • Demonstrated experience combining physics-based modelling with AI/ML models to predict oligo efficacy, off-target effects, and/or RNA structure-function relationships using biological datasets is a plus
  • Proven ability to work independently and collaboratively in multidisciplinary teams, effectively communicating computational insights to experimental scientists
  • Demonstrated ability to troubleshoot complex scientific problems and adapt to shifting priorities in a fast-paced, quality-driven environment