about the company
This innovative biotechnology organization is a leader in the advancement of RNAi and siRNA therapeutics. The firm is committed to developing next-generation RNA-based medicines, prioritizing the creation of sophisticated delivery platforms that enable extrahepatic targeting and utilize novel AOC/POC (Antibody/Peptide-Oligonucleotide Conjugate) modalities.
Located in Singapore, the early-discovery group operates as a premier international R&D center. This team is instrumental in spearheading the discovery pipeline, providing the scientific leadership necessary to move programs from initial target identification through to successful preclinical candidate (PCC) nomination.
about the job
This Singapore-based AI-Driven Drug Discovery (AIDD) Scientist will leverage advanced artificial intelligence and machine learning to accelerate a specialized RNAi drug discovery pipeline. The primary focus is on AI-assisted molecular design and optimization, bridging the gap between computational innovation and therapeutic reality.
The role requires a sophisticated blend of AI/ML expertise and deep drug discovery domain knowledge to transform complex chemical and biological hurdles into actionable computational solutions. You will be embedded within cross-functional chemistry and biology units to deploy AI-driven strategies, explore emerging methodologies, and directly influence the trajectory of discovery programs. Additionally, you will play a key role in scouting and managing external AI partnerships to further bolster the firm’s internal discovery engine.
... AI-Driven Molecular Design: Develop and apply advanced computational chemistry and ML models to design and optimize protein/peptide ligands. This includes building predictive frameworks for siRNA efficacy, off-target risk assessment, and chemical modification profiles for both single and dual-targeting strategies.
Methodology Innovation: Proactively explore and implement cutting-edge AI/ML architectures to solve unique bottlenecks in RNAi discovery. Stay at the forefront of the AIDD landscape, prototyping and validating new computational approaches to significantly compress discovery timelines.
Multimodal Data Integration: Architect unified data platforms by integrating internal in vitro and in vivo experimental results with external datasets. Develop multimodal models to predict in vitro-in vivo correlations (IVIVC) and optimize molecular properties through data-driven decision-support tools.
Cross-Functional Technical Leadership: Act as the primary AI resource for Chemistry, Biology, and DMPK teams. Translate complex therapeutic challenges into structured computational problems and manage the full lifecycle of AI solutions, from data curation to model deployment.
External Scouting & Strategic Partnerships: Evaluate the technical capabilities of external AI firms, CROs, and academic institutions. Facilitate the integration of third-party technologies into internal R&D workflows and provide technical oversight for collaborative research projects.
about the manager/team
Individual Contributor role, reporting to Vice President of Discovery
skills and experience required
- Ph.D. or master’s degree in computational chemistry, Computer Science, or a related field with strong AI/ML focus. Recent graduates with exceptional research experience will be considered.
CADD/AIDD Technical Mastery: Minimum of 2+ years of hands-on experience developing and deploying machine learning models (industry or advanced academic). Proficient with standard CADD suites (e.g., Schrödinger, Amber, Gromacs, Rosetta, or AutoDock) for molecular docking and high-fidelity molecular dynamics simulations.
Modern AI Architectures: Strong preference for candidates experienced with state-of-the-art models such as AlphaFold, ProteinMPNN, and RNA-FM for structural prediction and design.
Drug Discovery Application: Proven ability to apply AI/ML to hit-to-lead and lead optimization, including molecular design, property prediction, and ADMET modeling. Familiarity with chemical informatics representations (SMILES, Graph Neural Networks, 3D conformers) is essential.
Therapeutic Domain Knowledge: While not required, exposure to RNAi therapeutics, oligonucleotide chemistry, or AOC/POC modalities is highly advantageous.
Innovation & Agility: A passionate explorer of novel AI methodologies with the ability to rapidly pivot between scientific domains. You should possess a "fail-fast, learn-faster" experimental mindset to solve complex, real-world biological hurdles.
Cross-Functional Communication: Excellent ability to act as a "translator" between the dry-lab and wet-lab, simplifying complex AI/ML concepts for Chemistry, Biology, and DMPK colleagues to ensure project alignment.
Execution & Autonomy: Self-motivated and capable of driving independent research in a fast-paced, high-growth environment. You must demonstrate strong analytical rigor and the ability to manage multiple high-priority workstreams simultaneously.
Strategic Scouting: A keen interest in the broader AI-biotech ecosystem, with the technical depth required to evaluate the capabilities of potential external partners, CROs, and technology vendors.
To apply online please use the 'apply' function, alternatively you may contact at .
(EA: 94C3609/ )
