Iambic
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About
Iambic is a biotechnology platform that integrates physics-based artificial intelligence with high-throughput laboratory experimentation to accelerate the discovery of new medicines. The platform is designed to tackle complex challenges in drug design, particularly for targets that have previously been considered undruggable. By simulating and optimizing target product profiles in parallel, Iambic aims to deliver high-quality drug candidates that address significant unmet patient needs in areas like oncology and protein-protein interactions. The system operates through a continuous loop where AI-driven molecular designs are converted into biological insights on a weekly basis. It utilizes data-efficient physics-based algorithms to explore vast chemical spaces, identifying novel mechanisms of action and diverse leads. This computational approach is paired with an automated experimental platform that validates AI predictions through high-speed testing, allowing the team to refine molecules with high precision. This integration of innovation and automation facilitates the rapid transition from early-stage discovery to clinical evaluation. Iambic is primarily built for pharmaceutical companies and research institutions seeking to modernize their drug development pipelines. It serves as a strategic partner for large-scale drug hunters who need to solve specific problems in disease biology, such as achieving high selectivity or brain penetrance for metastatic diseases. The platform is currently being utilized for internal programs, such as HER2 and CDK2/4 inhibitors, as well as collaborative efforts with established industry players like Takeda. What distinguishes Iambic from traditional drug discovery methods is its pace and the depth of its physics-based modeling. While industry standards often take years to reach clinical candidates, Iambic’s execution model targets a significantly faster timeline. Furthermore, its ability to focus on cryptic pockets and allosteric sites—areas often missed by standard AI models—allows it to transform difficult targets into viable breakthrough treatments. The collaboration between seasoned drug hunters and AI experts ensures that the technology is grounded in practical medicinal chemistry and clinical utility.
Pros & Cons
Achieved over 5000-fold selectivity for specific targets like HER2.
Delivers clinical candidates years faster than typical industry timelines.
Integrates laboratory automation directly with AI feedback loops for weekly insights.
Supported by a scientific advisory board including Nobel laureates and AI experts.
Capable of targeting complex mechanisms like cryptic pockets and protein-protein interactions.
Not a self-service software; requires high-level institutional partnership and collaboration.
Focus is primarily restricted to small molecule discovery rather than biologics or other modalities.
Development is subject to the long timelines and risks inherent in Phase 1-3 clinical trials.
Use Cases
Pharmaceutical companies can partner with Iambic to identify leads for undruggable targets using physics-based simulations.
Clinical researchers can utilize the platform to design brain-penetrant molecules for metastatic diseases that traditional methods struggle to reach.
Biotech teams can automate the cycle of molecular design and biological testing to gain new insights on a weekly basis.
Drug hunters can use the platform to optimize target product profiles in parallel, ensuring molecules solve the right problems in disease biology.
Platform
Task
Features
• physics-based ai algorithms
• high-throughput experimental platform
• brain-penetrant molecule design
• chemical space exploration
• target product profile optimization
• weekly biological insight generation
• allosteric site targeting
• automated molecular design
FAQs
What specific therapeutic areas does Iambic focus on?
Iambic is currently focused on oncology, with active programs in HER2-selective inhibitors for metastatic disease and CDK2/4 profiles. The platform also explores novel target classes involving protein-protein interactions and allostery across multiple therapeutic areas.
How does Iambic's AI platform differ from standard machine learning?
Iambic uses physics-based AI algorithms which are designed to be more data-efficient than traditional deep learning models. This approach allows for more accurate molecular predictions in early-stage discovery where experimental data is often limited.
Can other pharmaceutical companies partner with Iambic?
Yes, Iambic engages in technology and discovery collaborations, such as its partnership with Takeda to advance small molecule design. These partnerships leverage Iambic's AI-driven platform to solve challenging design problems for specific targets.
What is the current status of Iambic's drug pipeline?
The pipeline includes the HER2 program, which has reached Phase 1 clinical studies, and several earlier-stage programs targeting CDK2/4. The platform aims to deliver clinical candidates at a pace significantly faster than the industry standard.
Pricing Plans
Enterprise Partnership
Unknown Price• Physics-based AI design
• High-throughput experimental validation
• Target product profile optimization
• Pipeline collaboration
• Access to differentiated clinical candidates
• Proprietary chemical space exploration
• Small molecule design
• Weekly biological data feedback
Job Opportunities
Head of Translational Oncology
Design superior therapeutic molecules and accelerate drug discovery using physics-based AI algorithms and high-throughput experimental feedback loops.
Benefits:
industry leading competitive pay
company paid healthcare
flexible spending accounts
voluntary life insurance
401K matching
Education Requirements:
Ph.D., M.D., or equivalent advanced degree in cancer biology, molecular biology, pharmacology, computational biology, or a related discipline
Experience Requirements:
Typically 12–20+ years of relevant experience in biotech or pharma
Success advancing at least one oncology program into clinical development
Deep expertise in translational science (biomarkers, MoA, resistance, combination biology)
Experience working with large, complex translational and/or clinical datasets
Track record of leading cross-functional teams
Other Requirements:
Strong working knowledge of AI/ML concepts as applied to drug discovery
Excellent scientific storytelling and communication skills
Responsibilities:
Lead Portfolio-Level Translational Strategy
Define Biomarker & Patient Selection Strategy
Direct Mechanism of Action, Resistance & Combination Biology
AI/ML-Enabled Translational & Clinical Prediction
Build and Lead a High-Impact Organization
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Research Scientist II / Senior, Translational Biology
Design superior therapeutic molecules and accelerate drug discovery using physics-based AI algorithms and high-throughput experimental feedback loops.
Benefits:
industry leading competitive pay
company paid healthcare
flexible spending accounts
voluntary life insurance
401K matching
Education Requirements:
PhD in biochemistry, cell biology, cancer biology, pharmacology, or related discipline
MS with 7+ years of experience
BS with 9+ years of experience
Experience Requirements:
At least 2 - 8 years of relevant academic or industry experience for PhD holders
Experience in cancer research, mechanism of action/resistance, or translational biomarker development
Hands-on experience in in vivo pharmacology (PK/PD, efficacy studies)
Oversight and coordination of in vivo studies at CROs
Proficiency in cell and molecular biology (cell culture, ELISA, Western blotting, qPCR, flow cytometry)
Other Requirements:
Excellent communication skills
Ability to work collaboratively across teams
Highly motivated to learn new technologies
Responsibilities:
Design and develop translational studies for various programs
Design and manage in vivo pharmacology studies at CROs
Design and execute hands-on experiments in cell and molecular biology
Analyze and interpret data from internal experiments and CRO deliverables
Conduct literature reviews to support mechanism of action and biomarker strategies
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Sr Associate Scientist I or II, Medicinal Chemistry
Design superior therapeutic molecules and accelerate drug discovery using physics-based AI algorithms and high-throughput experimental feedback loops.
Benefits:
industry leading competitive pay
company paid healthcare
flexible spending accounts
voluntary life insurance
401K matching
Education Requirements:
BS in Chemistry with 5+ years experience (pharma/biotech)
MS in Chemistry with 3+ years experience (pharma/biotech)
Experience Requirements:
Proven skills in multi-step organic synthesis and reaction optimization
Experience in SAR analysis and scientific presentation
Prior exposure to high-throughput experimentation (preferred)
Familiarity with ADME-PK principles (preferred)
Experience with molecular modeling and structure-based drug design (preferred)
Other Requirements:
Ability to thrive in a fast-paced, highly collaborative research environment
Availability to work onsite in San Diego
Responsibilities:
Design, synthesize, purify, and characterize chemical compounds (10 mg–5 g scale)
Identify and optimize reaction conditions using screening kits
Collaborate with the high-throughput experimentation team
Analyze biological and ADME-PK data using cheminformatics tools
Partner with computational chemists to leverage machine learning insights
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