Scientist/Senior Scientist – AI Small Molecule Drug Design

Systimmune•Redmond, WA

3d•$150,000 - $249,999•Onsite

About The Position

SystImmune is seeking an innovative and driven AI Scientist or Senior Scientist to contribute to its AI-driven small molecule drug design efforts. The successful candidate will work as part of an interdisciplinary team, focusing on using computational methods and machine learning to discover and design small molecules that can be developed into therapeutic drugs. This role will involve applying AI to predict molecular properties, optimize drug-like characteristics, and enhance the lead development process. The ideal candidate will have experience in computational chemistry, molecular modeling, and the application of AI and machine learning techniques to small molecule drug discovery. This individual will collaborate closely with chemists, biologists, and computational scientists to drive drug development from discovery through preclinical stages.

Requirements

Ph.D. or equivalent in Computational Chemistry, Bioinformatics, Biophysics, Machine Learning, or a related field.
5+ years of experience applying computational methods and AI to small molecule drug design or a related field, with specific experience in AI small molecule generation, AI molecular docking, virtual screening, and drug manufacturing.
Strong background in machine learning techniques (e.g., deep learning, generative models, reinforcement learning) and their application to drug discovery.
Expertise in molecular modeling and drug design software (e.g., AutoDock, Schrodinger, Open Babel, or other relevant tools).
Proficiency in programming languages such as Python, R, or C++, and experience with machine learning frameworks (e.g., TensorFlow, PyTorch).
Experience in analyzing large-scale datasets, including molecular databases (e.g., ChEMBL, PubChem) and performing virtual screening.
Proven track record in applying computational chemistry and machine learning to solve real-world drug discovery challenges.
Excellent communication skills with the ability to present complex data to both technical and non-technical stakeholders.

Nice To Haves

Familiarity with drug-likeness, ADMET (absorption, distribution, metabolism, excretion, toxicity) properties, and structure-activity relationships (SAR).
Experience working with AI models in the context of generative chemistry or reinforcement learning for drug design.
Contributions to AI-driven drug discovery publications and conference presentations.
Knowledge of biological data integration, such as combining genomic, proteomic, or transcriptomic data with drug discovery.
Experience with high-performance computing (HPC) is a plus.

Responsibilities

Develop and optimize AI-driven small molecule drug design pipelines to predict molecular properties, perform virtual screening, and improve drug-like characteristics.
Utilize advanced AI methods such as generative modeling (e.g., DiffDock, ProteinMPNN), deep learning, and reinforcement learning to generate novel small molecules and predict their interactions.
Implement AI-based molecular docking methods (e.g., DiffDock) to improve binding affinity predictions, optimize lead compounds, and enhance virtual screening efficiency.
Collaborate with cross-functional teams, including medicinal chemistry, biology, and computational biology, to integrate AI methods into drug discovery workflows, ensuring a seamless transition from computational design to experimental validation.
Lead AI-driven efforts in drug manufacturing, optimizing small molecule synthesis routes, yield predictions, and manufacturability profiles of novel drug candidates.
Apply virtual screening techniques using AI models to explore vast chemical spaces, prioritize compound libraries, and identify promising lead candidates for various therapeutic targets.
Analyze and interpret computational data to guide decision-making in the drug design process, focusing on optimizing molecular properties such as pharmacokinetics, toxicity, and efficacy.
Contribute to the development of AI-based software tools and platforms for drug design and analysis, ensuring that solutions are scalable and user-friendly for cross-disciplinary teams.
Generate insights from large-scale chemical and biological datasets, identifying key relationships and optimizing drug candidates for efficacy, safety, and pharmacokinetics.
Contribute to the development and deployment of software tools and platforms that enable AI-based drug design and analysis.
Stay updated on the latest advancements in AI and computational chemistry, especially in areas like AI small molecule generation, molecular docking, and virtual screening, and apply state-of-the-art methods to improve drug discovery processes.