Scientist/Senior Scientist – AI Small Molecule Drug Design

SystImmune is a leading and well-funded clinical-stage biopharmaceutical company located in Redmond, WA and Princeton, NJ. It specializes in developing innovative cancer treatments using its established drug development platforms, focusing on bi-specific, multi-specific antibodies, and antibody-drug conjugates (ADCs). SystImmune has multiple assets in various stages of clinical trials for solid tumor and hematologic indications. Alongside ongoing clinical trials. SystImmune has a robust preclinical pipeline of potential cancer therapeutics in the discover and IND-enabling stages, representing cutting-edge biologics development. We offer an opportunity for you to learn and grow while making significant contributions to the company's success.
 

Systimmune Inc. is seeking an innovative and driven AI Scientist or Senior Scientist to contribute to our AI-driven small molecule drug design efforts. The successful candidate will work as part of our 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.

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.

Qualifications:

• 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