Job Description

The Opportunity:

Stealth Biotech seeking a highly motivated and innovative computational biochemistry to join our therapeutic discovery team. In this pivotal role, you will leverage cutting-edge Computer-Aided Drug Design (CADD) methodologies, with a strong emphasis on computational AI, to accelerate the discovery and optimization of novel antibody therapeutics. You will be instrumental in designing and analyzing antibody candidates, contributing directly to our capability of turning ideas to clinical candidates in a rapid pace. In addition, you will be responsible for establishing Artificial Intelligence and Machine Learning (AI/ML)–based design/optimizations of therapeutic antibodies in-house or by working with outside AI companies.

Responsibilities:

• Design and execute in silico antibody design strategies utilizing a range of CADD tools and computational AI methods.
• Contribute to the development and implementation of efficient computational workflows and tools for antibody designs and optimizations. These include but are not limited to:
• Predict and rank our antibody lead molecules using CADD tools and state-of-art prediction tools.
• Build and refine three-dimensional models of antibodies using structural bioinformatics/computation tools.
• Perform and analyze molecular docking studies to predict antibody-target binding interactions and identify favorable orientations.
• Employ computational tools to optimize antibody affinity, specificity, and other critical developability properties.
• Develop and implement virtual screening strategies to analyze large antibody libraries and prioritize promising candidates for experimental validation.
• Conduct and interpret molecular dynamics simulations to evaluate the stability and dynamics of antibody-target complexes, guiding optimization efforts.
• Apply machine learning algorithms to analyze large datasets, predict antibody efficacy, and optimize design strategies based on historical and experimental data.
• Maintain accurate and detailed records of all computational work and results.
• Present findings and contribute to scientific discussions within the team and potentially with external collaborators.
• Collaborate closely with experimental scientists (biologists, immunologists) to interpret computational results and guide experimental validation efforts (e.g., ELISA, SPR, functional assays).
• Stay current with the latest advancements in CADD, computational AI, and antibody engineering.

Qualifications:

• Ph.D. in Computational Chemistry/biochemistry, Biophysics, Pharmaceutical Sciences, Chemical Engineering, or a related field with a strong emphasis on molecular modeling and simulation.
• Proven experience (typically 2+ years post-PhD or equivalent industry experience) in applying CADD principles to protein design, preferably with a focus on antibodies or other biologics.
• Strong theoretical and practical understanding of molecular modeling, docking, molecular dynamics simulations, and virtual screening techniques.
• Deep understanding of antibody structure, function, and engineering principles.
• Experience in applying machine learning methods to biomolecular design and analysis is highly desirable.
• Proficiency in using relevant software packages (e.g., MOE, Schrödinger, Rosetta, Amber, Gromacs, OpenMM) and scripting languages (e.g., Python).
• Excellent analytical, problem-solving, and critical thinking skills.
• Strong communication and collaboration skills, with the ability to effectively interact with both computational and experimental scientists.
• Ability to work independently and as part of a multidisciplinary team in a fast-paced environment.
• A strong publication record in peer-reviewed journals is preferred.

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