BioTalent Ltd
A pioneering biotech company is seeking a Director of AI-Driven Protein Design to lead computational strategies for the discovery and engineering of novel biologics. This role will focus on integrating AI-driven design principles, including generative models and diffusion-based approaches, to optimize protein and antibody therapeutics. The successful candidate will be instrumental in shaping next-generation computational pipelines, leveraging cutting-edge machine learning and structural modeling techniques to accelerate drug discovery.
Key Responsibilities
Develop and enhance AI-powered protein design pipelines, utilizing structure-based and sequence-based modeling to optimize biologic therapeutics. Implement diffusion models and generative AI techniques to design novel peptides, antibodies, and de novo proteins. Apply computational methodologies to predict, refine, and optimize molecular interactions, stability, and function. Work closely with experimental teams to validate in silico predictions, integrating insights from high-throughput screening and yeast display platforms. Contribute to lead optimization by leveraging multi-parametric molecular property modeling and data-driven approaches. Analyze, interpret, and present findings to internal and external stakeholders, guiding strategic decision-making in therapeutic development. Stay ahead of emerging technologies in computational protein design, generative modeling, and AI-driven molecular engineering. Qualifications
PhDin bioengineering, biophysics, computational biology, AI/ML, or a related field. Extensive experience with generative AI, diffusion models, and large-scale molecular simulations applied to protein design. Hands-on expertise in state-of-the-art macromolecular modeling tools such as AlphaFold, RoseTTAFold, RFdiffusion, ProteinMPNN, GROMACS, MOE, Schrödinger, or similar platforms. Strong background in computational antibody design, including screening and ranking techniques (ELISA, flow cytometry, BLI/OCTET, etc.). Track record of integrating deep learning-based approaches with experimental validation in a biotech or pharma setting. Experience in leading cross-functional teams and applying AI/ML-driven solutions to biologics discovery is highly desirable.
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Develop and enhance AI-powered protein design pipelines, utilizing structure-based and sequence-based modeling to optimize biologic therapeutics. Implement diffusion models and generative AI techniques to design novel peptides, antibodies, and de novo proteins. Apply computational methodologies to predict, refine, and optimize molecular interactions, stability, and function. Work closely with experimental teams to validate in silico predictions, integrating insights from high-throughput screening and yeast display platforms. Contribute to lead optimization by leveraging multi-parametric molecular property modeling and data-driven approaches. Analyze, interpret, and present findings to internal and external stakeholders, guiding strategic decision-making in therapeutic development. Stay ahead of emerging technologies in computational protein design, generative modeling, and AI-driven molecular engineering. Qualifications
PhDin bioengineering, biophysics, computational biology, AI/ML, or a related field. Extensive experience with generative AI, diffusion models, and large-scale molecular simulations applied to protein design. Hands-on expertise in state-of-the-art macromolecular modeling tools such as AlphaFold, RoseTTAFold, RFdiffusion, ProteinMPNN, GROMACS, MOE, Schrödinger, or similar platforms. Strong background in computational antibody design, including screening and ranking techniques (ELISA, flow cytometry, BLI/OCTET, etc.). Track record of integrating deep learning-based approaches with experimental validation in a biotech or pharma setting. Experience in leading cross-functional teams and applying AI/ML-driven solutions to biologics discovery is highly desirable.
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