Deep Apple Therapeutics is a Bay Area biotechnology company with a focus on combining capabilities in molecular docking and structural biology to create a nucleus for accelerated drug discovery through advanced computer aided drug design technologies. Deep Apple Therapeutics is applying a state-of-the-art technology research platform based on advanced computational modeling and large-scale compound docking (LSD), cryo-EM based structural biology, and deep learning to drug discovery.

We are seeking highly motivated and energetic scientists to join a dynamic and well-funded research organization. We have multiple openings in structural biology, deep learning and in vitro biology. Deep Apple Therapeutics offers a highly competitive compensation, equity, and benefits package.

Role Description

Our innovation and value-driven organization is seeking to hire an experienced Scientist to join our rapidly growing team. This position requires broad technical experience in deep learning, and a strong emphasis on an industrial mindset underpinned by critical thinking, multitasking, and the ability to propose new approaches and ideas.

Key Responsibilities

• Contribute to the development and docking of large virtual libraries for screening and multi-parameter lead optimization for small molecules.
• Continuing research in areas of ML/AI approaches to expand into novel chemical space.
• Work closely with the in vitro biology and structural biology teams to iterate on compound progression.
• Develop deep learning models to accelerate and optimize selection of target compounds from within billion membered virtual libraries.
• Develop deep learning models for Cryo-EM particle analysis and the extraction of protein dynamics and conformational landscapes.

Required Qualifications and Skills

• PhD with at least 2-years of experience in computational biology, computational chemistry or a related discipline.
• Experience developing and applying computational chemistry and machine learning approaches towards chemistry problems of biological interest.
• Comfort with deep learning frameworks, machine learning best practices and computational methods involving molecular dynamics.
• Attention to detail with rigorous scientific thinking and clear communication supported by appropriate documentation.
• Creative mindset with a willingness to propose new approaches and ideas.