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The Computational Chemistry Core at Dana-Farber Cancer Institute combines structure-based drug design, computational biology, cheminformatics to support chemical biology and drug discovery programs. We are seeking a highly motivated individual to join the team as a scientist I/II who will work on computational molecular modeling projects in the Center for Therapeutic Discovery and Fischer lab in Chemical Biology to guide the discovery and optimization of novel small molecules on targets of important therapeutic implications. While we consider various experiences, a background in small molecule drug discovery and an interest in understanding how molecular machines work and the desire to tackle a major problem of great significance are essential.

Our laboratory is based at Dana-Farber Cancer Institute in Longwood Center. Join our highly interactive team of scientists to work on small molecule discoveries for providing understanding of new signaling processes and leveraging these insights for therapeutic development.

Located in Boston and the surrounding communities, Dana-Farber Cancer Institute brings together world-renowned clinicians, innovative researchers and dedicated professionals, allies in the common mission of conquering cancer, HIV/AIDS and related diseases. Combining extremely talented people with the best technologies in a genuinely positive environment, we provide compassionate and comprehensive care to patients of all ages; we conduct research that advances treatment; we educate tomorrow's physician/researchers; we reach out to underserved members of our community; and we work with amazing partners, including other Harvard Medical School-affiliated hospitals.

• Work closely with medicinal chemists and apply advanced molecular modeling methods to identify hits and to guide the optimization of compounds for drug discovery projects

• Work closely with structure biologists to generate molecular models and mechanistic hypotheses for key molecular machinery through state-of-the-art computational modeling including deep learning methods

• Independently plan, manage, and perform research and projects. Monitor milestones and ensure deliverables are completed on time.

• Take an active role in development of novel computational methods for the designs of small molecule modulators

• Collect and analyze data, generate hypothesis, and identify next steps based on outcomes

• Communicate project deliverables to the collaborators and project leads

• Maintain clear and accurate records of research work (e.g., codes, research methods, results/findings)

• PhD. or equivalent degree in computational chemistry, or related scientific fields.

• In depth knowledge of computational methods used in small molecule protein interaction modeling

• More than 2 years of post-PhD experience in small molecule drug discovery projects in industrial or academic environments with demonstrated impact on lead optimization

• Ability to interpret and analyze experimental data, prepare research publications, and communicate results.

• Must demonstrate outstanding personal initiative and the ability to work effectively as part of an interdisciplinary team to discuss and present results, share ideas accurately, both in writing and verbally.

• Demonstrated ability to learn quickly.

• Excellent analytical, organizational and time management skills.

Expertise in the following areas:

• Strong programming skills and knowledge of UNIX/Linux shell scripting.

• Software skills: Extensive experience with Schrodinger suite, RDKit, Amber MD package, and Rosetta.

• Data analysis Skills: Familiarity with principles of experimental design and modern data analysis paradigms is required.

• Experience with modern machine learning methods is desired.

• Strong interpersonal skills – ability to effectively interact with all levels of staff and external contacts.

Dana-Farber Cancer Institute is an equal opportunity employer and affirms the right of every qualified applicant to receive consideration for employment without regard to race, color, religion, sex, gender identity or expression, national origin, sexual orientation, genetic information, disability, age, ancestry, military service, protected veteran status, or other groups as protected by law.

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