Ph.D. in Computational Chemistry, Cheminformatics, or related field with 5-8 years of life sciences experience.
Expertise in ligand-based and structure-based computational drug design.
Hands-on experience with Vortex, Schrödinger Suite, MOE, and familiarity with AMBER and Gromacs.
Excellent communication skills for presenting complex concepts to diverse audiences.
Ability to manage multiple priorities in a fast-paced environment.
Proficiency in Python and relevant data science packages like RDKit and Pandas.
Strong analytical skills with a track record of publications and patents in drug discovery.
Responsibilities
Apply ligand-based and structure-based drug design methods to enable compound design.
Collaborate with multidisciplinary teams including medicinal chemists and biologists.
Communicate findings to both technical and non-technical audiences.
Build and maintain computational tools and workflows for drug discovery datasets.
Analyze experimental data to generate actionable insights for project decisions.
Develop novel computational methodologies to enhance the molecular glue discovery platform.
Manage R&D software platforms and APIs for research purposes.
Benefits
Collaborative work environment with multidisciplinary teams.
Opportunity to work on cutting-edge computational methods and AI/ML approaches.
Access to advanced software platforms and tools for drug discovery.
Potential for impactful contributions to drug discovery and development.
Support for professional development through publications and presentations.
Full Job Description
Position Summary
We are seeking a highly motivated and hands-on Computational Chemist to join our team. In this role, you will apply cutting-edge computational methods and AI/ML approaches to accelerate drug discovery from hit identification through clinical candidate nomination. You will be a key contributor to our molecular glue platform, working at the intersection of structure-based design, cheminformatics, and data science to drive the DMTA cycle forward.
This is a highly collaborative role that requires both deep technical expertise and the ability to translate scientific questions into actionable strategies across multidisciplinary teams.
Responsibilities
Apply ligand-based and structure-based drug design approaches (including molecular dynamics simulation, TI/FEP+, water analysis, protein dynamics, etc) to enable hypothesis-driven compound design and reaction-based library design.
Partner closely with medicinal chemists, biologists, biophysicists, protein scientists, proteomics, DMPK, and informatics teams to support early discovery and pipeline programs.
Communicate findings clearly and compellingly to both technical and non-technical audiences across all levels of the organization.
Build and maintain computational tools, workflows, AI/ML models, and databases for the processing and analysis of complex drug discovery datasets, including DNA-encoded library (DEL) data analysis, DEL library construction, druggability evaluation, target/presenter gluability and ADMET prediction.
Analyze and interpret experimental data (such as ternary and binary biochemical, biophysical and cellular assay data, X-ray/Cryo-EM structures, XL-MS/HDX/NMR, DEL data, proteomics screening) to generate actionable insights that guide project decisions and impact the design-make-test-analyze (DMTA) cycle.
Drive development and implementation of novel computational methodologies - including AI/ML, data exploration, and visualization to strengthen and expand our molecular glue discovery platform.
Manage and maintain R&D software platforms including Vortex, Schrödinger Suite (Maestro, LiveDesign, PyMOL), MOE, and RESTful APIs.
Qualifications
Required
Ph.D. in Computational Chemistry, Cheminformatics, Chemistry, Computer Science, Data Science, or a related field, with 5-8 years of experience in life sciences; or equivalent combination of education and experience.
Demonstrated expertise in ligand-based and structure-based computational drug design and optimization.
Hands-on experience with Vortex, Schrödinger Suite (Maestro, LiveDesign, PyMOL), MOE, and/or AWS, and familiar with AMBER, Gromacs, NAMD, VMD.
Excellent written and verbal communication skills, with a proven ability to present complex scientific concepts to diverse audiences.
Ability to manage multiple priorities independently in a fast-paced, dynamic environment.
Proficiency in Python and relevant cheminformatics/data science packages (e.g., RDKit, Scikit-learn, Pandas, NumPy); comfort working in a Linux environment.
Strong analytical and problem-solving skills with the ability to derive meaningful insights from complex, multi-dimensional datasets.
A track record of publications, patents and conference presentations in drug discovery.
Preferred
Experience with molecular glues, PROTACs, RIPTACs, or other heterobifunctional degrader or non-degrader modalities.
Familiarity with FEP, molecular dynamics and medicinal chemistry design principal.
Experience with DEL data analysis workflows and target/presenter pair prediction.
Familiarity with cheminformatics and cofolding tools, explainable AI (xAI), generative AI such as REINVENT, LiblNVENT.
Experience building scalable research infrastructure with automation, good design patterns, and reusability in mind.
National salary range. Salary will be determined by many factors, including but not limited to, specific competencies, relevant education, qualifications, certifications, experience, skills, performance, location and business or organizational needs.
