Computational Protein Design (GPCR)

BioMap is a platform company that integrates AI technology with cutting-edge biotechnology. We are committed to achieving breakthroughs in target discovery and drug design, and bringing global first-in-class medicine for unmet medical needs in the areas of immune-oncology, autoimmune diseases, fibrosis, and aging-related diseases.
Location: Menlo Park. This role is preferably based in The Bay Area, but we are open to discussing other locations in the United States.
Keywords: GPCR, modeling, Protein Design, drug design, antibody, nanobody, and peptide
General Summary: We are seeking a highly skilled and motivated Computational Modeler with expertise in G Protein-Coupled Receptors (GPCRs) to join our dynamic research team. As a Computational Modeler, you will play a crucial role in designing, developing, and implementing computational models and simulations to understand the structure, dynamics, and function of GPCRs. You will collaborate closely with interdisciplinary teams, including biologists, chemists, and computational scientists, to contribute to the discovery and optimization of novel therapeutics targeting GPCRs.
Responsibilities:

• Utilize computational techniques to construct and refine 3D models of GPCRs, incorporating knowledge of protein structure, dynamics, and ligand interactions.
• Experience with antibody, nanobody, or peptide design towards GPCRs would be beneficial.
• Conduct molecular dynamics simulations, docking studies, and virtual screening to explore the binding interactions between GPCRs and ligands.
• Collaborate with experimental teams to integrate computational and experimental data, and validate model predictions.
• Develop and optimize computational workflows and protocols for efficient data analysis and visualization.
• Stay up-to-date with the latest advancements in computational modeling and GPCR research, and apply cutting-edge techniques to drive innovation.
• Contribute to scientific publications, reports, and presentations to effectively communicate research findings and insights.
• Collaborate with internal teams and external partners to support drug discovery projects and contribute to the development of therapeutic candidates.

Qualifications:

• PhD or Master's degree in computational biology, bioinformatics, computational chemistry, or a related field.
• 3+ years of experience and proficiency in applying a wide range of machine learning algorithms, such as deep learning, random forests, support vector machines, or neural networks, to analyze and interpret complex biological data related to G Protein-Coupled Receptors (GPCRs).
• 3+ years of experience in developing and implementing novel computational approaches, leveraging machine learning techniques, to extract meaningful insights from large-scale genomics, proteomics, or structural datasets of GPCRs.
• Strong familiarity with relevant bioinformatics tools, software packages, and libraries commonly used for machine learning in the field of GPCR research.
• Proven track record of successfully applying machine learning methods to address key research questions related to GPCR biology, function, or drug discovery.
• Proficiency in molecular modeling software (e.g., Schrödinger suite, CHARMM, GROMACS) and scripting languages (e.g., Python, R) for data analysis.
• Familiarity with GPCR biology, structure, and pharmacology.
• Excellent problem-solving skills and ability to interpret and communicate complex scientific concepts.
• Strong collaborative and communication skills to effectively work in a multidisciplinary team.
• Track record of scientific publications and contributions to computational biology or GPCR-related research is desirable.

Join our team and contribute to the exciting field of GPCR research through computational modeling and simulation techniques. Together, we aim to advance our understanding of GPCRs and drive the development of innovative therapies targeting this important class of drug targets.
BioMap is an equal opportunity employer and values diversity at our company. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.