Computational Chemistry Scientist – Drug Discovery

Computational Chemistry Scientist – Drug Discovery (Contract)

Pay Rate:
Up to $54.49/hour

Duration: 12-Month Temporary Assignment

Hours:

Full-Time

Start Date:

ASAP

Location:

San Diego, CA (Onsite)

Summary

We are seeking an experienced Computational Chemistry Scientist to support drug discovery programs through computationally driven approaches for the design and optimization of novel compounds. In this role, you will apply computational chemistry, molecular modeling, cheminformatics, and advanced computational methods to help identify and optimize compounds with balanced target, DMPK, and in-vivo properties.

The ideal candidate brings hands-on experience applying computational methodologies to real-world drug discovery programs, with expertise in one or more areas such as structure-based design, free energy perturbation (FEP), virtual screening, molecular dynamics, quantum chemistry, machine learning, or modern AI. This individual will work closely with multidisciplinary project teams, communicate computational insights clearly, and independently drive projects while maintaining a highly collaborative approach.

What You'll Do

• Drive computationally supported drug discovery projects from early lead identification through advanced lead optimization

• Apply molecular modeling and computational chemistry approaches to design and optimize compounds with balanced target, DMPK, and in-vivo properties

• Serve as a subject matter expert in one or more computational discovery approaches, including structure-based design, FEP, virtual screening, quantum chemistry, molecular dynamics, machine learning, or AI

• Apply molecular modeling techniques including pharmacophore analysis, library design, virtual high-throughput screening, diversity and similarity analysis, and scaffold hopping

• Perform protein-ligand modeling using commercial docking platforms and molecular dynamics methods, including post-docking analysis

• Develop and apply machine learning and AI models to predict DMPK and in-vitro biology endpoints and support more efficient multi-parameter optimization and compound design

• Leverage large chemistry and chemogenomic datasets, including relevant public-domain datasets, to generate predictive insights for drug discovery programs

• Integrate multiple computational approaches, including machine learning predictions and structure-based or ligand-based modeling, to inform compound design strategies

• Independently drive structure-based design projects, including consideration of target protein flexibility and other factors affecting compound optimization

• Serve as a computational chemistry representative on multidisciplinary project teams, communicating results and recommendations to support project decision-making

• Present computational findings, modeling results, and scientific insights to discovery project teams and cross-functional stakeholders

• Contribute innovative ideas and advanced methodologies to strengthen computational chemistry capabilities and support multiple discovery programs

• Lead or advance one to two computational technology platforms, developing new methods that align with project needs and broader computational chemistry strategies

• Collaborate with Research stakeholders to exchange key findings, align on project strategies, and advance compound development

• Provide training, mentorship, or technical guidance to junior team members as needed

What You Bring

• Bachelor's degree in Chemistry or a related field with 5+ years of relevant experience

OR

• Master's degree in Chemistry or a related field with 3+ years of relevant experience

OR

• PhD in Computational Chemistry or a related discipline with relevant industry or research experience

• Hands-on experience applying computational chemistry and molecular modeling approaches to drug discovery

• Experience in one or more areas including protein-ligand docking, post-docking analysis, molecular dynamics, homology modeling, quantum chemistry, pharmacophore analysis, or diversity analysis

• Strong understanding of physical chemistry, computational chemistry, cheminformatics, molecular modeling, and their application to compound design and optimization

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