Computational Chemistry Scientist – Drug Discovery

TCWGlobal•San Diego, CA

2d•Onsite

About The Position

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.

Requirements

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
Experience using computational methods to support lead identification and optimization within multidisciplinary drug discovery programs
Programming or scripting experience using Python, C++, R, or similar languages
Familiarity with commercial molecular modeling platforms such as Schrödinger, CCG, OpenEye, or similar tools
Ability to interpret computational and experimental results, identify inconsistencies or anomalies, and communicate their scientific implications
Strong scientific reasoning, analytical thinking, and problem-solving skills
Ability to independently manage projects and deliver high-quality work with minimal supervision
Excellent written and verbal communication skills with the ability to clearly explain technical concepts and computational results
Strong organizational skills and ability to manage multiple deadlines while maintaining accuracy and efficiency
Collaborative mindset with the ability to work effectively across multidisciplinary scientific teams

Nice To Haves

Experience with free energy perturbation (FEP) or other advanced structure-based drug design methodologies
Postdoctoral experience in computational chemistry or cheminformatics
Experience developing machine learning or AI models for drug discovery
Experience working with large-scale chemistry or chemogenomic datasets
Experience assessing early-stage targets, including computational approaches to target druggability
Experience designing or implementing new computational chemistry methodologies or platforms
Experience working within pharmaceutical or biotechnology drug discovery programs
Experience providing technical mentorship or training to junior scientists

Responsibilities

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