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Molecular Docking information

What is a Molecular Docking job?

A molecular docking job involves using computational techniques to predict the interaction between molecules, such as a drug candidate and a target protein. Researchers in this field use specialized software to model binding affinities, optimize molecular structures, and analyze potential therapeutic effects. This role is often found in pharmaceutical research, bioinformatics, and drug discovery, requiring expertise in molecular modeling, chemistry, and computational biology.

What are the key skills and qualifications needed to thrive in the Molecular Docking position, and why are they important?

To thrive in a Molecular Docking role, you need strong expertise in computational chemistry, structural biology, and molecular modeling, often supported by an advanced degree in a related field. Familiarity with molecular docking software (such as AutoDock or Schrödinger Suite), programming languages (like Python or R), and experience with high-performance computing are typically required. Attention to detail, analytical thinking, and effective communication are valuable soft skills for collaborating and presenting complex findings. These capabilities are essential for accurately predicting molecular interactions, driving scientific discovery, and contributing to successful multidisciplinary projects.

What does a typical day look like for someone working in Molecular Docking?

A typical day in Molecular Docking often involves running computational simulations to predict how small molecules interact with biological targets, analyzing and interpreting the resulting data, and preparing reports or presentations for research teams. You may also spend time troubleshooting software issues, developing scripts to automate workflows, and staying up to date with the latest scientific literature. Collaboration is common, as you’ll likely work closely with medicinal chemists, structural biologists, and other computational scientists to refine hypotheses and guide experimental design. This multifaceted environment provides opportunities to continually learn and apply new techniques, making the day-to-day work both intellectually stimulating and impactful.

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What states have the most Molecular Docking jobs? States with the most job openings for Molecular Docking jobs include:
Director, AI Small Molecule Drug Design

Director, AI Small Molecule Drug Design

SystImmune

Redmond, WA • On-site

Full-time

Posted 12 days ago


Job description

Job Summary:
SystImmune is a leading and well-funded clinical-stage biopharmaceutical company specializing in developing innovative cancer treatments. They are seeking a Director to define, build, and lead their AI-enabled small molecule design function, integrating AI-driven design into drug discovery efforts while collaborating with cross-disciplinary teams.
Responsibilities:
• Own the design, development, and optimization of AI-driven small molecule drug design pipelines, setting strategy and technical direction to predict molecular properties, enable virtual screening, and improve drug-like characteristics across programs
• Lead the application of advanced AI methodologies, including generative modeling, deep learning, and reinforcement learning, to generate novel small molecules and predict target interactions, with accountability for scientific rigor and translational impact
• Oversee and advance AI-based molecular docking capabilities (e.g., DiffDock), ensuring models are validated, scalable, and meaningfully improve binding affinity predictions, lead optimization, and virtual screening efficiency
• Partner closely with medicinal chemistry, biology, DMPK, and computational biology leaders to integrate AI methods into end-to-end drug discovery workflows, ensuring seamless transition from computational design to experimental validation
• Lead AI-enabled efforts supporting drug manufacturing and developability, including optimization of synthesis routes, yield prediction, and manufacturability assessment for small molecule drug candidates
• Direct large-scale virtual screening strategies, leveraging AI models to explore expansive chemical space, prioritize compound libraries, and identify high-quality lead candidates aligned with therapeutic objectives
• Guide interpretation of computational and AI-generated data, translating complex analyses into clear recommendations that inform decision-making around pharmacokinetics, toxicity, efficacy, and compound progression
• Drive the development, deployment, and evolution of AI-based software tools and platforms, ensuring scalability, robustness, and usability across cross-disciplinary scientific teams
• Establish standards for data integration and insight generation from large-scale chemical and biological datasets, enabling optimization of drug candidates for efficacy, safety, and pharmacokinetic profiles
• Stay at the forefront of advances in AI and computational chemistry, particularly in AI small molecule generation, molecular docking, and virtual screening, and strategically apply emerging methods to continuously improve discovery productivity
• Build, mentor, and lead a high-performing small molecule design team, fostering an inclusive, high-velocity culture while serving as a visible scientific leader internally and externally
Qualifications:
Required:
• PhD or equivalent in Computational Chemistry, Bioinformatics, Biophysics, Machine Learning or related field
• 10+ years of experience applying computational chemistry and AI to small molecule drug design, with a proven track record advancing compounds into lead and clinical stages, including hands-on expertise in AI-based molecular generation, docking, virtual screening, and manufacturability-aware design
• Deep experience building and owning computational design platforms and workflows
• Proven track record of applying AI/ML in drug discovery
• Hands-on expertise in structure-based drug design, ligand-based design, generative methods, and predictive modeling
• Strong technical fluency in relevant tools and programming languages such as Python, R, or C++, and experience with machine learning frameworks (e.g., TensorFlow, PyTorch)
• Excellent communicator with a bias for action and delivering results
Preferred:
• Experience in antibody-centric modalities and multi-specific projects
• Experience scaling teams and establishing operating rhythms in early programs
• Prior success integrating design teams into cross-disciplinary drug discovery organizations
Company:
SystImmune is a biotechnology company. Founded in 2014, the company is headquartered in Redmond, USA, with a team of 51-200 employees. The company is currently Growth Stage.