Senior Scientist - Associate Director, Computational Chemistry

About Superluminal Medicines

Superluminal Medicines is a generative biology and chemistry company revolutionizing the speed and accuracy of how small molecule medicines are created. The Company's platform aims to create candidate-ready compounds with unprecedented speed using a combination of deep biology, computational and medicinal chemistry, machine learning, and proprietary big data infrastructure. We are expanding the team of talented scientists who seek to build the future of small molecule drug discovery with creativity and innovation.

We are seeking a high-impact Computational Chemist to join our integrated discovery team. In this role, you will be the computational engine of our programs, combining physics-based modeling, machine learning and structural biology to generate the quantitative predictions necessary to drive small molecule drug discovery. Beyond technical mastery, you will serve as a core strategic partner to medicinal chemists and biologists, designing compounds that move series and programs efficiently toward Go/No-Go decision points and candidate nomination.

• Integrate physics-based simulations with ML-driven descriptors to achieve the quantitative accuracy required to prioritize compounds for synthesis.
• Act as the computational lead on project teams, collaborating intimately with Medicinal Chemists to refine SAR and with Biologists to interpret assay results and target-ligand interactions.
• Design and prioritize chemical matter specifically aimed at hitting key program milestones, such as establishing in vivo POC, achieving selectivity windows, or optimizing ADMET profiles for candidate selection.
• Develop, validate and deploy workflows that use computational rigor to optimize the "Design-Make-Test-Analyze" cycles.
• Synthesize complex computational data into clear, actionable design hypotheses that non-computational stakeholders can use to make high-level program decisions.

• Ph.D. in Computational Chemistry, Biophysics, or a related field.
• 4-7+ years of experience in a biotech or pharma setting performing computational support for small molecule drug discovery.
• Demonstrated success in a cross-functional environment, with specific examples of computational designs that directly led to the achievement of program milestones.
• Exceptional ability to communicate the "why" behind a design to a diverse scientific audience.

• Design experience working in concert with medicinal chemistry teams to design synthesizable compounds that efficiently work towards defined goals of activity, affinity, selectivity, properties, etc.
• Advanced knowledge of physics-based and ML computational chemistry packages including knowing when and how to deploy various tools for maximum project impact.
• Experience working with structural biology teams to extract the most information possible from cryo-EM and x-ray crystallography experiments and using this to accelerate programs using structure-based drug discovery techniques.
• Proven experience using ML to scale physics-based insights, specifically in the context of large-scale virtual screening or FEP-guided lead optimization.
• A proven track record for innovation in structure-based small molecule drug discovery including developing and validating new workflows and techniques or expansions of existing ones.

• Expert level use of structure-based small molecule drug discovery software tools including protein preparation, docking, FEP, QM, conformer selection. (Schrodinger suite, Open Eye, MOE, etc)
• Ability to work directly in a Linux-based environment
• Familiarity with cloud computing infrastructure (AWS, GCS) is a plus
• Python scripting and prototyping experience including knowledge of key packages (RDKit, scikit-learn, numpy, pandas, pytorch, etc).

Equal Opportunity Statement: Superluminal Medicines is an Equal Opportunity Employer committed to a culturally diverse workforce. All qualified applicants will receive consideration for employment without regard to race; color; creed; religion; national origin; age; ancestry; nationality; marital, domestic partnership or civil union status; sex, gender, gender identity or expression; affectional or sexual orientation; disability; veteran or military status or liability for military status.