Principal Computational Chemist

• 
  

  Computational Drug Design: Design and optimize small molecules targeting specific therapeutic areas by leveraging Aqemia's drug design engine. Systematically model ADME, toxicity, and selectivity properties.

  
  


• 
  

  Communication: Your ability to clearly present and communicate your findings to stakeholders at all levels and across functions will be essential. You will ensure that your conclusions are robust and data-driven, fostering trust and collaboration within the team.

  
  


• 
  

  Technology Application: You will apply and validate Aqemia's baseline technologies, which include generative AI algorithms and physics-based methods. Your expertise will ensure that these technologies are used effectively to accelerate drug discovery.

  
  


• 
  

  Feedback and Improvement: Your project experience will provide valuable insights. You will offer systematic feedback on Aqemia's platform, driving continuous improvement and enhancing our technological capabilities.

  
  


• 
  

  Innovation and Advocacy: You will proactively propose and implement new approaches to advance our Drug Discovery programs. Your innovative mindset will help improve Aqemia's technology for greater accuracy, speed, and scalability, keeping us at the forefront of scientific discovery.

  
  


• 
  

  Interdisciplinary Collaboration: Your collaboration with machine learning engineers, project managers, medicinal chemists, and physicists will be pivotal in pushing the boundaries of what is possible in drug discovery.

  
  


• 
  

  Mentoring & Technical Leadership: Mentor junior computational chemists on project execution and methodology. Lead by example in rigor, reproducibility, and scientific communication. Contribute to hiring and team development for the computational chemistry team.

  
  

• Industry Experience: At least 10 years of experience in pharmaceutical, biotech or CRO companies, with a focus on computational chemistry for small molecule drug discovery.


• Deep expertise in structure-based and ligand-based drug design: homology modeling, docking, SAR analysis, virtual screening, pharmacophore design, QSAR, ADMET property modeling, multi-parameter optimization.


• Drug Discovery Contributions: Proven success in advancing compounds from hit identification to pre-clinical candidates.


• Diverse Target Experience: Experience with various targets, including kinases, GPCRs, phosphatases, ion channels, and bromodomains.

Technical skills

• Strong experience in structure-based drug design and ligand focused techniques such as: Protein homology modeling, small molecule docking and pharmacophore hypothesis generation, virtual screening, structure-activity relationship (SAR) analysis.


• QSAR and ADMET property modeling, multi-property optimization-based compound design, and physics-based methods (FEP, MD, MM/GBSA).


• Familiarity with standard computational chemistry/cheminformatics packages (e.g., RDKit, OpenMM, OpenFE, CCDC).


• Extensive knowledge of structural biology.


• Solid understanding of medicinal chemistry principles and computational methods for optimizing drug properties.


• Ability to analyze chemical data and identify trends using statistical methods to ensure reproducibility and data-driven decision-making.


• Proficiency in Python in Linux/UNIX environments.

Nice-to-Have

• Prior experience with generative AI methods in drug discovery.


• Experience optimizing or evaluating generative models (assessing chemical diversity, evaluating model-generated molecules for quality/novelty, training/fine-tuning generative architectures).


• Familiarity with co-folding algorithms: Experience integrating structural predictions into computational design workflows.