We are a seed stage biotechnology company dedicated to accelerating early stage small molecule discovery by combining massively parallel bench science and computation. Our technology rapidly analyzes 100-1000x more compounds than traditional methods by using a variety of DNA Encoded Libraries and applying machine learning to design high throughput follow-on experiments. By adopting a tightly coupled, iterative cycles of computational and bench science we reduce the cost of early stage drug discovery while tackling hard, medically relevant targets.
To build this platform we have raised a substantial amount of capital (>2 year runway) and are assembling a unique, cross-functional team with equal parts computational, biochemical and chemical talent. We believe that merging these disciplines, with each one complementing the other, is the best way to find new medicines.
We are looking for an entrepreneurial computational chemist to help us prove it.
- Apply cutting edge methods to identify optimal building blocks and design DNA encoded libraries
- Lead adoption of best-practice computational chemistry principles in the organization
- Develop tools to guide follow-on wet lab experiments based on the results from DNA encoded libraries and other sources of information
- Build machine learned models for compound activity
- Operate virtual screens
- Run docking and/or molecular dynamics studies
- Integrate information from outside sources (such as patents, meetings and literature) into program development.
- Communicate effectively as a member of a highly interdisciplinary team
- Ph.D. in Chemistry, Biology, Biophysics, Computer Science (or related fields) with demonstrated computational chemistry focus
- 2+ years of postdoctoral or industry experience is a plus
- Comfort with Python or similar programming language
- Practical experience with computational chemistry toolkits such as RDKit, OpenEye, or Schrodinger
- Machine learning experience is a strong plus
- Strong track record of solving computational chemistry challenges demonstrated with publications or patents
- Expertise with two or more of: compound library design, docking studies, virtual screening, druggability analysis, structure-based lead optimization, molecular dynamics
- Working knowledge of protein-ligand interactions and other biochemical and chemical concepts
- Effective decision making in a rapidly evolving environment
- Independent and self-motivated while also being a great collaborator
- Strong communication and coaching skills