At Enko we believe that to create the future the world wants, we need to change the way the world grows. That is why we are on a mission to help farmers and growers provide for the planet, without it costing the Earth. Enko discovers and develops novel products for farmers to protect their crops from pests and disease. Our agricultural invention platform, Enkompass™TM, uses DNA encoded libraries, A.I. and machine learning to create more effective ways of finding and selecting the right treatments for the right targets, faster than anyone had ever imagined to be possible. By providing farmers with new tools to grow their crops successfully and look after their land sustainably, we hope to open the door for increased adoption of other new and emerging technologies being applied to agriculture.
Led by a team of proven scientists, entrepreneurs, and industry veterans, Enko’s innovative
science, agile design, and discovery of new modes of action is producing next-generation crop
protection solutions that will overcome the critical challenges facing industry. The industry has taken notice and we have active projects with many key industry players…with more to come!
At Enko we are as focused on building up our team members as we are on building a great platform and products. As such, we offer a competitive benefits package including health, dental, vision, life, and disability insurance, 401(k) matching contributions, and more.
We are currently seeking a Scientist/Sr Scientist Computational Chemistry (title commensurate with experience) to be a foundational member of the Enko Data Science team and to play an integral role in building out our computer aided drug design and virtual screening capabilities. A successful candidate will thrive in a growing and rapidly changing environment, enjoy collaborative projects, and seek to contribute to projects across the R&D portfolio. Specifically, the role has the following criteria:
- Collaborate with interdisciplinary teams to support hit discovery and lead optimization by leveraging expertise in computer aided drug design (CADD) methods.
- Drive the growth and refinement of Enko’s technical capabilities around CADD methods. Champion new methods and approaches, identify emerging opportunities, and serve as a subject matter expert on project teams.
- Perform structural analysis to evaluate new targets (e.g., druggability analysis, selectivity analysis, sequence analysis, etc.), and rationalize ligand binding with observed Structure Activity Relationships
- Generate compound proposals using computational methods (e.g., virtual library construction, structure-based screening, ligand-based screening, pharmacophore methods, etc.).
- Write new scripts/code to implement workflows that enable large-scale analyses of compounds and targets
(Note that these qualifications are intended as a guide, and we are open to candidates who meet some, but not all, provided they can succeed in the responsibilities of the role)
- Ph.D. in computational chemistry, structural biology, or related computational discipline
- Professional experience in drug or pesticide discovery/development
- Professional experience working with diverse, interdisciplinary teams including chemists, biologists, data scientists, software engineers, etc.
- Working knowledge of contemporary synthetic organic chemistry and a deep understanding of the principles underlying intimate molecular recognition events
- Experience with the hit to lead process from high throughput screening, DNA encoded library screening, or similar
- Expert knowledge in computational chemistry principles, including both structure-based and ligand-based methods
- Demonstrated expertise in using multiple commercial and/or academic molecular modeling packages (such as Schrodinger, Cresset, OpenEye, etc.)
- Fluency in one or more programming/scripting languages (e.g., Python, Bash, etc.), cheminformatics/data science tools (e.g., RDKit, Pandas, SKlearn, etc.) and experience working with cloud computing resources (e.g., AWS).
- A passion for writing reusable and reproducible data analysis pipelines and experience with tools (e.g., Docker, Data Version Control, etc.).
- Expertise in applying machine learning methods to enable compound discovery and design.