About the role
Eikon Therapeutics is a new biopharmaceutical company employing revolutionary technology at the intersection of chemistry, engineering, computation, and biology to discover novel treatments for life-threatening diseases. Eikon’s discovery platform is built on groundbreaking innovations from its founders (Nobel Prize, 2014), culminating in the creation of microscopes which enable real time, molecular-resolution measurements of protein movement in living cells, thereby unlocking otherwise intractable classes of proteins as drug targets. Position We seek a Principal Scientist in Computational Chemistry with a demonstrated ability of successfully applying in silico technologies to drive the discovery of quality lead-like molecules against hard to drug therapeutic targets. The candidate will apply AI/ML to impact library design, HTS hit identification, virtual screens, and (virtual) hit expansion efforts towards hit-to-lead across multiple projects.
The role involves protein modeling, AI co-folded structure/affinity predictions, binding site/ligand-ability assessment, homology-based chemistry mining, virtual screening and purchasable compound procurement. You will daily collaborate closely with biologists, medicinal chemists, software engineers and machine learning experts. As an integral member of our growing Drug Discovery team, you will make a real and direct impact on accelerating the drug discovery process and ultimately benefiting patients. About You You’re a computational chemistry/cheminformatics expert who loves to solve difficult problems and are not discouraged by challenges. You have a passion for drug discovery and thrive in a fast-paced, dynamic environment. You are curious, love challenges, and enjoy pushing the edge of technology to solve important problems with creative solutions. You’re collaborative by nature, resourceful, open-minded, data-driven, and can operate with a sense of urgency. What You’ll Do Deliver virtual screening workflows that leverage physics- and AI-based modeling methods involving ligand- and structure-based methods, ensemble docking, molecular dynamics, and free energy calculations Provide computational chemistry program support across our drug discovery pipeline spanning target selection, HTS, hit identification/calling, hit to lead, and lead optimization Provide guidance on the application of modern cheminformatics, ML/AI methods for library design (e.g. diversity, focused, fragment, DEL) and analyzing large datasets from HTS campaigns to building predictive models from them Routinely employ expert knowledge and analysis of SAR, physiochemical and ADMET properties Work alongside medicinal chemists to further triage emerging hits for synthesis/purchase Collaborate closely with our data science team to integrate cutting edge AI/ML tools into our chemistry and drug discovery processes