About the job

The ideal candidate will have a strong, demonstrated track-record of applying computational chemistry to accelerate drug-discovery processes, as well as excellent cross-disciplinary communication skills. The candidate should enjoy working in multi-disciplinary teams comprised of medicinal chemists, enzymologists, molecular, cell, and chemical biologists and DMPK experts, playing a critical role in program strategy, hit finding, compound design and prioritization, and drug optimization. A deep knowledge of current techniques in computational chemistry and cheminformatics is required, as well as a drive to explore and implement AI/ML techniques to enhance the drug-discovery process.

The best candidate for this role will have demonstrated success in these responsibilities:

  • closely with our medicinal chemistry and biology teams in hit-finding initiatives and to provide timely compound design support and to rationalize structural biology, chemical biology, and in vitro and in vivo SAR data driving towards drug candidate selection.
  • identifying and analyzing key challenges during hit and lead optimization processes to propose novel testable solutions.
  • in new target and druggability assessment by providing protein structural and published compound analysis.
active monitoring of new computational and ML techniques and promoting introduction of new methodologies to optimize the drug-discovery process.
  • summary results and relevant literature to teams and leadership, promoting and championing strategies to enhance our drug-discovery platform and contributing to external publications and presentations.
  • to and fostering a work environment focused on scientific excellence and integrity, open communication, and collaboration.

The ideal candidate for this role will have the following qualifications:

  • Ph.D. degree in computational chemistry, organic chemistry, or related field with at least 8-10 years of experience in small-molecule drug discovery in industry.
  • Diverse expertise applying both structure and ligand-based design in drug-discovery teams including homology modeling, docking, MD, relative binding free energy calculations, ADMET property prediction, virtual screening and hit expansion.
  • Deep experience across commercial molecular modeling packages (e.g. Schrodinger, MOE, OpenEye, Molsoft) and cheminformatics packages as well as experience in scientific scripting.
  • Hands-on experience with the application of ML in drug discovery is desirable, e.g. application to generative molecular design and in property prediction as well as a current knowledge of new developments impacting the drug discovery process.   • Leadership skills with a demonstrated record of collaboration and accomplishment including a passion for promoting new computational approaches to enhance the drug discovery process.   • Ability to prioritize and organize across multiple workflows to drive the drug discovery process.   • Effective ability to summarize and document data and results.   • Excellent communication skills with the ability to convey complex messages to diverse audiences.