Sr. Director, Computational Chemistry & Molecular Design

About Psivant Therapeutics

Psivant Therapeutics is creating better therapeutics through an integrated molecular design paradigm that leverages best-in-class physics-based AI technologies arising from decades of research and innovation. Our focus is first-in-class inflammation and immunology (I&I) small molecule therapeutics. We have assembled a diverse team with extensive experience across research disciplines ranging from physics and computer science to chemistry, biology, biochemistry, and structural biology. This continuum of complementary skills is essential for driving our scientific vision and bringing meaningful therapeutics to patients in need. We are patient-inspired, perseverant, synergistic, supportive, intrepid, and inclusive. If you are inspired to bring better medicines to patients within a collaborative organization that combines advanced computation with drug discovery, then Psivant could be the right place for you.

Here at Psivant – we are a lean, high performing team. We want individuals who are both smart and collaborative– who want to get things done. We push boundaries for growth in both ourselves and our science. We look for people who thrive in high-paced, nimble, early-stage biotech environments.

Read More about Us

Team: https://psivant.com/company/team/

Our Story: https://psivant.com/company/our-story/

Vision and Values: https://psivant.com/company/vision-and-values/

The Role

Reporting to the Senior Vice President, Drug Discovery, you will lead our computational chemistry and molecular design efforts. You will work closely with cross-functional teams to develop and implement computational strategies to support drug discovery projects ranging from target druggability assessment, hit finding, compound design/prioritization, and drug property optimization. You will act as a subject matter expert within Psivant and provide mentorship to a team of in silico modelers and computational method developers. We are seeking an accomplished computational drug hunter to lead a team of talented computational chemists and modelers, support their growth and development, and provide strategic and cutting-edge scientific guidance.

• Deliver on our drug discovery pipeline through direct project support and mentoring. Provide expertise and hands-on experience in molecular modeling techniques such as docking, molecular dynamics, free energy simulations, homology modeling, quantum mechanics, QSAR, machine learning, ligand- and structure-based design.
• Integrate and promote the use of the latest computational chemistry and cheminformatics methods and workflow to enhance research productivity and success.
• Collaborate with multidisciplinary teams and provide computational strategies to inform, influence, and prioritize designs for potency, selectivity, and improve ADME/Tox endpoints.
• Lead structure-based and computer-aided design for multiple internal projects and work with the medicinal chemistry team to prioritize compounds for synthesis.
• Stay up to date with emerging technologies, advocate for, and implement innovative computational methodologies, workflow and tools such as AI/ML, free energy simulations, and cheminformatics approaches to advance our therapeutic pipeline.
• Develop and mentor computational chemists and foster a culture of cross-functional collaborations.

• PhD degree in chemistry, biochemistry, biophysics, or equivalent with a computational emphasis.
• 10 years of drug discovery experience working at the interface of medicinal chemistry and computational sciences with a strong track record in advancing small molecule drug discovery projects through computation.
• Leadership experience with a passion for developing and/or mentoring computational chemists, preferably experience with direct people management.
• Expert level knowledge in structure-based drug design (SBDD), ligand conformations, protein structure and function, and protein-ligand interactions.
• Proficient in molecular modeling techniques such as docking, molecular dynamics, free energy simulations, homology modeling, quantum mechanics, QSAR / machine learning, ligand- and structure-based design.
• Skilled in the application of ML in drug discovery, including application to generative molecular design and property prediction.
• Strong experience with cheminformatics.
• Demonstrated experience across multiple commercial molecular modeling packages (e.g. Schrodinger, CCG, OpenEye, PyMOL, Molsoft) and cheminformatics packages (e.g. the RDKit) as well as experience in scientific scripting.
• Proficiency in scientific programming (e.g. Python, R) and pipelining tools, (e.g. Knime).
• Hands-on experience with running computational workflows in cloud environments.
• Excellent communication and interpersonal skills. Ability to work collaboratively and communicate effectively with scientists from diverse areas of expertise.
• Ability to build and foster strong, inclusive relationships in highly collaborative, cross-functional teams to effectively drive computational drug discovery and prioritize across multiple demands.