• Full Time
  • Boston, MA

Company

Silicon Therapeutics (“SITX”) is a privately-held, physics-driven integrated drug discovery company in the preclinical IND stage targeting innate immunity to start, but with the capability to move into many other franchise opportunities. The Company’s initial pipeline is focused on modulation of the innate immune system to “light the spark” within immunologically cold tumors by using its physics-enabled drug discovery engine for the design of small molecule therapeutics and the lead program is in late stage preclinical studies. SITX’s discovery efforts as well as physics-based platform are all completely in-house, with a full wet lab as well as a team of over 25 experienced R&D professionals spanning biology, biochemistry, chemistry, biophysics (NMR + X-ray) & preclinical sciences. SITX’s engine leverages quantum mechanics and molecular dynamics, which are deployed on its own internal super computer composed of over 400 GPU’s and FPGA’s and allows SITX to accurately simulate the physical motion and properties of biological targets at an atomistic level resolution. The platform is completely proprietary to SITX and was built from the ground up with the purpose of addressing difficult targets and using simulations to break through bottlenecks that has plagued traditional drug discovery approaches on such targets. It is currently the ONLY company which owns the entire spectrum of physics-driven drug discovery from chip-to-clinic with a team of over 40 individuals in Boston.

The Company’s initial focus in on innate immunity and its pipeline is currently composed of one project entering into IND-enabling studies, another one entering hit-to-lead and two more ramping up. It’s unique advantage in designing small molecules using all atom simulations has allowed it to advance a systemically available small molecule STING agonist into a preclinical development. The program is ramping up for IND enabling studies entry to the clinic in 2020. This differentiated product can be administered systemically via an IV dose and has single agent effects in early mouse efficacy studies using PD1 resistant tumor models and is a “rule-of-5” compliant small molecule. Beyond this one program, the Company is also advancing an ADAR inhibitor which can be considered an innate immune check-point as well as a multitude of targets that explore Innate Immunity’s role in cancer biology.

Job Summary

  • Work with a multi-disciplinary team (e.g. chemists, biologists, biophysicists) to discover novel medicines for challenging disease targets
  • Apply machine learning techniques to real-world drug discovery problems
  • Develop methods to improve the robustness of predictions from molecular dynamics simulations and quantify uncertainties associated with free energy simulations
  • Build state of the art machine learning infrastructure and workflows for molecular design
  • Work closely with bench scientists to understand critical project bottlenecks and how machine learning can help

Qualifications

  • BS, MS, or Ph.D. in computer science, statistics, mathematics, physics, engineering, or equivalent practical experience
  • Minimum of 3 years machine learning experience
  • Demonstrated track record in team-based projects
  • Strong communication skills
  • Ability to conduct independent research
  • Experience in one or more modern general purpose programming languages (e.g. C++, Python, Scala)
  • Experience building production machine learning systems (e.g. readable and reproducible code with appropriate unit tests and modern software design principles)

 

Additional Desirable Qualifications

  • Experience with at least one modern Deep Neural Network package (e.g. Tensorflow, Theano, Keras, Pytorch)
  • Experience with Bayesian optimization or other black-box techniques
  • Experience working with biological data sets (e.g. DNA, RNASeq, Mass Spec.)
  • Experience working with version control
  • Desire to publish and present your work at ML conferences and in prominent journals
  • Authorized to work in the United States
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