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.
SITX was founded out of the BIDMC in late 2016 and has raised ~50mm USD to date from leading investors including Sequoia & Chengwei Capital. The Company’s investors are long-term, premier brands that have the capability to hold businesses for decades and are believers in SITX’s mission and physics-driven approach.
Silicon Therapeutics is seeking a highly motivated Principal Investigator, Machine Learning who wants to work in a multi-disciplinary organization with the aim of treating challenging diseases.
Work closely with our scientists to design, develop, and deploy simulation methods for our drug discovery efforts
Conceptualize, implement, and maintain software engineering best practices
Ensure scalability, extensibility, and portability of platform code
Create, integrate, and maintain databases for storage and access of results
Investigate latest trends and scientific advancements in data analytics and high performance computing and propose adoption where appropriate
Apply machine learning techniques to real-world drug discovery problems
Develop machine learning 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
Required Skills & Qualifications
Outstanding development skills with demonstrated track record of overcoming challenging problems
Experience building large scale software platforms
Experience developing and applying machine learning methods to scientific problems
Experience working with a multidisciplinary team of software engineers and scientists
Excellent C/C++ development and debugging skills
Strong knowledge of Python, Git, and Jenkins (or related tools)
Strong knowledge of data analysis methods
MS and >12 years of related experience or PhD, or higher degree in computer science, machine learning, or related field and > 8 years of related experience (including post-doc).
Additional Desirable Qualifications
Ability to mentor other software engineers and manage outsourced software development projects
Experience with parallel programming, especially data-parallel, using MPI, OpenMP, OpenACC or CUDA C/C++/Fortran
Experience working with containers
Ability to manage HPC clusters (CPU and GPU based)
Experience in using/deploying workflow frameworks such as Airflow, CWL, etc.
Experience with scientific computing systems and schedulers