Johnson & Johnson Careers
Scientist generative ML/AI for chemical synthesis
Requisition ID: 1905760691W
Janssen Research & Development seeks to drive innovation and deliver transformational medicines for the treatment of diseases in six therapeutic areas: neuroscience, cardiovascular diseases and metabolism, infectious diseases, immunology, oncology and pulmonary hypertension. In these areas, Janssen aims to address and solve unmet medical needs through the development of small and large molecules, as well as vaccines. The Janssen campus in Beerse (Belgium) has a unique ecosystem covering the complete drug development life cycle, with all capabilities from basic science to market access on one campus. The integrated environment of our campus gives our people the chance to experience many different aspects of drug development throughout their career. It has a successful track record of over sixty years of drug discovery and development and is one of the most important innovation engines of the Janssen group worldwide.
Developing innovative therapeutics to treat diseases like Alzheimer’s disease, various types of cancers and infectious diseases like Hepatitis B, influenza is our passion. In this endeavor, we are seeking to recruit new talent for the comprehensive analyses of high-dimensional datasets using state-of-the-art data science methods applied to drug discovery programs. These two scientist positions will be opened on the Beerse campus, which is the flagship R&D center for small molecules within Janssen, investing over 1 billion euros each year in R&D.
These positions frame in the ongoing virtualization of parts of the research process. The successful candidate will join a team of multiple PhD level data scientists who introduce large scale machine learning and artificial intelligence to leverage the extensive datasets accessible to the company. Accessible datasets include chemical descriptions and annotations of desired and undesired biological activities for millions of small molecules across thousands of assays, and information-rich but hard-to-interpret documentation of small molecules like microscopy images or transcriptomics profiles acquired at high throughput (hundreds of thousands of profiles in each of several assays). In addition, dozens of millions of chemical reactions can be mined. The goal is the selection and design of small molecules to make and test en route to better and safer drugs for patients with unmet medical needs.
The Scientist will support chemical synthesis design and small library creation based on chemical synthesis ML/AI, which might support flow chemistry, parallel chemistry, and small molecular library design.
This includes generative chemistry via 1. forward-synthesis based on existing chemistry plans, 2. retrosynthesis analytics in combination with generative deep-learning to translate novel chemical ideas fullfilling more generic and specific targetp-product profiles (gTPP and sTPP) into actionable chemistry routes to be executed; and both in combination with 3. 3D structural constraints for ensuring a structure-based driven exploration in combination with data-driven exploitation to maximize the success of sensible chemical ideas resulting in a learning/exploration of novel SAR as well as an exploitation of existing SAR within or across chemical series.
The successful candidates will be responsible for:
• coordinating the design, development, internalization and combination of state-of-the-art machine learning methods to select and design small molecules to make and or test
• encourage the formulation new and creative ways of unlocking information from accessible data sources and theoretical concepts
• ensure the application of the resulting capabilities to support portfolio projects
• emphasize the translation of questions of biologists and chemists to a quantitative analysis formulation
• interaction with R&D informatics to make proof-of-concept solutions robustly accessible to users, with visualization solutions
• interaction and coordination with counterparts from computational chemistry, drug metabolism and pharmacodynamics and toxicology to account for their insights and needs
• incentivize team contributions to peer-reviewed papers and presentations at relevant conferences
• PhD in machine learning with Master level training in organic chemistry, biochemistry, cell biology or pharmacology, or PhD in organic chemistry, biochemistry, cell biology or pharmacology with Master level training in machine learning or equivalent interdisciplinary training in related quantitative fields, who has had direct exposure to and interaction with collaborating chemists, biologists, pharmacologists or toxicologists.
• preferably experience with working in a matrix teams (ideally in an industrial or semi-industrial setting)
• excellent communication, reporting, planning and team interaction skills, self-motivation, proactivity and the ability to work independently
• ability to build bridges between colleagues, disciplines, departments and collaborators
• advanced hands-on programming and scripting skills that enable the development of functional prototypes
• experience with advanced machine learning frameworks, like PyTorch, Keras, Tensorflow
Janssen Pharmaceutica N.V. (7555)