Marie Skłodowska-Curie Actions

ESR3 Prediction of chemical synthesis using NLP models

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    Helmholtz Zentrum Muenchen
    ChemistryComputational chemistry
    Computer scienceDigital systems
    First Stage Researcher (R1)
    11/12/2021 23:00 - Europe/Athens
    Germany › Neuherberg
    H2020 / Marie Skłodowska-Curie Actions


Advanced machine learning for Innovative Drug Discovery (AIDD) Project (http://ai-dd.eu):

Machine learning is changing our society, as exemplified by speech and image recognition applications. Also the life sciences change rapidly through the use of artificial intelligence, and it is expected that fields like drug development can take advantage of machine learning. The main goal of the AIDD project is to train and prepare the next generation of scientists who need to have skills in both machine learning and drug discovery and will, after graduating, be able to helping speeding up the drug development process. The European Marie Skłodowska-Curie Innovative Training Network funds the AIDD project that brings together twelve academic partners (Helmholtz Zentrum München (coordinator), Germany; Aalto University, Finland; Freie Universität Berlin, Germany; Katholieke Universiteit Leuven, Belgium; Johannes Kepler Universität Linz, Austria; The Swiss AI Lab IDSIA, Switzerland; TU Dortmund, Germany; Universiteit Leiden, Netherlands; Université du Luxembourg, Luxembourg; University of Vienna, Austria; Universitat Pompeu Fabra, Spain and Vancouver Prostate Center, University of British Columbia, Canada) as well as four industrial partners (AstraZeneca, Sweden; Bayer Aktiengesellschaft, Germany; Janssen Pharmaceutica NV , Belgium and Enamine Limited Liability Company, Ukraine).

The AIDD network offers 15 PhD fellowships. The employed fellows will be supervised by academics who have strong technical expertise and have contributed to some of the fundamental AI algorithms which are used billions of times each day in the world, and by machine learning scientists working at pharmaceutical companies. The developed methods by the fellows will contribute to an integrated "One Chemistry" model that can predict outcomes ranging from different properties to molecule generation and synthesis. The network will offer comprehensive, structured training through a well-elaborated Curriculum, online courses, and six schools.

Each fellow will perform research 1.5 years at an academic partner and 1.5 years at an industrial partner.

Description of the ESR3 position:

Chemical synthesis is critical to further increase life quality by contributing to new medicine and new materials. The optimal synthesis can decrease its costs as well as the amount of produced chemical waste. The prediction of the direct, i.e., which new chemical compound results by mixing a set of reactants, or retro-synthesis, which compounds are starting materials to make a given product, is the cornerstone of chemical synthesis. The ESR3 will develop a new method (based on the preliminary results [1,2]) to predict the outcome of reactions. The goal is to extend the published models by incorporating additional information about experiments (reagents, catalyst, solvent, temperature, etc.) and expert knowledge. The fellow will actively collaborate with ESR13 (QM models for reactivity prediction), ESR4 (prediction of the yield of chemical reactions), and ESR7 (multi-objective synthesis planning) and develop a solid theoretical foundation as well as practical intuition for how additional data and knowledge can improve the models.

Relevant references:

  1. Karpov P., Godin G., Tetko I.V.: A Transformer Model for Retrosynthesis. In: Artificial Neural Networks and Machine Learning – ICANN 2019: Workshop and Special Sessions: 17th - 19th September 2019 2019; Münich. Springer International Publishing: 817-830.
  2. Tetko I.V., Karpov P., Van Deursen R., Godin G.: State-of-the-art augmented NLP transformer models for direct and single-step retrosynthesis. Nat Comm 2020, 11(1):1-11.

More Information



Marie Skłodowska-Curie funding offers highly competitive and attractive salaries. Gross and net amounts are subject to country-specific deductions as well as individual factors such as family allowance.

Eligibility criteria

  • Early-Stage Researchers (ESRs) shall, at the time of recruitment by the host organization, be in the first four years(full-time equivalent research experience) of their research careers and have not been awarded a doctoral degree;
  • At the time of recruitment by the host organization, researchers must not have resided or carried out their main activity (work, studies, etc.) in the country of their host organization for more than 12 months in the 3 years immediately prior to the reference date. Compulsory national service and/or short stays such as holidays are not taken into account. As far as international European interest organizations or international organizations are concerned, this rule does not apply to the hosting of eligible researchers. However, the appointed researcher must not have spent more than 12 months in the 3 years immediately prior to their recruitment at the host organization.

Selection process

  • Each application will be screened by the respective supervisors from the host organizations
  • Prospective candidates will be contacted by the supervisors for individual interviews and the best ones will be shortlisted
  • The shortlisted candidates will be interviewed by the recruitment commission either in person or by SKYPE/Zoom
  • The candidates will be informed by e-mail about the results of their applications

Additional comments

More info https:/ai-dd.eu/esr-positions

Offer Requirements

    Chemistry: Master Degree or equivalent
    Biological sciences: Master Degree or equivalent
    Computer science: Master Degree or equivalent
    Physics: Master Degree or equivalent
    Engineering: Master Degree or equivalent
    ENGLISH: Excellent


  1. Master's degree in computer science, physics, chemistry, biology, or engineering with and sincere interest in biology and the life sciences;
  2. prior expertise in one or more of the following fields: machine learning, modeling and simulation;
  3. be excellent in oral and written English with good presentation skills;
  4. possess strong interpersonal skills, excellent written and verbal communication, and the ability to work effectively both independently and in cross-functional teams;
  5. be a highly creative person with outstanding problem-solving ability and the willingness to undertake challenging analysis tasks in a timely fashion.

Specific Requirements

  1. Excellent software engineering skills are essential. Programming skills in Python must be top-notch;
  2. experience with relevant libraries (TensorFlow/PyTorch, the python scientific stack) is necessary;
  3. good command of modern software development tools, from git to continuous integration pipelines, is an additional plus.

Map Information

Job Work Location Personal Assistance locations
Work location(s)
1 position(s) available at
Helmholtz Zentrum Muenchen
Ingolstaedter Landstrasse 1

EURAXESS offer ID: 660545


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