Connectionists: PhD position: Machine learning in reaction informatics

[Igor Tetko]

PhD position: Machine learning in chemical reaction informatics

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 fellow will develop new method(s) (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 based on NLP approaches.

Requirements: knowledge in NLP and deep learning methods, Python frameworks (PyTorch, Tensorflow, etc.); a knowledge of chemistry is desirable but not crucial
Eligibility: see detailed rules at https://ai-dd.eu/esr-positions (briefly: not more than 4 years after MSc, MSc from recognised University)

Relevant references:
• 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.
• 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.


About: This position is announced within the Advanced machine learning for Innovative Drug Discovery (AIDD) network (http://ai-dd.eu), that is European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 956832.  Multi-modal learning is a hot topic in AI research around the globe. With this project, you will work on applying this innovative approach for chemistry and drug discovery. The project is run in collaboration with 15 other PhD students, multiple academic groups and industry partners, creating great opportunities for networking and collaboration on cool science projects. This position will be located at Helmholtz Zentrum in vibrant München and at Janssen Pharmaceutica in Belgium. 
	The fellow will collaborate with several other positions such as QM models for reactivity prediction based on machine learning, academic PI Alexandre Tkatchenko; Prediction of outcome of chemical reactions using new neural network architectures, academic PI Jürgen Schmidhuber and others. 

See application details at https://ai-dd.eu/esr-positions

See also LinkedIn announcement at https://www.linkedin.com/posts/dorota-herman-ab433033_phd-positions-activity-6866004030494150656-mW69

Dr. Igor V. Tetko
Institute of Structural Biology
Helmholtz Zentrum Muenchen (GmbH)
German Research Center for Environmental Health
Ingolstaedter Landstrasse 1,
D-85764 Neuherberg, Germany
 
AIDD: http://ai-dd.eu (coordinator)
OCHEM http://ochem.eu



Helmholtz Zentrum Muenchen

Deutsches Forschungszentrum fuer Gesundheit und Umwelt (GmbH)

Ingolstaedter Landstr. 1

85764 Neuherberg

www.helmholtz-muenchen.de

Aufsichtsratsvorsitzende: MinDir.in Prof. Dr. Veronika von Messling

Geschaeftsfuehrung: Prof. Dr. med. Dr. h.c. Matthias Tschoep, Kerstin Guenther

Registergericht: Amtsgericht Muenchen HRB 6466

USt-IdNr: DE 129521671

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