ORGANISATION/COMPANYHelmholtz Zentrum Muenchen
RESEARCH FIELDChemistry › Computational chemistryComputer science › Digital systems
RESEARCHER PROFILEFirst Stage Researcher (R1)
APPLICATION DEADLINE12/09/2021 23:00 - Europe/Brussels
LOCATIONGermany › Neuherberg
TYPE OF CONTRACTTemporary
HOURS PER WEEK39
OFFER STARTING DATE01/11/2021
EU RESEARCH FRAMEWORK PROGRAMMEH2020 / Marie Skłodowska-Curie Actions
MARIE CURIE GRANT AGREEMENT NUMBER956832
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 consists of 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 ESR1 position:
This is a great opportunity to potentially shape the future of drug discovery by working on and designing innovative edge deep learning architectures. Recent years have seen an explosion in the interest of deep neural networks for drug discovery applications. This project aims to take the architecture to the next level by creating an interpretable "One-Chemistry" model, which unifies multiple predictions of physicochemical and ADME properties and incorporates tasks from the other Ph.D. projects within the AIDD. To support choices in drug discovery projects by understanding the reasoning of the model, the interpretability of the model is crucial. Statistical modeling in drug design projects reveals correlations between chemical compounds' features and their physicochemical and biological endpoints. Though the final models possess excellent statistical characteristics, the reasoning of the models behind their predictions is only limited by the training dataset. Combining different expert modules with specific internal knowledge with subsequent simultaneous retraining on a particular problem can support the model's reasoning by linking together knowledge about designing new compounds, their properties, biological responses, and synthetic accessibility. The "One-Chemistry" model will thus explain why it has picked up a particular drug candidate relating to different areas of chemistry and biology. The entry point will be data interpretation based on Layer-wise Relevance Propagation2 approach implemented within the Transformer CNN network3 which demonstrated an excellent performance thanks to transfer learning and data augmentation techniques.4 The successful Ph.D. student will implement the complex system for AI drug discovery, with pluggable modules, to design new chemical compounds. The overall method and software will be experimentally validated in collaboration with a Ph.D. research project aiming at finding new effective drugs against prostate cancer.
(1) Tetko, I. V.; Engkvist, O. From Big Data to Artificial Intelligence: Chemoinformatics Meets New Challenges. J. Cheminformatics 2020, 12 (1), 74. https://doi.org/10.1186/s13321-020-00475-y.
(2) Lapuschkin, S.; Wäldchen, S.; Binder, A.; Montavon, G.; Samek, W.; Müller, K.-R. Unmasking Clever Hans Predictors and Assessing What Machines Really Learn. Nat. Commun. 2019, 10 (1), 1096. https://doi.org/10.1038/s41467-019-08987-4.
(3) Karpov, P.; Godin, G.; Tetko, I. V. Transformer-CNN: Swiss Knife for QSAR Modeling and Interpretation. J. Cheminformatics 2020, 12 (1), 17. https://doi.org/10.1186/s13321-020-00423-w.
(4) Bjerrum, E. J. SMILES Enumeration as Data Augmentation for Neural Network Modeling of Molecules. ArXiv170307076 Cs 2017.
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.
- 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.
- 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
More info https:/ai-dd.eu/esr-positions
REQUIRED EDUCATION LEVELChemistry: Master Degree or equivalentBiological sciences: Master Degree or equivalentComputer science: Master Degree or equivalentPhysics: Master Degree or equivalentEngineering: Master Degree or equivalent
REQUIRED LANGUAGESENGLISH: Excellent
- Master's degree in computer science, physics, chemistry, biology, or engineering with and sincere interest in biology and the life sciences;
- prior expertise in one or more of the following fields: machine learning, modeling and simulation;
- be excellent in oral and written English with good presentation skills;
- possess strong interpersonal skills, excellent written and verbal communication, and the ability to work effectively both independently and in cross-functional teams;
- be a highly creative person with outstanding problem-solving ability and the willingness to undertake challenging analysis tasks in a timely fashion.
- Excellent software engineering skills are essential. Programming skills in Python must be top-notch;
- experience with relevant libraries (TensorFlow/PyTorch, the python scientific stack) is necessary;
- good command of modern software development tools, from git to continuous integration pipelines, is an additional plus.
EURAXESS offer ID: 675339
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