XXV General Assembly and Congress of the
International Union of Crystallography - IUCr 2021
August 14 - 22, 2021 | Prague, Czech Republic
Conference Agenda
Overview and details of the sessions of this conference. Please select a date or location to show only sessions at that day or location. Please select a single session for detailed view (with abstracts and downloads if available).
Please note that all times are shown in the time zone of the conference. The current conference time is: 19th Apr 2024, 05:40:22am CEST
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Session Overview |
| Session | ||
MS-73: Machine learning in biological and structural sciences
Invited: Melanie Vollmar (UK), Sergei Grudinin (France) | ||
| Session Abstract | ||
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Recently Machine Learning (ML), has become very popular in the fields of structural biology and chemical crystallography, throughout the pipeline from data collection and data processing through to structure solution and refinement. This technique can improve crystal structure prediction and classification, while ML and its tools (for example deep learning) have also been applied, inter alia, to drug discovery, powder diffraction and materials science. Experts in the field will discuss the background and recent advances in ML as applied to structural science. For all abstracts of the session as prepared for Acta Crystallographica see PDF in Introduction, or individual abstracts below. | ||
| Introduction | ||
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10:20am - 10:25am
ID: 1804 / MS-73: 1 Introduction Oral/poster Introduction to session 10:25am - 10:55am
ID: 938 / MS-73: 2 Biological and macromolecular crystallography Invited lecture to session MS: Machine learning in biological and structural sciences Keywords: experimental phasing, machine learning, data triaging Predicting experimental phasing success for data triaging 1Diamond Light Source, Didcot, United Kingdom; 2Rosalind Franklin Institute, Didcot, United Kingdom External Resource:
 10:55am - 11:25am
ID: 1173 / MS-73: 3 All topics Invited lecture to session MS: Machine learning in biological and structural sciences Keywords: deep learning, protein structure prediction, geometric learning, end-to-end architectures, protein language models Deep learning entering the post-protein structure prediction era : new horizons for structural biology Univ. Grenoble Alpes, CNRS, Grenoble INP, LJK, 38000 Grenoble, France External Resource:
11:25am - 11:45am
ID: 1196 / MS-73: 4 Biological and macromolecular crystallography Oral/poster MS: Machine learning in biological and structural sciences, Automation in bio-crystallography: tools, perspectives and applications Keywords: machine learning, quality indicators, data processing, ice rings, X-ray data How machine learning can supplement traditional quality indicators - and the human eye: A case study 1Universität Hamburg, Germany; 2Julius-Maximilians-Universität Würzburg, Germany External Resource:
11:45am - 12:05pm
ID: 965 / MS-73: 5 Biological and macromolecular crystallography Oral/poster MS: Machine learning in biological and structural sciences Keywords: crystal structure prediction, machine learning, Gaussian process regression Learning structure-energy relationships for the prediction of molecular crystal structures University of Southampton, Southampton, United Kingdom External Resource:
12:05pm - 12:25pm
ID: 869 / MS-73: 6 All topics Oral/poster MS: Machine learning in biological and structural sciences, Data-driven discovery in crystallography Keywords: machine learning, crystal descriptors, physical property prediction, RDF, radial distribution function New generalized crystallographic descriptors for structural machine learning University of Edinburgh, Edinburgh, United Kingdom External Resource:
12:25pm - 12:45pm
ID: 341 / MS-73: 7 Bursary application Oral/poster MS: Machine learning in biological and structural sciences, XAS and crystallography allied for geomaterials and environmental problems, Disordered materials: spectroscopic and scattering techniques Keywords: XANES, pre-edge, Wannier function, MLFT, machine learning Analysis of pre-edge XANES spectra of Fe:SiO4 system by using machine learning methods. Southern Federal University, Rostov-on-Don, Russian Federation Bibliography
1. E. Gorelov, A.A. Guda, M.A. Soldatov, S.A. Guda, D. Pashkov, A. Tanaka, S. Lafuerza, C. Lamberti, A.V. Soldatov, MLFT approach with p-d hybridization for ab initio simulations of the pre-edge XANES, Radiation Physics and Chemistry, 2018, DOI: 10.1016/j.radphyschem.2018.12.025. 2. A. Martini, S. A. Guda, A. A. Guda, G. Smolentsev, A. S. Algasov, O. A. Usoltsev, M. A. Soldatov, A. L. Bugaev, Y. V. Rusalev, A. V. Soldatov, PyFitit: the software for quantitative analysis of XANES spectra using machine learning algorithms, Computer Physics Communications, 2019 3. A. A. Guda, S. A. Guda, K. A. Lomachenko, M. A. Soldatov, I. A. Pankin, A. V. Soldatov, L. Braglia, A. L.Bugaev, A. Martini, M. Signorile, E. Groppo, A. Piovano, E. Borfecchia, C. Lamberti, Quantitative structural determination of active sites from in situ and operando XANES spectra: From standard ab initio simulations to chemometric and machine learning approaches, Catalysis Today, V. 336, 2019, P. 3-21, DOI: 10.1016/j.cattod.2018.10.071. 4. Guda, A.A., Guda, S.A., Martini, A., Bugaev A., Soldatov, M. A., Soldatov, A. V. & Lamberti, C. (2019). Machine learning approaches to XANES spectra for quantitative 3D structural determination: The case of CO2 adsorption on CPO-27-Ni MOF. Radiation Physics and Chemistry. 108430. DOI: 10.1016/j.radphyschem.2019.108430. External Resource:
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