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: 26th Nov 2022, 02:17:18pm CET

 
 
Session Overview
Session
MS-73: Machine learning in biological and structural sciences
Time:
Friday, 20/Aug/2021:
10:20am - 12:45pm

Session Chair: Rita Giordano
Session Chair: Harold Roger Powell
Location: Terrace 2B

100 2nd floor

Invited: Melanie Vollmar (UK), Sergei Grudinin (France)


Session Abstract

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.


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Presentations
10:20am - 10:25am

Introduction to session

Rita Giordano, Harold Roger Powell



10:25am - 10:55am

Predicting experimental phasing success for data triaging

Melanie Vollmar1, Irakli Sikharulidze1, Gwyndaf Evans1,2

1Diamond Light Source, Didcot, United Kingdom; 2Rosalind Franklin Institute, Didcot, United Kingdom



10:55am - 11:25am

Deep learning entering the post-protein structure prediction era : new horizons for structural biology

Sergei Grudinin

Univ. Grenoble Alpes, CNRS, Grenoble INP, LJK, 38000 Grenoble, France



11:25am - 11:45am

How machine learning can supplement traditional quality indicators - and the human eye: A case study

Andrea Thorn1, Kristopher Nolte1, Yunyun Gao1, Sabrina Stäb1, Philip Kollmannsberger2

1Universität Hamburg, Germany; 2Julius-Maximilians-Universität Würzburg, Germany



11:45am - 12:05pm

Learning structure-energy relationships for the prediction of molecular crystal structures

Graeme M Day

University of Southampton, Southampton, United Kingdom



12:05pm - 12:25pm

New generalized crystallographic descriptors for structural machine learning

James Cumby, Sohan Seth, Ruizhi Zhang

University of Edinburgh, Edinburgh, United Kingdom



12:25pm - 12:45pm

Analysis of pre-edge XANES spectra of Fe:SiO4 system by using machine learning methods.

Danil Pashkov, Alexander Guda, Sergey Guda, Alexander Soldatov

Southern Federal University, Rostov-on-Don, Russian Federation



 
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