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SUMMARY:Cloud-mounted Molecular Experiments during COVID times - Dr David 
 Glowacki\, University of Bristol
DTSTART:20201021T133000Z
DTEND:20201021T143000Z
UID:TALK140626@talks.cam.ac.uk
CONTACT:Lisa Masters
DESCRIPTION:In this talk\, I will describe some recent attempts to carry o
 ut molecular science studies during COVID times. Specifically\, I will des
 cribe two projects: \n(1)	Crowd-sourced attempts to search the space of ML
  strategies and develop algorithms for predicting atomic-pairwise nuclear 
 magnetic resonance (NMR) properties in molecules. Using an open-source dat
 aset\, we worked with Kaggle to design and host a 3-month competition whic
 h received 47\,800 ML model predictions from 2\,700 teams in 84 countries.
  Within 3 weeks\, the Kaggle community produced models with comparable acc
 uracy to our best previously published ‘in-house’ efforts. A meta-ense
 mble model constructed as a linear combination of the top predictions has 
 a prediction accuracy which exceeds that of any individual model\, 7-19x b
 etter than our previous state-of-the-art. [1]\n(2)	Efforts to develop Naru
 pa\, [2] a flexible\, open-source\, cloud-mounted\, multi-person VR softwa
 re framework which enables groups of researchers distributed across the wo
 rld to simultaneously cohabit real-time simulation environments and intera
 ctively build\, inspect\, visualize\, and manipulate the dynamics of compl
 ex molecular structures with atomic-level precision. [3\,4] I will outline
  a range of application domains where we are using Narupa to obtain micros
 copic insight into 3D dynamical concepts and enable effective research and
  communication\, including protein-ligand binding\, [5] and machine learni
 ng potential energy surfaces. [6]\n\n1. L. A. Bratholm et al.\, “A commu
 nity powered search of machine learning strategy space to find NMR propert
 y prediction models\,” arxiv. 2008.05994\n2.  M. O'Connor et al.\, An op
 en-source multi-person virtual reality framework for interactive molecular
  dynamics: from quantum chemistry to drug binding\, J. Chem Phys 150\, 224
 703\, 2019.\n3.  M. O’Connor et al.\, Sampling molecular conformations a
 nd dynamics in a multiuser virtual reality framework. Science Advances\, 2
 018\, 4 (6).\n4. https://vimeo.com/420036282\n5. H. M. Deeks et al.\, “S
 ampling protein-ligand binding pathways to recover crystallographic bindin
 g poses using interactive molecular dynamics in virtual reality”\, arXiv
 :1908.07395\, 2019\n6.  S. Amabilino et al.\, Training neural nets to lear
 n reactive potential energy surfaces using interactive quantum chemistry i
 n virtual reality. J Phys Chem A\, 2019\, 123\, 20\, 4486\, 2019\n
LOCATION:Zoom Meeting ID: 986 1567 0048 Passcode: 266539
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