BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Talks.cam//talks.cam.ac.uk//
X-WR-CALNAME:Talks.cam
BEGIN:VEVENT
SUMMARY: Machine learning approaches to predicting protein-ligand binding 
 - Dr. Pedro J. Ballester\, MRC Methodology Research Fellow and EMBL-EBI
DTSTART:20130220T140000Z
DTEND:20130220T150000Z
UID:TALK43654@talks.cam.ac.uk
CONTACT:Danielle Stretch
DESCRIPTION:Predicting the binding affinities of large sets of diverse mol
 ecules against a range of macromolecular targets is an extremely challengi
 ng task. The scoring functions that attempt such computational prediction 
 are essential for exploiting and analysing the outputs of molecular dockin
 g\, which is in turn an important tool for structural biology\, chemical b
 iology and drug discovery. Traditional scoring functions assume a predeter
 mined theory-inspired functional form for the relationship between the var
 iables that characterise the X-ray crystal structure of the complex and it
 s binding affinity. The inherent problem of this approach is in the diffic
 ulty of explicitly modelling the various contributions of intermolecular i
 nteractions to binding affinity.\n\nNew scoring functions based on machine
  learning\, which are able to exploit effectively much larger amounts of e
 xperimental data and circumvent the need for a predetermined functional fo
 rm\, have recently been shown to outperform a broad range of state-of-the-
 art scoring functions. In this talk\, I will review work in this emerging 
 research area\, focusing on the different types of approaches and the pres
 ented studies to assess their performance against that of traditional scor
 ing functions.\n\n\nThe talk is part of the CCBI seminar series and the DT
 P graduate course Reviews in Computational Biology\, but is open to all at
 tendees.
LOCATION:MR4\, Centre for Mathematical Sciences\, Wilberforce Road\, Cambr
 idge
END:VEVENT
END:VCALENDAR