talk.bib

@misc{Ning2007,
  author = {\textbf{X. Ning} and Karypis, George},
  title = {Evaluation of 3D Descriptors in Virtual Screening},
  howpublished = {Oral presentation, ACS annual meeting},
  month = {Sept.},
  year = {2007},
  abstract = {In recent years there has been an increased interest in using structural
	descriptors in conjunction with advanced supervised learning algorithms
	(e.g., support vector machines and neural networks) for solving various
	problems arising in virtual screening. This research resulted in
	the development of highly effective activity and/or property prediction
	methods and has provided an objective and data-driven assessment
	of the characteristics that a descriptor set  should have in order
	to achieve good performance. Unfortunately, this research has primarily
	focused on topological descriptors and to a large extent has ignored
	the various 3D descriptors. In this talk we discuss our results in
	evaluating the various parameters of the design space for 3D descriptors
	and how they impact the machine learning based virtual screening
	approaches. Specifically, our work focuses on the questions like:
	What kinds of 3D elements of the compound structures are the most
	significant for bioactivity and how to efficiently extract them?
	How to quantitatively measure and represent these significant 3D
	elements in descriptors so as to optimally balance the trade-off
	between generality and specificity of structure representation? What
	is the best way of using the 3D descriptors in kernel-based machine
	learning approaches in order to take great advantage of boththe descriptors
	and the learning method? We address these questions by performing
	a comprehensive experimental evaluation using different 3D descriptors
	on a wide-range of datasets.},
  file = {papers/Ning2007.pdf},
  owner = {xia},
  timestamp = {2011.03.27}
}