Learning and Charting Chemical Space with Strings and Graphs: Challenges and Opportunities for AI and Machine Learning
published: Aug. 27, 2007, recorded: August 2007, views: 5928
Report a problem or upload filesIf you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
Informatics methods and computers have not yet become as pervasive in chemistry as they have in physics and biology. Drawing analogies from bioinformatics, key ingredients for progress in chemoinformatics are the availability of large, annotated databases of compounds and reactions, data structures and algorithms to efficiently search these databases, and computational methods to predict the physical, chemical, and biological properties of new compounds and reactions. We will describe how graph-based methods play a key role in the development of: (1) a large public database of compounds and reactions (ChemDB) and the underlying algorithms and representations; (2) machine learning kernel methods to predict molecular properties; and (3) the applications of these methods to drug screening/design problems and the identification of new drug leads against a major disease.
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !