Dear Colleague,

The growing protein structure information has given structure-based methods a place of prominence in drug discovery. The current state of in silico technology for effectively using biological big data for structure-based drug design is significantly limited by the lack of a realistic environment for simulations and an absence of fully integrated infrastructure. Moreover, the emerging deluge of biological data of all kinds available to the field of biomedicine and the pharmaceutical industry represents a significant opportunity to fundamentally change the process of disease target discovery and drug design.

Leveraging ever-increasing computing power and large-scale informatics framework that integrate functional, structural and expression information, we face ample opportunities to fine-tune our protein-ligand modeling algorithm and modify the molecule to get the desired properties.

In silico approaches to rational structure-based drug discovery are leveraging big data technology in the identification and optimization of lead compounds and the identification and validation of drug targets, followed by the prediction of ligands for the target protein from molecular modeling computation.

At CHI/Bio-IT World's thirteenth annual Structure-Based Drug Design conference, you will hear about developments in in silico technology, as well as experimental approaches useful for accurately predicting and modeling the structures of proteins in structure-based drug design efforts. In addition, examples of successful applications of such technology approaches to genome-to-drug lead investigations will be addressed.

Please make sure to take a moment to review the agenda and share our website with your clients and colleagues. Register by March 22 at the early discount rate and save up to $350 off your conference registration.

Warmest regards,

Edel O'Regan, Ph.D.
Vice President, Production
Cambridge Healthtech Institute

"Given both the overwhelming amount of available data and the fact that traditional pharma approaches to innovation seem to have largely run out of steam, a bet on big data analytics might make a lot of sense now."     - David Shaywitz, M.D., Ph.D., Physician, Scientist and Management Consultant

Who You Will Meet
Titles: 

• Lab Head/Head
• Team Leader
• Chief Scientific Officer
• Director
• Research Scientist/Principal Scientist/ Scientist
• Senior Researcher/Researcher
• Graduate Student
• Professor
• PostDoc

Departments: 

• Biochemistry
• Bioinformatics
• Chemical Biology
• Chemistry
• Computational Biology
• Computational Chemistry
• Discovery Chemistry
• Drug Discovery
• Informatics
• Lead Discovery
• Medicinal Chemistry
• Molecular Modeling
• Scientific Computing
• Stuctural Biology
• Structural Chemistry
• Structural Sciences