Manuel C. Peitsch

Computational structural biology : methods and applications

Structure Prediction Methods: Protein Fold Recognition and Threading (L McGuffin) Assessment of Protein Structure Predictions (E Capriotti & M A Marti-Renom) From Structure to Function to Design: Evolution of Protein Folds (A Lupas & K Koretke) Molecular Modeling of Enzyme Reactions and Transition States (M Meuwly) Virtual Screening and Docking: MD-based Free Energy Simulations (O Michielin) Protein-Protein Interactions and Aggregation Processes (R Dima) New Frontiers in X-ray Crystallography (M Grutter) New Frontiers in Electron Microscopy (A Engel) Other Selected Topics: Docking for Neglected Diseases as Community Effort (M Provinec et al.) Protein Structure Databases (K Henrick et al.) Molecular Graphics (A M Lesk) and other chapters.

Competitive intelligence and patent analysis in drug discovery: Mining the competitive knowledge bases and patents

Patents are a major source of information in drug discovery and, when properly processed and analyzed, can yield a wealth of information on competitors activities, R&D trends, emerging fields, collaborations, among others. This review discusses the current state-of-the-art in textual data analysis and exploration methods as applied to patent analysis.:

Genome size and numbers of biological functions

Calculations of potential numbers of interactions between gene products to generate physiological functions show that we can expect a highly non-linear relation between genome size and functional complexity. Moreover, very small differences in gene numbers or sequence can translate into very large differences in functionality.

Human aspects of the management of drug discovery knowledge

A well-defined strategy for knowledge management is a key success factor of any knowledge-intensive industry. This applies particularly well to pharmaceutical drug discovery, which is one of the most knowledge-intensive processes. The subject has only rarely been studied in the context of pharmaceutical firms and we can only extrapolate a limited number of findings from other industries. Here, we look at five key human aspects of knowledge management (social networks and communities of practice, the roles of professional knowledge managers, the behaviors and processes of knowledge workers, management strategies and tactics and the role of the external work environment) and how they apply to the drug discovery process.:

Manuel Peitsch discusses knowledge management and informatics in drug discovery

Abstract Manuel Peitsch is Global Head of Informatics and Knowledge Management at the Novartis Institutes for BioMedical Research, a position he has held since he joined Novartis in 2001. He is a world-renowned leader in bioinformatics, with more than 90 publications and several patents and awards. His pioneering research impacted bioinformatics by introducing high-throughput automated protein modeling and web-based bioinformatics. Manuels findings and developments in this area have had important implications for the use of protein structures in biology. Most of his career has been focused on cell death research (complement, T cells and apoptosis), bioinformatics and scientific computing in life sciences. Prior to Novartis, Manuel held positions at the National Cancer Institute FCRF in Frederick, MA, and at the Institute of Biochemistry at the University of Lausanne. In 1994, he was head of Bioinformatics at GlaxoWellcomes Geneva Biomedical Research Institute. In 1997 and 1998 he co-founded three bioinformatics organizations: Geneva Bioinformatics (GeneBio), the Swiss Institute of Bioinformatics and GlaxoWellcome Experimental Research, where he was Director for Scientific Computing. In 2000 he headed Informatics & Knowledge Management at GlaxoSmithKline Research & Development. Since 2002, Manuel has been Professor for Bioinformatics at the University of Basel. He is also a member of the Foundation Council of the Swiss Institute of Bioinformatics.

The SwissBioGrid Project: Objectivse, Preliminary Results and Lessons Learned

Modern biology has become a science of information, analysis and prediction, coalescing into computational biology -- a single discipline at the crossroads of life sciences, informatics, and mathematics. New developments in information and communications technology as well as high-performance computing enable researchers to address new demanding scientific problems which seemed far out of reach only a few years ago. The computational requirements of most applications in computational biology differ significantly from the requirements of other users of highthroughput computing such as high energy physics. To address these needs, the SwissBioGrid initiative, a collaboration among several partner institutions with a broad spectrum of expertise, was started over a year ago. In this paper, we report on its current status and achievements as well as the lessons learned which are of interest to the wider e-Science and Grid communities.

Computer-assisted reading in drug discovery

We are witnessing an exponential increase in the available publications, patents and textual documents that can no longer be assimilated by individual scientists. The complexity of the information landscape is further enhanced by the constantly growing number and diversity of databases and web-based information sources. Therefore, much scientific information might go unnoticed or untapped by a large portion of the scientific community. Consequently, scientists crucially need new methods and tools to find information and navigate the ever-evolving world of information. In this perspective, the author proposes that an integrated approach of text mining, advanced computing and modern library sciences is the key to developing new paradigms in computer-assisted reading and will be the key enablers of tomorrow’s science.

Grid Computing in Drug Discovery

Drug Discovery is aimed at finding novel approaches to unmet medical needs. This requires identifying and validating biological pathways and their associated molecular targets, discovering and optimizing chemical structures and running Proof of Concept trials in humans. Each step along this process is aimed at selecting a limited number of scientifically sound options from the large pool of known genes and available chemical diversity. This complex process relies on experimental approaches which yield large amounts of data, leading to major challenges in data analysis and interpretation. In this context, it is not surprising that in silico methods are being developed with the aim to accelerate and optimize the Drug Discovery process. These methods range from data mining, modeling and simulation of molecular interactions, biological networks and processes and the large scale computer-aided analysis of scientific literature and patents. The demands for such approaches will increase dramatically in the years to come, providing Drug Discovery with new ways to associate pathways and targets with diseases and select candidate drugs. This presentation will outline how in silico approaches and High Performance Computing can impact Drug Discovery through specific examples.