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Large molecules section
THEOCH 5830
Journal of Molecular Structure (Theochem) 463 (1999) 1
Introduction
Large molecules section Chemistry is still predominantly an empirical science with experiments driving the development of new compounds, materials, and reactions. However, this approach becomes increasingly expensive and a more planned approach is asked for. Nothing illustrates the poor state of affairs better than the present situation in the development of a new drug. Presently, on average, 35 000 new compounds have to be synthesized to find a new drug. These compounds have to be screened for their activity, to find lead structures, that have to be further optimized to obtain compounds that can go into the clinical testing. All this takes a long time and involves high costs. On average, the development of a new drug takes 12 years and costs US$200 million. Theoretical Chemistry has taken up this challenge and, increasingly, rational drug design methods are explored in order to provide more guidance in the development of a new drug. Progress in hardware technology is accompanied with developments in software to allow the computation of increasingly larger systems. The combination of quantum mechanical (QM) approaches with molecular mechanics (MM) calculation is one answer to the problem of calculating large systems such as proteins. The lecture by Rivail and a number of the posters present attempts to find efficient combinations of QM and MM methods that lead to accurate results. In view of the importance of understanding the relationship between chemical structure and biological activity — particularly in the drug design process — many studies presented in the posters and in the full lectures deal with biochemical systems. However, not only theoretical computations — deductive methods — are used, but inductive approaches are also employed in an attempt to understand the relationships between structure and biological activity are employed. A great deal of progress has also been made by learning from data, by an inductive approach. In fact, chemists have
gained most of their insight and understanding of the complex systems they deal with — such as biological systems or chemical reactions — by analyzing a series of individual experiments and data. Inductive learning has a long history in chemistry and is probably still the most useful approach to increase our knowledge in chemistry. In recent years, powerful algorithms from statistics and chemometrics, as well as artificial neural networks, have been developed that allow a systematic analysis of data. This, combined with the storing of information in electronic form in databases, creates a platform for the novel application of computers for furthering our understanding of large and complex systems. Furthermore, combination of knowledge acquisition techniques with modeling and simulation methods allows exploration that can lead to the design of novel lead structures exhibiting biological activity. This will be outlined in the lecture on ‘de novo design’ by A.P. Johnson and in the lecture on structure-based drug design by G. Barnickel. Although most of the work being performed and presented here centers — due to its economic importance — around the drug design problem, it is increasingly felt that the understanding of chemical reactions presents an important problem which is equally challenging. Some work presented in the posters addresses the problems encountered in handling chemical reactions and thus builds a bridge to the section that explicitly deals with chemical reactivity. In summary, the challenges offered by large systems, in particular those faced in the drug design process, have been taken up by computational chemists and are addressed by deductive calculations and by inductive learning techniques, as well as with the use of simulations. Johann Gasteiger Computer-Chemie-Centrum, University of Erlangen-Nuernberg, Nuernberg, Germany
0166-1280/99/$ - see front matter 䉷 1999 Published by Elsevier Science B.V. All rights reserved. PII: S0166-128 0(98)00385-6
Journal of Molecular Structure (Theochem) 463 (1999) 1
Introduction
Large molecules section Chemistry is still predominantly an empirical science with experiments driving the development of new compounds, materials, and reactions. However, this approach becomes increasingly expensive and a more planned approach is asked for. Nothing illustrates the poor state of affairs better than the present situation in the development of a new drug. Presently, on average, 35 000 new compounds have to be synthesized to find a new drug. These compounds have to be screened for their activity, to find lead structures, that have to be further optimized to obtain compounds that can go into the clinical testing. All this takes a long time and involves high costs. On average, the development of a new drug takes 12 years and costs US$200 million. Theoretical Chemistry has taken up this challenge and, increasingly, rational drug design methods are explored in order to provide more guidance in the development of a new drug. Progress in hardware technology is accompanied with developments in software to allow the computation of increasingly larger systems. The combination of quantum mechanical (QM) approaches with molecular mechanics (MM) calculation is one answer to the problem of calculating large systems such as proteins. The lecture by Rivail and a number of the posters present attempts to find efficient combinations of QM and MM methods that lead to accurate results. In view of the importance of understanding the relationship between chemical structure and biological activity — particularly in the drug design process — many studies presented in the posters and in the full lectures deal with biochemical systems. However, not only theoretical computations — deductive methods — are used, but inductive approaches are also employed in an attempt to understand the relationships between structure and biological activity are employed. A great deal of progress has also been made by learning from data, by an inductive approach. In fact, chemists have
gained most of their insight and understanding of the complex systems they deal with — such as biological systems or chemical reactions — by analyzing a series of individual experiments and data. Inductive learning has a long history in chemistry and is probably still the most useful approach to increase our knowledge in chemistry. In recent years, powerful algorithms from statistics and chemometrics, as well as artificial neural networks, have been developed that allow a systematic analysis of data. This, combined with the storing of information in electronic form in databases, creates a platform for the novel application of computers for furthering our understanding of large and complex systems. Furthermore, combination of knowledge acquisition techniques with modeling and simulation methods allows exploration that can lead to the design of novel lead structures exhibiting biological activity. This will be outlined in the lecture on ‘de novo design’ by A.P. Johnson and in the lecture on structure-based drug design by G. Barnickel. Although most of the work being performed and presented here centers — due to its economic importance — around the drug design problem, it is increasingly felt that the understanding of chemical reactions presents an important problem which is equally challenging. Some work presented in the posters addresses the problems encountered in handling chemical reactions and thus builds a bridge to the section that explicitly deals with chemical reactivity. In summary, the challenges offered by large systems, in particular those faced in the drug design process, have been taken up by computational chemists and are addressed by deductive calculations and by inductive learning techniques, as well as with the use of simulations. Johann Gasteiger Computer-Chemie-Centrum, University of Erlangen-Nuernberg, Nuernberg, Germany
0166-1280/99/$ - see front matter 䉷 1999 Published by Elsevier Science B.V. All rights reserved. PII: S0166-128 0(98)00385-6