Metastable Conformations in Computational Drug Design
by
Peter Deuflhard
Konrad-Zuse-Zentrum für Informationstechnik (ZIB) and Free University of Berlin, Dept. Mathematics and Computer Science

Computational drug design requires information about the dynamics of molecular systems. The classical model consists of Hamiltonian differential equations, whose initial value problems are ill-conditioned after psec time spans. Therefore, numerical long term integration will not supply the desired Computational dynamical information. The talk presents a rather recent approach called Perron cluster analysis developed by the author, Schütte, and co-workers. Its key idea is to directly identify almost invariant sets of the Hamiltonian system as metastable conformations together with their life spans and transition patterns. This leads to the numerical solution of a Perron cluster eigenvalue problem for some Markov operator. Discretization of that operator via hybrid Monte Carlo methods generates transition matrices for nearly uncoupled Markov chains. The discretization of the Markov operator requires careful consideration to avoid the curse of dimension: two variants have been developed, one based on temperature embedding via a Boltzmann distribution, the other via Kohonen self-organizing maps.

The approach as a whole aims at the substitution of experiments in chemical labs by realistic simulations in a Virtual Lab. Biomolecular examples will include an HIV protease inhibitor, which is a potential anti-AIDS drug, or epigallocatechine, a compound of green tea, suspected to be a possible drug against cancer.

Date received: April 10, 2001