CAMD and GAs in Rational Drug Design Christopher Michael Lorenz BioChemistry 118Q June 4 2001 What is CAMD CAMD stands for Computer Aided Molecular Design The design of new molecules based on desired properties Focused on modeling drugs and biological receptors the drugs bind to so that better binding and more potent drugs can be developed Why use CAMD Avoids tedious lab work by using computers to model molecules and their properties Computers can develop new structures and determine whether those structures could serve a specific purpose much faster than humans Computers can imitate the millions of years of random variation and natural selection that specialized the molecular structures that gave rise to compounds such as morphine penicillin digitalis and tamoxifen Allows discoveries of a random almost accidental nature CAMD Success Development of an HIV protease inhibitor by Dupont Merck Developed completely on a computer by studying the molecular properties favorable to such an inhibitor and then designing a molecule to meet the necessary requirements HIV REV bound to RNA CAMD addresses two problems Forward the computation of macroscopic properties given the molecular structure Backward identification of the appropriate molecular structure given the desired properties How does CAMD address these problems Genetic Algorithms GAs What are genetic algorithms GAs are computer programs that apply optimization methods of evolution mutation crossover replication etc to generations of populations of computer code chromosomes Genetic Algorithms Genetic algorithms manipulate genetic material but instead of DNA this genetic material is some other linear string of symbols which can represent base pairs codons amino acids or molecular structures What happens Genetic operators crossovers mutations etc occur and fittest offspring pass on to next generation Crossover An Example In this example crossover occurs after position three of parent 1 and position two of parent 2 Mutation An Example The CH2 is replaced by a benzene ring Other Operators Blending Insertion Deletion Hopping How are the fittest offspring determined Population members are ranked by a fitness function function which could include parameters such as bond angles and energy values that reflect the structure s stability The fitness function can estimate and rank the docking abilities of ligands and receptors the poorest docking compounds are removed and the remainder are modified genetically and continue through the loop The members in the generation with the highest level of fitness become the optimal designs and will have a higher expected number of offspring Fitness Functions Difficulties Extremely complex Must build molecules and calculate properties Determine effects of placing molecule on the receptor Account for 3 D aspect of the molecule and its pathway to the receptor Genetic Algorithm Framework Much better than How Will You Make Money Protein Simulation Programs Predict structures based on sequences Predict how ligands will dock into protein structures
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