COMPUTATIONAL METHOD FOR DESIGNING CHEMICAL STRUCTURES HAVING COMMON FUNCTIONAL CHARACTERISTICS

摘要

1. A computer-based method of designing chemical structures having a preselected functional characteristic, comprising the steps of: (a) producing a physical model of a simulated receptor phenotype encoded in a linear charater sequence, and providing a set of target molecules sharing at least one quantifiable functional characteristic; (b) for each target molecule; (i) calculating an affinity between the receptor and the target molecule in each of a plurality of orientations using an effective affinity calculation; (ii) calculating a sum affinity by summing the calculated affinities; (iii) identifying a maximal affinity; (c) using the calculated sum and maximal affinities to: (i) calculate a maximal affinity correlation coefficient between the maximal affinities and the quantifiable functional characteristic; (ii) calculate a sum affinity correlation coefficient between the sum affinities and the quantifiable functional characteristic; (d) using the maximal correlation coefficient and sum correlation coefficient to calculate a fitness coefficient; (e) altering the structure of the receptor and repeating steps (b) through (d) until a population of receptors having a preselected fits coefficient are obtained; (f) providing a physical model of a chemical structure encoded in a molecular linear character sequence, calculating an affinity between the chemical structure and each receptor in a plurality of orientations using said effective affinity calculation, using the calculated affinities to calculate an affinity fitness score; (g) altering the chemical structure to produce a variant of the chemical structure and repeating step (f); and (h) retaining and further altering those variants of the chemical structure whose affinity score approaches a preselected affinity score. 2. The method according to claim 1 wherein the step of producing a simulated receptor genotype comprises generating a receptor linear character sequence which codes for spatial occupancy and charge, and wherein the step of producing a physical model of a chemical structure comprises generating said molecular linear character sequence which codes for spatial occupancy and charge. 3. The method according to claim 2 wherein said effective affinity calculation comprises two measures, the first being a proximity measure wherein the proportion of uncharged portions on said simulated receptors being sufficiently close to non-polar regions on said molecular structure to generate effective London dispersion forces is estimated, and the second being the summed strengths of charge-dipole electrostatic force interactions generated between charged portions of said simulated receptor and dipoles present in said molecular structure. 4. The method according to claim 2 wherein said step of calculating the affinity fitness score includes calculating a sum and maximal affinity between the molecular structure and each receptor, the fitness score being calculated as: Σ {(|calculated maximal affinity - target maximal affinity|/ target maximal affinity} and wherein said preselected fitness score is substantially zero. 5. The method according to claim 2 wherein said step of calculating the affinity fitness score includes calculating a sum and maximal affinity between the molecular structure and each receptor, the fitness score being calculated as: Σ {(|calculated maximal affinity-target maximal affinity|/ 2 x target maximal affinity) + (|calculated sum affinity target sum affinityl/2 x target sum affinity|)}, and wherein said preselected fitness score is substantially zero. 6. The method according to claim 2 wherein said sum affinity correlation coefficient is rSA2, said maximal affinity correlation coefficient is rMA2 , and wherein said fitness coefficient is F=(rMA2 x rSA2)0.5, and wherein said preselected fitness coefficient is substantially unity. 7. The method according to claim 2 wherein said sum affinity correlation coefficient is rSA-MA2, said maximal affinity correlation coefficient is rMA2, and wherein said fitness coefficient is F=(rMA2 x (1-rSA-MA2))0.5 and wherein said preselected fitness coefficient is substantially unity. 8. The method according to claim 2 wherein said molecular linear character sequences comprise a plurality of sequential character triplets, a first character of said triplet being randomly selected from a first character set specifying position and identity of an occupying atom in a molecular skeleton of said molecular structure, a second character of said triplet being randomly selected from a second character set specifying the identity of a substituent group attached to said occupying atom, and a third character of said triplet being randomly selected from a third character set specifying the location of said substituent on the atom specified by said first character of the triplet. 9. The method according to claim 8 wherein the molecular linear character sequence is decoded using an effective molecular assembly algorithm which sequentially translates each triplet from said molecular linear sequence and thereafter fills unfilled positions on said molecular skeleton with hydrogen atoms. 10. The method according to claim 9 wherein the step of mutating said molecular structure includes at least one of the following steps: i) mutating said molecular genotype by randomly interchanging at least one of said first, second and third characters of at least one triplet from the associated character sets, ii) deletion wherein a triplet from molecular genotype is deleted, iii) duplication wherein a triplet in the molecular genotype is duplicated, iv) inversion wherein the sequential order of one or more triplets in the molecular genotype is reversed, and v) insertion wherein a triplet from the molecular genotype is inserted at a different position in the molecular genotype. 11. The method according to claim 10 wherein the step of mutating said molecular genotypes includes recombining randomly selected pairs of said retained mutated molecular genotypes whereby corresponding characters in said molecular linear sequences are interchanged. 12. The method according to claim 2 wherein each character in the receptor linear character sequence specifies one of either a spatial turning instruction and a charged site with no turn. 13. The method according to claim 12 wherein said receptor phenotype comprises at least one linear polymer provided with a plurality of subunits, one of said subunits being a first subunit in said at least one linear polymer. 14. The method according to claim 13 wherein said receptor linear character sequence is decoded using an effective receptor assembly algorithm in which turning instructions applied to each subunit subsequent to said first subunit are made relative to an initial position of said first subunit. 15. The method according to claim 14 wherein said characters specifying spatial turning instructions code for no turn, right turn, left turn, up turn, down turn, and wherein characters specifying charge sites code for positively charged site with no turn, and negatively charged site with no turn. 16. The method according to claim 14 wherein said subunits are substantially spherical having a Van der Waals radii substantially equal to the Van der Waals radius of hydrogen. 17. The method according to claim 15 wherein the step of mutating said receptor genotype includes at least one of the following steps: i) deletion wherein a character from the receptor genotype is deleted, ii) duplication wherein a character in the receptor genotype is duplicated, iii) inversion wherein the sequential order of one or more characters in the receptor genotype is reversed, and iv) insertion wherein a character from the receptor genotype is inserted at a different position in the genotype. 18. The method according to claim 17 wherein the step of mutating said receptor genotypes includes recombining randomly selected pairs of said retained mutated receptor genotypes whereby corresponding characters in said receptor linear sequences are interchanged. 19. A method of screening chemical structures for preselected functional characteristics, comprising: a) producing a simulated receptor genotype by generating a receptor linear character sequence which codes for spatial occupancy and charge; b) decoding the genotype to produce a receptor phenotype, providing at least one target molecule exhibiting a selected functional characteristic, calculating an affinity between the receptor and each target molecule in a plurality of orientations using an effective affinity calculation, calculating a sum and maximal affinity between each target molecule and receptor, calculating a sum affinity correlation coefficient for sum affinity versus said functional characteristic of the target molecule and a maximal affinity correlation coefficient for maximal affinity versus said functional characteristic, and calculating a fitness coefficient dependent on said sum and maximal affinity correlation coefficients; c) mutating the receptor genotype and repeating step b) and retaining and mutating those receptors exhibiting increased fitness coefficients until a population of receptors with preselected fitness coefficients are obtained; thereafter d) calculating an affinity between a chemical structure being screened and each receptor in a plurality of orientations using said effective affinity calculation, calculating an affinity fitness score which includes calculating a sum and maximal affinity between the compound and each receptor and comparing at least one of said sum and maximal affinity to the sum and maximal affinities between said at least one target and said population of receptors whereby said comparison is indicative of the level of functional activity of said chemical structure relative to said at least one target molecule. 20. The method according to claim 19 wherein said effective affinity calculation comprises two measures,