Design of molecules

Abstract

A method for computational drug design using an evolutionary algorithm, comprises evaluating virtual molecules according to vector distance (VD) to at least one achievement objective that defines a desired ideal molecule. In one method the invention comprises defining a set of n achievement objectives (O A 1-n ), where n is at least one; defining a population (P G=0 ) of at least one molecule; selecting an initial population (P parent ) of at least one molecule (I 1 −I n ) from the population (P G=0 ); and evaluating members (I 1 −I n ) of the initial population (P parent ) against at least one of the n achievement objectives (O A 1-x ), where x is from 1 to n.

1 . A method for computational drug design using an evolutionary algorithm, comprising:
 defining a set of n achievement objectives (O A   1-n ), where n is at least one;   defining a population (P G=0 ) of at least one molecule;   selecting an initial population (P parent ) of at least one molecule (I 1 −I n )) from the population (P G=0 ); and   evaluating members (I 1 −I n ) of the initial population (P parent ) against at least one of the n achievement objectives (O A   1-x ), where x is from 1 to n;   wherein the evaluating comprises the calculation of vector distance (VD) from the member (I 1 −I n ) to the at least one achievement objective (O A   1-x ).   
     
     
         2 . The method of  claim 1 , further comprising:
 generating further populations (P G ; P G+1 ) of molecules and evaluating each one by an iterative process until a predefined stop condition is satisfied.   
     
     
         3 . (canceled) 
     
     
         4 . The method of  claim 1 , wherein the evaluating comprises:
 calculating the parameters of each member (I 1 −I n ) of the initial population (P parent ) for each of the at least one achievement objective (O A   1-x ); and   calculating the vector distance (VD) of each member (I 1 −I n ) of the initial population (P parent ) to the at least one achievement objective (O A   1-x ); and optionally calculating and assigning a Pareto frontier ranking to members (I 1 −I n ) of the initial population (P parent ).   
     
     
         5 . The method of  claim 1 , further comprising:
 determining whether a stop condition is satisfied   (a) if the stop condition is satisfied:
 ranking members (I 1 −I n ) of the evaluated initial population (P parent ) according to vector distance (VD) and optionally Pareto frontier to the set of n achievement objectives (O A   1-n ), and 
 identifying at least the first ranked member (I 1 −I n ) of the evaluated initial population (P parent ); and 
   (b) if the stop condition is not satisfied, performing a first iteration (G=1) of the evolutionary algorithm to generate and evaluate a new population (P G ) of at least one molecule, the method comprising:
 transforming at least one member of the parent population (P parent ) to generate a transformed population (P transformed ) of at least one molecule; 
 defining a new population (P G ) of at least one molecule, the new population (P G ) comprising at least one member of the transformed population (P transformed ); 
 optionally evaluating the population (P G ) against at least one achievement objective (O A   1-x ), and selecting molecules from the evaluated population (P G ) by applying a strategy function (S); 
 defining a new population (P G+1 ) of at least one molecule (I 1 −I n ); and 
 evaluating members (I 1 −I n ) of the new population (P G+1 ) against the at least one achievement objective (O A   1-x ); 
 wherein the evaluating comprises the calculation of vector distance (VD) to the at least one achievement objective (O A   1-x ). 
   
     
     
         6 . (canceled) 
     
     
         7 . The method of  claim 5 , wherein the ranking comprises:
 calculating the parameters of each member (I 1 −I n ) of the initial population (P parent ) for each of the n achievement objectives (O A   1-n ); and   calculating the vector distance (VD) and optionally the Pareto rank of each member (I 1 −I n ) of the initial population (P parent ) to each of the n achievement objectives (O A   1-n ); and   wherein the first ranked member has the shortest vector distance (VD) to the achievement objectives (O A   1-n ).   
     
     
         8 - 9 . (canceled) 
     
     
         10 . The method of  claim 5 , wherein the evaluating comprises:
 calculating the parameters of each member (I 1 −I n ) of the new population (P G+1 ) for each of the at least one achievement objective (O A   1-x ); and   calculating the vector distance (VD) of each member (I 1 −I n ) of the new population (P G+1 ) to the at least one achievement objective (O A   1-x ) and optionally calculating and assigning a Pareto frontier ranking to members (I 1 −I n ) of the new population (P G+1 ).   
     
     
         11 . (canceled) 
     
     
         12 . The method of  claim 5 , further, comprising:
 if the stop condition is not satisfied:
 defining the evaluated new population (P G+1 ) as a new parent population (P parent ) of at least one molecule (I 1 −I n ); and 
 performing a second iteration (G=2) of the evolutionary algorithm by repeating the steps of section (b) of  claim 5   
   if the stop condition is satisfied:
 ranking members (I 1 −I n ) of the new population (P G+1 ) according to vector distance (VD) and optionally Pareto frontier to the set of n achievement objectives (O A   1-n ); and 
 identifying at least the first ranked member (I 1 −I n ) of the evaluated new population (P G+1 ). 
   
     
     
         13 . (canceled) 
     
     
         14 . The method of  claim 12 , wherein the ranking comprises:
 calculating the parameters of each member (I 1 −I n ) of the new population (P G+1 ) for the n achievement objectives (O A   1-n ); and   calculating the vector distance (VD) and optionally the Pareto frontier of each member (I 1 −I n ) of the new population (P G+1 ) to the n achievement objectives (O A   1-n ); and   wherein the first ranked member has the shortest vector distance (VD) to the achievement objectives (O A   1-n ).   
     
     
         15 . The method of  claim 12 ,
 wherein P G =P parent +P transformed .   
     
     
         16 . The method of  claim 12 , further comprising:
 applying at least one filter (F) to remove molecules that fail at least one predefined criteria of the filter (F).   
     
     
         17 . The method of  claim 16 , wherein the at least one filter (F) is applied to molecules of the population (P G ) before evaluating the population (P G ) against at least one achievement objective (O A   1-x )); and/or wherein the at least one filter (F) is applied to molecules of the population (P G+1 ) before ranking members (I 1 −I n ) of the new population (P G+1 ). 
     
     
         18 - 19 . (canceled) 
     
     
         20 . The method of  claim 16 , wherein the at least one predefined criteria of the at least one filter (F) is selected from at least one of: no duplicate molecules from the population (P G ); non-broken molecule requirement; solubility; drug-like properties, such as absorption, distribution, metabolism, and excretion (ADME); molecular weight; hydrogen bonding capacity; octanol-water partition coefficient; toxicity; unwanted group definition; total polar surface area; number of rotatable bonds; molecule size, such as number of atoms, number of rings, size of ring systems; number of functional groups, such as H-bond donors, H-bond acceptors; and number of heteroatoms. 
     
     
         21 . The method of  claim 5 , wherein evaluating the population (P G ) against at least one achievement objective (O A   1-x ), and selecting molecules from the evaluated population (P G ) by applying a strategy function (S), comprises:
 identifying at least one desired activity (A) of an optimised molecule and defining a strategy function (S) to score each member (I 1 −I n ) of the population (P G ) against one or more of the at least one desired activity (A 1-n );   calculating the parameters of each member (I 1 −I n ) of the population (P G ) for at least one of the achievement objectives (O A   1-x ) relevant to the one or more desired activity (A 1-n );   determining the predicted activity (Prediction 1 to Prediction n) of each member (I 1 −I n ) of the population (P G ) for the one or more desired activity (A 1-n );   selecting the sub-population (P elite ) of molecules of the population (P G ) that satisfy the strategy function (S); and   optionally selecting a sub population (P random ) of at least one molecule from the sub-population (P non-elite ) of molecules that do not satisfy the strategy function (S).   
     
     
         22 . The method of  claim 21 , wherein the new population (P G+1 ) of at least one molecule (I 1 −I n ) comprises P elite  or P elite +P random . 
     
     
         23 . (canceled) 
     
     
         24 . The method of  claim 21 , wherein the strategy function (S) is satisfied for molecules of the population (P G ) where the predicted activity (Prediction 1) is greater than the sum of the mean predicted activity ([Prediction 1]Mean) and the standard deviation of the predicted activity ([Prediction 1]StdDev) for all members (I 1 −I n ) of the population (P G ); i.e. where:
   Prediction 1>[Prediction 1]Mean+[Prediction 1] StdDev.    
 
     
     
         25 . The method of  claim 21 , wherein the at least one molecule (P random ) from the sub-population (P non-elite ) of molecules that do not satisfy the strategy function (S) is selected at random from the sub-population (P non-elite ). 
     
     
         26 . The method of  claim 21 , wherein the strategy function (S) is based on two or more desired activities (A 1-n ) of an optimised molecule. 
     
     
         27 . The method of  claim 21 , wherein the at least one desired activity (A) of an optimised molecule is selected from one or more of: predicted activity against one or more target molecule (e.g. specificity, binding affinity, inhibition constant); predicted relative activity against one target molecule compared to another molecule; predicted selectivity for one or more target molecule over another molecule; predicted relative selectivity for more than one target molecule; predicted drug-like properties/scores (e.g. ADME); prioritisation of one or more ADME property; prioritisation of drug-like properties over one or more activity or specificity; and prioritisation of vector optimisation over Pareto frontier. 
     
     
         28 . The method of  claim 5 , wherein the transformations are derived from a database of known chemical transformations, and/or the transformations include a null-transformation, and/or the transformations are derived from a combination of known chemical transformations and genetic algorithms. 
     
     
         29 - 30 . (canceled) 
     
     
         31 . The method of  claim 5 , which comprises applying all transformations in the database to all members of the population (P parent ). 
     
     
         32 . The method of  claim 1 , further comprising:
 transforming at least one of the molecules of the population (P G=0 ) to create a transformed population of molecules before selecting the initial population (P parent ).   
     
     
         33 . (canceled) 
     
     
         34 . The method of  claim 1 , wherein the initial population (P parent ) is selected using one or more selection criteria selected from at least one of: 3D virtual docking, chemical similarity, database searching, Bayesian activity modelling, and an algorithm. 
     
     
         35 . The method of  claim 1 , wherein the initial population (P parent ) of molecules consists of one molecule. 
     
     
         36 . The method of  claim 1 , wherein the set of n achievement objectives (O A   1-n ) includes at least one of: inhibition activity against a target molecule; binding affinity to a target molecule; specificity for a target molecule; selectivity for the target molecule over a non-target molecule; pharmacokinetic properties; ADME scores; desirability scores; and ligand efficiency. 
     
     
         37 . The method of  claim 1 , wherein the set of n achievement objectives (O A   1-n ) comprises a plurality of parameter values that define properties of a desired optimised molecule. 
     
     
         38 . The method of  claim 1 , wherein the definition of the set of n achievement objectives (O A   1-n ) includes parameters relating to interactions of an optimised molecule with two or more different target molecules or target sites. 
     
     
         39 . The method of  claim 5 , wherein the stop condition is selected from: the number of iterations (G=n) of the evolutionary algorithm; a predefined vector distance (VD) of a predefined number or proportion of molecules in a population (P parent ; P G ; P G+1 ) to one or more of the set of n achievement objectives (O A   1-n ); mean vector distance ([VD]Mean) of a population (P parent ; P G ; P G+1 ) of molecules to one or more of the set of n achievement objectives (O A   1-n ); the rate of change in the mean vector distance ([VD]Mean) of successive evaluated populations (P parent ; P G ; P G+1 ) of molecules; the rate of change in any other evaluated predefined criteria between successive populations (P parent ; P G ; P G+1 ) of molecules; molecular complexity; and a time limitation. 
     
     
         40 . (canceled) 
     
     
         41 . The method of  claim 12 , further comprising:
 evaluating each of the members (I 1 −I n ) of new population (P G+1 ) of molecules against the set of n achievement objectives (O A   1-n );   applying at least one filter (F);   assigning to each molecule of the population (P G+1 ) a vector distance (VD) and a Pareto frontier to the n achievement objectives (O A   1-n );   ranking each member of the population (P G+1 ) of molecules by vector distance to the set of n achievement objectives (O A   1-n );   ranking the members of the population (P G+1 ) of molecules in at least the first two Pareto frontiers according to vector distance (VD) to the set of n achievement objectives (O A   1-n ); and   identifying at least the first ranked member of the population (P G+1 ) in at least the first Pareto frontier.   
     
     
         42 - 48 . (canceled) 
     
     
         49 . A carrier medium for carrying a computer readable code for controlling a computing device to carry out the method of  claim 1 . 
     
     
         50 . A computing device for computational drug design using an evolutionary algorithm, comprising:
 input means arranged to receive a set of n achievement objectives (O A   1-n );   processing means arranged to generate a population (P G=0 ; P parent ) of at least one molecule and to evaluate the population (P G=0 ; P parent ) against the n achievement objectives (O A   1-n ); and   output means arranged to output results of the evaluation;   wherein the processing means is arranged to evaluate members of the population according to vector distance (VD) to the set of n achievement objectives (O A   1-n ) and optionally Pareto frontier; and optionally   input, processing and/or output means for performing any of the steps of  claim 1 .   
     
     
         51 . (canceled)