Molecules

1. A method for designing and synthesizing a drug compound having a particular property for a desired use, the method 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 m ) from the population (P G=0 );

evaluating, by a computer device, members (I 1 -I m ) 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 the members (I 1 -I m ) of the initial population (P parent ) comprises:

calculating the parameters of each member (I 1 -I m ) of the initial population f(P parent ) for the at least one achievement objective (O A 1-x ); and

calculating vector distance (VD) from each member (I 1 -I m ) to the at least one achievement objective (O A 1-x );

wherein the vector distance (VD) between each member (I 1 -I m ) and the at least one achievement objective (O A 1-x ) is defined as:

VD

⁢

⁢

V

A

=

∑

p

=

1

n

⁢

(

x

I

p

-

x

A

p

)

2

,

wherein a desired value of each of the achievement objectives (O A 1-n ) has coordinates (x I 1 , . . . , x I n ), and a calculated value of the corresponding parameter p for each member (I 1 -I m ) of the initial population has coordinates (x A 1 , . . . , x A n ); and

determining whether a stop condition is satisfied;

(a) upon determining that the stop condition is satisfied, performing a first process including:

ranking members (I 1 -I m ) of the evaluated initial population (P parent ) according to a shortest vector distance (VD) and optionally Pareto frontier to the set of n achievement objectives (O A 1-n ), wherein a first ranked member (I 1 -I m ) has a shortest vector distance (VD) to the set of n achievement objectives (O A 1-n ) compared to any other vector distance (VD) of any other ranked member (I 1 -I m ), and

identifying a set of z ranked members including at least the first ranked member (I 1 -I m ) of the evaluated initial population (P parent ), where z is 1, 2, 5, 10, 20,50 or 100; and

(b) upon determining that the stop condition is not satisfied, performing a second process including:

performing a first iteration (G=1) of an evolutionary algorithm to generate and evaluate a new population (P G ) of at least one molecule by:

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 new 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 m ); and

evaluating members (I 1 -I m ) of the new population (P G+1 ) against the at least one achievement objective (O A 1-x );

wherein the evaluating the new population (P G ) comprises performing the calculation of vector distance (VD) to the at least one achievement objective (O A 1-x ) to identify a set of z ranked members including at least a first ranked member (I 1 -I m ) of the evaluated new population (P G ) having a shortest vector distance to the at least one achievement objective (O A 1-x ); and

selectively synthesizing the drug compound based on any of the members of the identified set of z ranked members (I 1 -I m ) of the evaluated initial population (P parent ) or based on any of the members of the identified set of z ranked members (I 1 -I m ) of the evaluated new population (P G ).

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. The method of claim 1 , wherein the evaluating comprises:

calculating the parameters of each member (I 1 -I m ) 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 m ) 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 m ) of the initial population (P parent ).

4. The method of claim 1 , wherein the ranking comprises:

calculating the parameters of each member (I 1 -I m ) 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 m ) of the initial population (P parent ) to each of the n achievement objectives (O A 1-n ).

5. The method of claim 1 , wherein the evaluating comprises:

calculating the parameters of each member (I 1 -I m ) 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 m ) 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 m ) of the new population (P G+1 ).

6. The method of claim 1 , 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 m ); and

performing a second iteration (G=2) of the evolutionary algorithm by repeating the steps of section (b);

if the stop condition is satisfied:

ranking members (I 1 -I m ) 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 m ) of the evaluated new population (P G+1 ).

7. The method of claim 6 , wherein the ranking comprises:

calculating the parameters of each member (I 1 -I m ) 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 m ) of the new population (P G+1 ) to the n achievement objectives (O A 1-n ).

8. The method of claim 6 , wherein P G =P parent +P transformed .

9. The method of claim 6 , further comprising:

applying at least one filter (F) to remove molecules that fail at least one predefined criteria of the filter (F).

10. The method of claim 9 , 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 m ) of the new population (P G+1 ).

11. The method of claim 9 , 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; molecular weight; hydrogen bonding capacity; octanol-water partition coefficient; toxicity; unwanted group definition; total polar surface area; number of rotatable bonds; molecule size; number of functional groups; and number of heteroatoms.

12. The method of claim 1 , 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 optimized molecule and defining a strategy function (S) to score each member (I 1 -I m ) 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 m ) 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 m ) of the population (P G ) for the one or more desired activity (A 1-n );

selecting the sub-population (Pete) 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).

13. The method of claim 12 , wherein the new population (P G+1 ) of at least one molecule (I 1 -I m ) comprises P elite or P elite +P random .

14. The method of claim 12 , 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 m ) of the population (P G ); where:

Prediction 1>[Prediction 1]Mean+[Prediction 1]StdDev.

15. The method of claim 12 , 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 ).

16. The method of claim 12 , wherein the strategy function (S) is based on two or more desired activities (A 1-n ) of an optimized molecule.

17. The method of claim 12 , wherein the at least one desired activity (A) of an optimized molecule is selected from one or more of:

predicted activity against one or more target molecule;

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;

prioritization of one or more ADME property;

prioritization of drug-like properties over one or more activity or specificity; and

prioritization of vector prioritization over Pareto frontier.

18. The method of claim 1 , 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.

19. The method of claim 18 , further comprising:

applying all transformations in the database to all members of the population (P parent ).

20. 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 ).

21. A method for designing and synthesizing a drug compound having a particular property for a desired use, the method 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 m ) from the population (P G=0 );

evaluating, by a computer device, members (I 1 -I m ) 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 the members (I 1 -I m ) of the initial population (P parent ) comprises:

calculating the parameters of each member (I 1 -I m ) of the initial population f(P parent ) for the at least one achievement objective (O A 1-x ); and

calculating vector distance (VD) from each member (I 1 -I m ) to the at least one achievement objective (O A 1-x );

wherein the vector distance (VD) between each member (I 1 -I m ) and the at least one achievement objective (O A 1-x ) is defined as:

VD

⁢

⁢

V

A

=

∑

p

=

1

n

⁢

(

x

I

p

-

x

A

p

)

2

,

wherein a desired value of each of the achievement objectives (O A 1-n ) has coordinates (x I 1 , . . . , x I n ), and a calculated value of the corresponding parameter p for each member (I 1 -I m ) of the initial population has coordinates (x A 1 , . . . , x A n ); and

determining whether a stop condition is satisfied;

(a) upon determining that the stop condition is satisfied, performing a first process including:

ranking members (I 1 -I m ) of the evaluated initial population (P parent ) according to a shortest vector distance (VD) and optionally Pareto frontier to the set of n achievement objectives (O A 1-n ), wherein a first ranked member (I 1 -I m ) has a shortest vector distance (VD) to the set of n achievement objectives (O A 1-n ) compared to any other vector distance (VD) of any other ranked member (I 1 -I m ), and

identifying highest ranking members of the ranked members (I 1 -I m ) including at least the first ranked member (I 1 -I m ) of the evaluated initial population (P parent ); and

(b) upon determining that the stop condition is not satisfied, performing a second process including:

performing a first iteration (G=1) of an evolutionary algorithm to generate and evaluate a new population (P G ) of at least one molecule by:

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 new 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 m ); and

evaluating members (I 1 -I m ) of the new population (P G+1 ) against the at least one achievement objective (O A 1-x );

wherein the evaluating the new population (P G ) comprises performing the calculation of vector distance (VD) to the at least one achievement objective (O A 1-x ) to identify highest ranking members including at least a first ranked member (I 1 -I m ) of the evaluated new population (P G ) having a shortest vector distance to the at least one achievement objective (O A 1-x ); and

selectively synthesizing the drug compound based on any of the members of the identified highest ranking members (I 1 -I m ) of the evaluated initial population (P parent ) or based on any of the members of the identified highest ranking members (I 1 -I m ) of the evaluated new population (P G ).