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 (OA1-n), where n is at least one; defining a population (PG=0) of at least one molecule; selecting an initial population (Pparent) of at least one molecule (I1−In) from the population (PG=0); and evaluating members (I1−In) of the initial population (Pparent) against at least one of the n achievement objectives (OA1-x), where x is from 1 to n.

Description

FIELD OF THE INVENTION[0001]The invention relates to systems for the design and evolution of molecules, such as drugs. In particular, the invention relates to an in silico system for the design and optimisation of drugs that interact with selected target molecules, and to the drugs so designed.BACKGROUND OF THE INVENTION[0002]Medicinal chemistry is an iterative design process in which the biological properties of an analogous set of compounds are modified and assessed until a compound is discovered that meets required criteria for subsequent development. On average over one thousand compounds (at a cost of up to £2,000 each), may be synthesised and tested during the course of a drug discovery project, as it proceeds from an initial screening ‘hit’ to drug candidates for pre-clinical assessment. Therefore, the cost of developing new drug candidates can be extremely high even before clinical trials can be undertaken.[0003]High-speed analogue chemistry library methods can be useful for...

AI Technical Summary

Benefits of technology

[0120]In contrast, a goal-type criterion can readily be employed when a linear distance from a given target value (which can be changed between the interactions) is to be minimised. Thus, a particular advantage of the invention is that it employs a vector distance (VD) evaluation of molecules to a defined achievement point, in order to prioritise compounds against a desired objective or set of objectives. The use of vector distance evaluation of molecules to a achievement or target point is beneficial because, amongst other things, it provides a simple linear measure of the distance between each molecule and the achievement point, and it can equally take into consideration other relevant parameters of the evaluated molecules. In this way, the vector distance measurement provides a clear, unambiguous measurement that reflects the actual proximity of a molecule to the achievement objectives. Vector distance optimisation can be used by defining an achievement scalarising function (ASF), for example, as demonstrated below.

[0121]In addition to vector distance measurement, the invention my further comprise Pareto frontier ranking (FIG. 2B). In this regard, by further evaluating molecules according to Pareto frontier, the method of the invention is able to use vector distance to prioritise and compare compounds within the same and different Pareto frontier against the one or more achievement objectives, thereby prioritising molecules from the entire population, if desired. Accordingly, in a preferred embodiment of the invention, molecules from each generation are evaluated by Pareto ranking and vector distance (VD) to the various predicted and calculated parameters (such as predicted biological activity and ADMET properties), in order to identify optimised molecules.

[0122]Furthermore, the combined use of Pareto ranking and vector distance during the evolutionary process enables the prioritisation and selection of molecules having different Pareto rankings, which may allow for the blending of unpredictable but ultimately beneficial properties and activities, and / or may also allow molecules from a lower Pareto frontier to be selected ahead of less useful molecules (overall) from a higher Pareto frontier. As shown in FIG. 2B, the highest ranking molecule A (xA,yA) in the population can be readily identified by its linear proximity to the achievement point OA. Compound C on the second Pareto frontier is closer by vector distance to the achievement point than compound D on the first Pareto frontier, and may be prioritised ahead of compound D, to provide useful molecular properties that might otherwise be missed. Accordingly, in some embodiments the invention prioritises molecules within the same Pareto frontier according to vector distance from the target objective; and in other embodiments, the invention prioritises molecules from different Pareto frontiers (e.g. from first, second and third frontiers) according to vector distance from the target objective. In the final iteration of the method, i.e. when the stop condition has been satisfied, it may be particularly advantageous to prioritise the highest ranking molecules (by vector distance) from more than one Pareto frontier (e.g. from first and second, first to third, or first to fourth frontiers) to ensure that the best overall molecules are selected. By way of example, the molecules closest to the achievement point by vector distance may be selected irrespective of their Pareto ranking. In other cases, the selection may be deliberately biased towards the first Pareto frontier: for instance, of the molecules selected at least the first 50% may be taken from the first Pareto frontier and the remainder selected irrespective of frontier ranking.

[0123]Vector distance to the achievement objective (or point) may be calculated using any suitable system or algorithm, for example, using trigonometry or vector notations. With reference to FIG. 3A, in trigonometry notation, an ideal point I (with coordinates xideal, yideal—i.e. where there are two objective coordinates), can be defined as the achievement scalarising function. VA is the vector distance (V) from the ideal point to molecule A (with coordinates xA, yA). Then:VA=√((xA2+yA2)+xideal2+yideal2)−2(√(xA2+yA2))(√(xideal2+yideal2))cos αA)Where:

[0129]In vector notation, VA is the vector distance (V) from the ideal point I (with coordinates xideal, yideal, zideal i.e. where there are 3 objective coordinates), to molecule A (with coordinates xA, yA, zA), is equal to the modulus of the vector AI.Then:

[0130]VA=√((xideal−xA)2+(yideal−yA)2+(zideal−zA)2)Where there are more than 3 objective coordinates in the achievement point the vector distance calculation can be generalised as:VA=∑p=1n(xlp-xAp)2where the ideal point has the coordinates (xI1, . . . , xIn), and molecule A the coordinates (xA1, . . . , xAn).

Problems solved by technology

Therefore, the cost of developing new drug candidates can be extremely high even before clinical trials can be undertaken.

However manual synthesis of individual compounds is usually unavoidable when a chemical series is to be optimised from a ‘lead’ to a clinical drug candidate, because specific, perhaps complicated, chemical design changes might be required.

Another problem in drug design is that during lead optimisation a number of independent, non-correlated (often divergent) properties may need to be optimised, such as: potency against a desired target; selectivity against non-desired targets; low probability of toxicity; and good drug metabolism and pharmacokinetic properties (ADME).

To add to the complications, often the chemical changes that might most benefit one of these properties, such as target specificity, may be detrimental to another property, such as bioavailability.

The process of drug design is also an optimisation problem, as each project starts out with a product profile of desired attributes, e.g. a target function.

However, even though the problem can be described, it is a difficult challenge to find an optimal solution from the vast space of hypothetical feasible solutions.

However, to date, none of the known systems offer the power, versatility and strategic intelligence that are required to achieve the desired levels of process simplification and cost savings.

However, no biological information was used in the selection of the transformations.

Furthermore, the system was not demonstrated in a method for drug discovery.

Hence, to date, none of the prior art has demonstrated utility in the design and prediction of active molecules that are valid drug candidates having desired biological activity.

Another challenge in the art of drug design and evolution is in the area of ‘polypharmacology’.

However, this recognition reveals further problems in the current systems and methods for rational drug design, because of the potential need for optimising multiple structure-activity relationships at the same time.

None of the prior art systems provide a robust method for the design and evolution of drug candidates having desired polypharmacological profiles.

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