6.2.1 Compound Modification
The first of the three essential modules for the optimization is the modification of the input structure or the structures, if multiple input structures are provided.
The modification algorithm can either be used to freely explore the surrounding in the chemical space of a parent structure or be implemented in a way that only child structures are generated that fall into certain constraints. The former option would allow the exploration of undesired but potentially fruitful chemical space, since e.g. the inconsistency with the rule of five does not necessarily mean undesired biochemical properties. The latter option focuses on a chemical sub-space with a higher probability for success and therefore may save computational
Figure 6.2: Scheme of the optimiza-tion algorithm: input structures are the initial seed for the modification module. The generated derivates are docked to one or more receptor structures. The efficiency is evalu-ated based on the docking results and structure of the derivatives. The top N scored compounds serve as seed for the next iteration until the efficiency is converged. Finally, all top scored compounds are reevalu-ated by a second (more accurate) scoring procedure.
time and experimental effort. The generated set of 3D molecular structures is then processed to the next module to estimation their affinities.
6.2.2 The Docking Module
The second module reflects the affinity estimation of the modified structures on the target receptor. Here it is referred to as “docking” since the molecular docking algorithm that was employed to predict the hits identified in chapter 4 is applied. The docking part is the same as described in section ?? since it has been validated against a set of known active compounds and it successfully predicted novel active compounds. Multiple receptor structures can be targeted either as target or anti-target. After docking the molecule to each structure a consensus-score is calculated. Here, the average of the target structures is taken as final docking score.
Initially, the optimization was performed on the basis of the pure docking score.
As a first test, compound ID1 (Fig. 6.3) was subjected to the optimization algorithm. The top 20 compounds of each iteration served as seed for the next iteration. As illustrated in Fig. 6.3 the docking score decreased during the iterations to a value of approximately −50 kJ⋅mol−1. Also structural convergence was observed as indicated by the similarity of the generated structures (Fig. 6.3, Panel C). The optimized structures of this first generation optimization algorithm were enriched with hydrogen acceptors and donors forming favorable interactions with the receptor and achieving low scores (data not shown). In the following,
Figure 6.3: Optimization of compound ID1 (A) (Chapter 3). The FlexX total score (B) used for optimization converged to a value of −50 kJ⋅mol−1. The similarity of the generated structures indicates structural convergence (C).
The optimized structures were endowed with several hydrogen donators (D).
the optimization according to an efficiency function that takes the structure of the ligand into account is discussed.
6.2.3 Final Scoring
A final evaluation was inserted to increase the number of true-positives. All compounds which were used as seeds during the optimization procedure were reevaluated. Technically, this was done by rescoring poses with a second scoring function, namely the Hyde module that is implemented in the LeadIT/FlexX software suite [101] and which is designed for reduction of false positives. Hereby, the poses which were generated with FlexX were optimized in the Hyde force field. The relaxed pose is than scored with the Hyde scoring function which is supposed to describe hydrogen bonding and desolvation effects more accurately than the FlexX scoring function [110].
6.2.4 Compound Efficiency
The pure docking score optimizes for high receptor-ligand interactions and there-fore may leave the drug-like chemical space. Therethere-fore, an efficiencyE was intro-duced, that based on both the docking score and properties of the ligand. The efficiency penalized deviations from certain reference values in order to guide the process to both active and preferential drug-like properties. The most efficient structures then serve as seed in the next iteration. A first approach to ensure drug-like structure was to penalize deviations from a target compound mass of m0 =400 Da and a target fraction carbon atoms f0 =57 %. These values were
Figure 6.4: Results obtained by using the efficiency E for the optimization of ligand ID1 from chapter 4. Both docking score (A) and efficiency (B) converge very fast. The similarity of the top scored compounds (C) indicates structural convergence. The final structure reevaluated with Hyde (D).
taken to calculate the ligand efficiency:
where S is the FlexX total score, m the mass, f the fraction of carbon atoms and the respective target values m0 and f0. For σm andσf define the strength of the restraining and were set to 300 Da and 30 % respectively to allow deviations from the target value. The restrained mass restricts the size of the ligands.
Without mass restrictions the size of the ligands is only limited by the size of the target site. The restricted fraction of carbon atoms implicitly penalizes inefficient interactions. The optimization run was repeated using E for the optimization.
In this case only the top 10 compounds of each docking run served as seed for the next iteration. The algorithm converged in only 11 iterations to a FlexX score of −47 kJ⋅mol−1 (Fig. 6.4). The optimized structure had less hydrogen bond acceptors and donors than the optimized structure using the pure FlexX total score (Fig. 6.4). Notably, both compounds were similar in size. The current implementation of the optimization algorithm only allows additions and replacements of atoms but basically no deletions. Therefore, the compounds can only grow upon a particular core structure. In order to achieve even more efficient structures it would be necessary to allow deletions.
6.2.5 Quasi-de novo Design
The current implementation only allows growth of the compounds upon a cer-tain core structure. This limits the chemical space that can be covered by the optimization algorithm. One possibility to overcome this restriction could be to allow atomic deletions and therefore shrinking and regrowth of the compounds.
Alternatively to deletions, it is possible to use smaller fragments of the initial compound. A first attempt to explore this possibility was done using the urea motif that was observed to be present in various hAQP9 inhibitors (Fig. 4.10) and also present in compound ID1. Using the urea motif as input, the dock-ing score converged to a value of −50 kJ⋅mol−1 (Fig. 6.5). The resulting top scored structure was structurally different compared to the previous top scored compounds. This attempt revealed the possibility to use this approach for the generation of completely new scaffolds by using very small initial structures, therefore providing a smooth transition to thede novo design of novel inhibitors.
Figure 6.5: Results obtained from the urea motif. Both the docking score (A) and the efficiency (B) reach comparable level as in the previous cases (6.3 and 6.4). The similarity of the top scored compounds (C) indicates structural convergence. The final structure reevaluated with Hyde (D).