Strategies for small molecule library design.

Compilation of an appropriate set of compounds is essential for the success of a small molecule screen. When very little is known about the target and when no or few ligands have been identified, the screening file is often made as diverse as possible. When structural information on the target or target family is available or ligands of the target are known, it is more efficient to apply a ligand- or target-focused bias, so as to predominantly screen compounds that can be expected to modulate the target. One way to achieve this is to select subsets of existing collections; another is to specifically design and synthesize libraries focused on a particular target, target family or mechanism of action. Despite the number of success stories, designing such libraries is still challenging and requires specialized knowledge, especially in emerging target areas such as protein-protein interactions (PPI), epigenetics and the ubiquitin proteasome pathway. BioFocus has successfully produced so-called SoftFocus(®) libraries for many years, evolving their targets from kinases to GPCRs and ion channels to difficult targets in the epigenetics and PPI fields. This article outlines several of the principles applied to SoftFocus library design, showcasing successes achieved by BioFocus' clients. In addition, screening results for a comprehensive set of BioFocus' kinase libraries against 20 kinase targets are used to demonstrate the power of the SoftFocus approach in delivering both selective and less-selective compounds and libraries against these targets. Trademarks: BioFocus(®), SoftFocus(®), HDRA™, FieldFocus™, Thematic Analysis™, ThemePair™ and ThemePair Fragment™ are trademarks of Galapagos NV and/or its affiliates.

Highly selective 2,4-diaminopyrimidine-5-carboxamide inhibitors of Sky kinase.

We report the SAR around a series of 2,4-diaminopyrimidine-5-carboxamide inhibitors of Sky kinase. 2-Aminophenethyl analogs demonstrate excellent potency but moderate kinase selectivity, while 2-aminobenzyl analogs that fill the Ala571 subpocket exhibit good inhibition activity and excellent kinase selectivity.

Optimization of highly selective 2,4-diaminopyrimidine-5-carboxamide inhibitors of Sky kinase.

Optimization of the ADME properties of a series of 2,4-diaminopyrimidine-5-carboxamide inhibitors of Sky kinase resulted in the identification of highly selective compounds with properties suitable for use as in vitro and in vivo tools to probe the effects of Sky inhibition.

Novel and selective spiroindoline-based inhibitors of Sky kinase.

We report the discovery of a novel series of spiroindoline-based inhibitors of Sky kinase that bind in the ATP-binding site and exhibit high levels of kinome selectivity through filling the Ala571-subpocket. These inhibitors exhibit moderate oral bioavailability in the rat due to low absorption across the gut wall.

Simple idea to generate fragment and pharmacophore descriptors and their implications in chemical informatics.

Using a well-defined set of fragments/pharmacophores, a new methodology to calculate fragment/ pharmacophore descriptors for any molecule onto which at least one fragment/pharmacophore can be mapped is presented. To each fragment/pharmacophore present in a molecule, we attach a descriptor that is calculated by identifying the molecule's atoms onto which it maps and summing over its constituent atomic descriptors. The attached descriptors are named C-fragment/pharmacophore descriptors, and this methodology can be applied to any descriptors defined at the atomic level, such as the partition coefficient, molar refractivity, electrotopological state, etc. By using this methodology, the same fragment/pharmacophore can be shown to have different values in different molecules resulting in better discrimination power. As we know, fragment and pharmacophore fingerprints have a lot of applications in chemical informatics. This study has attempted to find the impact of replacing the traditional value of "1" in a fingerprint with real numbers derived form C-fragment/pharmacophore descriptors. One way to do this is to assess the utility of C-fragment/ pharmacophore descriptors in modeling different end points. Here, we exemplify with data from CYP and hERG. The fact that, in many cases, the obtained models were fairly successful and C-fragment descriptors were ranked among the top ones supports the idea that they play an important role in correlation. When we modeled hERG with C-pharmacophore descriptors, however, the model performances decreased slightly, and we attribute this, mainly to the fact that there is no technique capable of handling multiple instances (states). We hope this will open new research, especially in the emerging field of machine learning. Further research is needed to see the impact of C-fragment/pharmacophore descriptors in similarity/dissimilarity applications.

Novel, customizable scoring functions, parameterized using N-PLS, for structure-based drug discovery.

The ability to accurately predict biological affinity on the basis of in silico docking to a protein target remains a challenging goal in the CADD arena. Typically, "standard" scoring functions have been employed that use the calculated docking result and a set of empirical parameters to calculate a predicted binding affinity. To improve on this, we are exploring novel strategies for rapidly developing and tuning "customized" scoring functions tailored to a specific need. In the present work, three such customized scoring functions were developed using a set of 129 high-resolution protein-ligand crystal structures with measured Ki values. The functions were parametrized using N-PLS (N-way partial least squares), a multivariate technique well-known in the 3D quantitative structure-activity relationship field. A modest correlation between observed and calculated pKi values using a standard scoring function (r2 = 0.5) could be improved to 0.8 when a customized scoring function was applied. To mimic a more realistic scenario, a second scoring function was developed, not based on crystal structures but exclusively on several binding poses generated with the Flo+ docking program. Finally, a validation study was conducted by generating a third scoring function with 99 randomly selected complexes from the 129 as a training set and predicting pKi values for a test set that comprised the remaining 30 complexes. Training and test set r2 values were 0.77 and 0.78, respectively. These results indicate that, even without direct structural information, predictive customized scoring functions can be developed using N-PLS, and this approach holds significant potential as a general procedure for predicting binding affinity on the basis of in silico docking.

Inhibition of protein-protein interactions: the discovery of druglike beta-catenin inhibitors by combining virtual and biophysical screening.

The interaction between beta-catenin and Tcf family members is crucial for the Wnt signal transduction pathway, which is commonly mutated in cancer. This interaction extends over a very large surface area (4800 A(2)), and inhibiting such interactions using low molecular weight inhibitors is a challenge. However, protein surfaces frequently contain "hot spots," small patches that are the main mediators of binding affinity. By making tight interactions with a hot spot, a small molecule can compete with a protein. The Tcf3/Tcf4-binding surface on beta-catenin contains a well-defined hot spot around residues K435 and R469. A 17,700 compounds subset of the Pharmacia corporate collection was docked to this hot spot with the QXP program; 22 of the best scoring compounds were put into a biophysical (NMR and ITC) screening funnel, where specific binding to beta-catenin, competition with Tcf4 and finally binding constants were determined. This process led to the discovery of three druglike, low molecular weight Tcf4-competitive compounds with the tightest binder having a K(D) of 450 nM. Our approach can be used in several situations (e.g., when selecting compounds from external collections, when no biochemical functional assay is available, or when no HTS is envisioned), and it may be generally applicable to the identification of inhibitors of protein-protein interactions.

Potent and selective Aurora inhibitors identified by the expansion of a novel scaffold for protein kinase inhibition.

Potent and selective Aurora kinase inhibitors were identified from the combinatorial expansion of the 1,4,5,6-tetrahydropyrrolo[3,4-c]pyrazole bi-cycle, a novel and versatile scaffold designed to target the ATP pocket of protein kinases. The most potent compound reported in this study had an IC(50) of 0.027 microM in the enzymatic assay for Aur-A inhibition and IC(50)s between 0.05 microM and 0.5 microM for the inhibition of proliferation of different tumor cell lines.

Linear and nonlinear methods in modeling the aqueous solubility of organic compounds.

Solubility data for 930 diverse compounds have been analyzed using linear Partial Least Square (PLS) and nonlinear PLS methods, Continuum Regression (CR), and Neural Networks (NN). 1D and 2D descriptors from MOE package in combination with E-state or ISIS keys have been used. The best model was obtained using linear PLS for a combination between 22 MOE descriptors and 65 ISIS keys. It has a correlation coefficient (r2) of 0.935 and a root-mean-square error (RMSE) of 0.468 log molar solubility (log S(w)). The model validated on a test set of 177 compounds not included in the training set has r2 0.911 and RMSE 0.475 log S(w). The descriptors were ranked according to their importance, and at the top of the list have been found the 22 MOE descriptors. The CR model produced results as good as PLS, and because of the way in which cross-validation has been done it is expected to be a valuable tool in prediction besides PLS model. The statistics obtained using nonlinear methods did not surpass those got with linear ones. The good statistic obtained for linear PLS and CR recommends these models to be used in prediction when it is difficult or impossible to make experimental measurements, for virtual screening, combinatorial library design, and efficient leads optimization.

Novel scoring functions comprising QXP, SASA, and protein side-chain entropy terms.

Novel scoring functions that predict the affinity of a ligand for its receptor have been developed. They were built with several statistical tools (partial least squares, genetic algorithms, neural networks) and trained on a data set of 100 crystal structures of receptor-ligand complexes, with affinities spanning 10 log units. The new scoring functions contain both descriptors generated by the QXP docking program and new descriptors that were developed in-house. These new descriptors are based on solvent accessible surface areas and account for conformational entropy changes and desolvation effects of both ligand and receptor upon binding. The predictive r(2) values for a test set of 24 complexes are in the 0.712-0.741 range and RMS prediction errors in the 1.09-1.16 log K(d) range. Inclusion of the new descriptors led to significant improvements in affinity prediction, compared to scoring functions based on QXP descriptors alone. However, the QXP descriptors by themselves perform better in binding mode prediction. The performance of the linear models is comparable to that of the neural networks. The new functions perform very well, but they still need to be validated as universal tools for the prediction of binding affinity.