Most drugs exert their effects via multitarget interactions, as hypothesized by polypharmacology. While these multitarget interactions are responsible for the clinical effect profiles of drugs, current methods have failed to uncover the complex relationships between them. Here, we introduce an approach which is able to relate complex drug-protein interaction profiles with effect profiles. Structural data and registered effect profiles of all small-molecule drugs were collected, and interactions to a series of nontarget protein binding sites of each drug were calculated. Statistical analyses confirmed a close relationship between the studied 177 major effect categories and interaction profiles of ca. 1200 FDA-approved small-molecule drugs. On the basis of this relationship, the effect profiles of drugs were revealed in their entirety, and hitherto uncovered effects could be predicted in a systematic manner. Our results show that the prediction power is independent of the composition of the protein set used for interaction profile generation.

Drug Profile Matching (DPM), a novel virtual affinity fingerprinting method capable of predicting the medical effect profiles of small molecules, was introduced by our group recently. The method exploits the information content of interaction patterns generated by flexible docking to a series of rigidly kept nontarget protein active sites. We presented the ability of DPM to classify molecules excellently, and the question arose, what the contribution of 2D and 3D structural features of the small molecules is to the intriguingly high prediction power of DPM. The present study compared the prediction powers for effect profiles of 1163 FDA-approved drug compounds determined by DPM and ChemAxon 2D and 3D similarity fingerprinting approaches. We found that DPM outperformed the 2D and 3D approaches in almost all therapeutic categories for drug classification except for mechanically rigid structural categories where high accuracy was obtained by all three methods. Moreover, we also tested the predictive power of DPM on external data by reducing the parent data set and demonstrated that DPM can overcome the common screening problems of 2D and 3D similarity methods arising from the presence of structurally diverse molecules in certain effect categories.

BackgroundVarious pattern-based methods exist that use in vitro or in silico affinity profiles for classification and functional examination of proteins. Nevertheless, the connection between the protein affinity profiles and the structural characteristics of the binding sites is still unclear. Our aim was to investigate the association between virtual drug screening results (calculated binding free energy values) and the geometry of protein binding sites. Molecular Affinity Fingerprints (MAFs) were determined for 154 proteins based on their molecular docking energy results for 1,255 FDA-approved drugs. Protein binding site geometries were characterized by 420 PocketPicker descriptors. The basic underlying component structure of MAFs and binding site geometries, respectively, were examined by principal component analysis; association between principal components extracted from these two sets of variables was then investigated by canonical correlation and redundancy analyses.ResultsPCA analysis of the MAF variables provided 30 factors which explained 71.4% of the total variance of the energy values while 13 factors were obtained from the PocketPicker descriptors which cumulatively explained 94.1% of the total variance. Canonical correlation analysis resulted in 3 statistically significant canonical factor pairs with correlation values of 0.87, 0.84 and 0.77, respectively. Redundancy analysis indicated that PocketPicker descriptor factors explain 6.9% of the variance of the MAF factor set while MAF factors explain 15.9% of the total variance of PocketPicker descriptor factors. Based on the salient structures of the factor pairs, we identified a clear-cut association between the shape and bulkiness of the drug molecules and the protein binding site descriptors.ConclusionsThis is the first study to investigate complex multivariate associations between affinity profiles and the geometric properties of protein binding sites. We found that, except for few specific cases, the shapes of the binding pockets have relatively low weights in the determination of the affinity profiles of proteins. Since the MAF profile is closely related to the target specificity of ligand binding sites we can conclude that the shape of the binding site is not a pivotal factor in selecting drug targets. Nonetheless, based on strong specific associations between certain MAF profiles and specific geometric descriptors we identified, the shapes of the binding sites do have a crucial role in virtual drug design for certain drug categories, including morphine derivatives, benzodiazepines, barbiturates and antihistamines.

ABSTRACT:We recently introduced drug profile matching 12 (DPM), a novel virtual affinity fingerprinting bioactivity pre-13 diction method. DPM is based on the docking profiles of ca. 30 Finding compounds for a given target is a common computa-31 tional task in a conventional medicinal chemistry program. However, 32 by means of increasingly available bioactivity data, this 33 approach can be reversed to finding targets for compounds. 34 In silico target fishing 1 is an emerging field that aims at pre-35 dicting biological targets of molecules based on their chemical 36 structure. The rise of this area is in connection with that of 37 polypharmacology, 2,3 which posits that drugs act on multiple 38 targets in contrast with the traditional one drug−one target 39 paradigm. As a consequence, it is likely to discover new targets 40 even for well-known drugs. 41Many in silico target prediction tools have been developed, 42 and they were summarized by a recent review. 4 As it is common 43 for drug development methods, target prediction tools can also 44 be divided into two main groups: ligand-based and structure-45 based approaches. 46Similarity search is often used among the ligand-based methods. 47 The most common question that arises in case of similarity 48 based virtual screening is the description of molecular structure. 49 No universal solution seems to exist for this problem, 5 as the 50 best representation used to characterize the molecules depends 51 on the studied activity classes. Therefore, it is important to 52 combine several methods for a given task, e.g. by applying data 53 fusion techniques. 6 An approach that generates off-target profiles 54 of drugs based on their 3D similarity has just been reported, 55 and some of its predictions were proved by a literature survey.

ABSTRACT:We recently introduced drug profile matching (DPM), a novel affinity 14 fingerprinting-based in silico drug repositioning approach. DPM is able to 15 quantitatively predict the complete effect profiles of compounds via probability 16 scores. In the present work, in order to investigate the predictive power of DPM, three