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.
Focusing attention on relevant information while ignoring distracting stimuli is essential to the efficacy of working memory. Alpha- and theta-band oscillations have been linked to the inhibition of anticipated and attentionally avoidable distractors. However, the neurophysiological background of the rejection of task-irrelevant stimuli appearing in the focus of attention is not fully understood. We aimed to examine whether theta and alpha-band oscillations serve as an indicator of successful distractor rejection. Twenty-four students were enrolled in the study. 64-channel EEG was recorded during a modified Sternberg working memory task where weak and strong (salient) distractors were presented during the retention period. Event-related spectral perturbation in the alpha frequency band was significantly modulated by the saliency of the distracting stimuli, while theta oscillation was modulated by the need for cognitive control. Moreover, stronger alpha desynchronization to strong relative to weak distracting stimuli significantly increased the probability of mistakenly identifying the presented distractor as a member of the memory sequence. Therefore, our results suggest that alpha activity reflects the vulnerability of attention to distracting salient stimuli.
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.
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