Privileged Structures Revisited Schneider, Petra; Schneider, Gisbert
Angewandte Chemie (International ed.),
June 26, 2017, Letnik:
56, Številka:
27
Journal Article
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Privileged structures inspire compound library design in medicinal chemistry. We performed a comprehensive analysis of 1.4 million bioactive compounds, with the aim of assessing the prevalence of ...certain molecular frameworks. We used the Shannon entropy formalism to quantify the promiscuity of the most frequently observed atom scaffolds across the annotated target families. This analysis revealed an apparent inverse relationship between hydrogen‐bond‐acceptor count of a scaffold and its potential promiscuity. The results further suggest that chemically easily accessible scaffolds can serve as templates for the generation of bespoke compound libraries with differing degrees of multiple target engagement, and heterocyclic, sp3‐rich frameworks are particularly suited for target‐focused library design. The outcome of our study enables us to place some of the many narratives surrounding the concept of privileged structures into a critical context.
Drug design: Analysis of 1.4 million bioactive compounds revealed an apparent inverse relationship between the sp3 atom and hydrogen‐bond‐acceptor count of a scaffold and its potential promiscuity in terms of binding a variety of protein targets.
Drug discovery is governed by the desire to find ligands with defined modes of action. It has been realized that even designated selective drugs may have more macromolecular targets than is commonly ...thought. Consequently, it will be mandatory to consider multitarget activity for the design of future medicines. Computational models assist medicinal chemists in this effort by helping to eliminate unsuitable lead structures and spot undesired drug effects early in the discovery process. Here, we present a straightforward computational method to find previously unknown targets of pharmacologically active compounds. Validation experiments revealed hitherto unknown targets of the natural product resveratrol and the nonsteroidal anti‐inflammatory drug celecoxib. The obtained results advocate machine learning for polypharmacology‐based molecular design, drug re‐purposing, and the “de‐orphaning” of phenotypic drug effects.
Predicting side effects: A new computational method led to the discovery of hitherto unknown activities of the natural product resveratrol and the non‐steroidal anti‐inflammatory drug celecoxib.
De novo molecular design and in silico prediction of polypharmacological profiles are emerging research topics that will profoundly affect the future of drug discovery and chemical biology. The goal ...is to identify the macromolecular targets of new chemical agents. Although several computational tools for predicting such targets are publicly available, none of these methods was explicitly designed to predict target engagement by de novo-designed molecules. Here we present the development and practical application of a unique technique, self-organizing map—based prediction of drug equivalence relationships (SPiDER), that merges the concepts of self-organizing maps, consensus scoring, and statistical analysis to successfully identify targets for both known drugs and computer-generated molecular scaffolds. We discovered a potential off-target liability of fenofibraterelated compounds, and in a comprehensive prospective application, we identified a multitarget-modulating profile of de novo designed molecules. These results demonstrate that SPiDER may be used to identify innovative compounds in chemical biology and in the early stages of drug discovery, and help investigate the potential side effects of drugs and their repurposing options.
Generative artificial intelligence models present a fresh approach to chemogenomics and de novo drug design, as they provide researchers with the ability to narrow down their search of the chemical ...space and focus on regions of interest. We present a method for molecular de novo design that utilizes generative recurrent neural networks (RNN) containing long short‐term memory (LSTM) cells. This computational model captured the syntax of molecular representation in terms of SMILES strings with close to perfect accuracy. The learned pattern probabilities can be used for de novo SMILES generation. This molecular design concept eliminates the need for virtual compound library enumeration. By employing transfer learning, we fine‐tuned the RNN′s predictions for specific molecular targets. This approach enables virtual compound design without requiring secondary or external activity prediction, which could introduce error or unwanted bias. The results obtained advocate this generative RNN‐LSTM system for high‐impact use cases, such as low‐data drug discovery, fragment based molecular design, and hit‐to‐lead optimization for diverse drug targets.
We present the development and application of a computational molecular de novo design method for obtaining bioactive compounds with desired on‐ and off‐target binding. The approach translates the ...nature‐inspired concept of ant colony optimization to combinatorial building block selection. By relying on publicly available structure–activity data, we developed a predictive quantitative polypharmacology model for 640 human drug targets. By taking reductive amination as an example of a privileged reaction, we obtained novel subtype‐selective and multitarget‐modulating dopamine D4 antagonists, as well as ligands selective for the sigma‐1 receptor with accurately predicted affinities. The nanomolar potencies of the hits obtained, their high ligand efficiencies, and an overall success rate of 90 % demonstrate that this ligand‐based computer‐aided molecular design method may guide target‐focused combinatorial chemistry.
Finding the best way: A molecular design method inspired by ant colony optimization generated novel, highly potent, druglike ligands for the sigma‐1 and dopamine D4 receptors. The computational approach is readily applicable to combinatorial chemistry and delivers focused compound libraries profiled for desired target‐panel activities.
We propose a new matrix learning scheme to extend relevance learning vector quantization (RLVQ), an efficient prototype-based classification algorithm, toward a general adaptive metric. By ...introducing a full matrix of relevance factors in the distance measure, correlations between different features and their importance for the classification scheme can be taken into account and automated, and general metric adaptation takes place during training. In comparison to the weighted Euclidean metric used in RLVQ and its variations, a full matrix is more powerful to represent the internal structure of the data appropriately. Large margin generalization bounds can be transferred to this case, leading to bounds that are independent of the input dimensionality. This also holds for local metrics attached to each prototype, which corresponds to piecewise quadratic decision boundaries. The algorithm is tested in comparison to alternative learning vector quantization schemes using an artificial data set, a benchmark multiclass problem from the UCI repository, and a problem from bioinformatics, the recognition of splice sites for
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In contrast to standard fragment-based drug discovery approaches, dual-display DNA-encoded chemical libraries have the potential to identify fragment pairs that bind simultaneously and benefit from ...the chelate effect. However, the technology has been limited by the difficulty in unambiguously decoding the ligand pairs from large combinatorial libraries. Here we report a strategy that overcomes this limitation and enables the efficient identification of ligand pairs that bind to a target protein. Small organic molecules were conjugated to the 5' and 3' ends of complementary DNA strands that contain a unique identifying code. DNA hybridization followed by an inter-strand code-transfer created a stable dual-display DNA-encoded chemical library of 111,100 members. Using this approach we report the discovery of a low micromolar binder to alpha-1-acid glycoprotein and the affinity maturation of a ligand to carbonic anhydrase IX, an established marker of renal cell carcinoma. The newly discovered subnanomolar carbonic anhydrase IX binder dramatically improved tumour targeting performance in vivo.
Fragment‐like natural products were identified as ligand‐efficient chemical matter for hit‐to‐lead development and chemical‐probe discovery. Relying on a computational method using a topological ...pharmacophore descriptor and a drug database, several macromolecular targets from distinct protein families were expeditiously retrieved for structurally unrelated chemotypes. The selected fragments feature structural dissimilarity to the reference compounds and suitable target affinity, and they offer opportunities for chemical optimization. Experimental confirmation of hitherto unknown macromolecular targets for the selected molecules corroborate the usefulness of the computational approach and suggests broad applicability to chemical biology and molecular medicine.
Target acquired: Hitherto unknown macromolecular targets of the fragment‐like natural products goitrin, isomacroin, and graveolinine were discovered through the use of a computational target‐prediction tool tailored for natural products. The results suggest that such methods will find application in target discovery for natural products and could inspire the design of new chemical entities for chemical biology and molecular medicine.
Rational drug discovery would greatly benefit from a more nuanced appreciation of the activity of pharmacologically active compounds against a diverse panel of macromolecular targets. Already, ...computational target-prediction models assist medicinal chemists in library screening, de novo molecular design, optimization of active chemical agents, drug re-purposing, in the spotting of potential undesired off-target activities, and in the 'de-orphaning' of phenotypic screening hits. The self-organizing map (SOM) algorithm has been employed successfully for these and other purposes. Areas covered: The authors recapitulate contemporary artificial neural network methods for macromolecular target prediction, and present the basic SOM algorithm at a conceptual level. Specifically, they highlight consensus target-scoring by the employment of multiple SOMs, and discuss the opportunities and limitations of this technique. Expert opinion: Self-organizing feature maps represent a straightforward approach to ligand clustering and classification. Some of the appeal lies in their conceptual simplicity and broad applicability domain. Despite known algorithmic shortcomings, this computational target prediction concept has been proven to work in prospective settings with high success rates. It represents a prototypic technique for future advances in the in silico identification of the modes of action and macromolecular targets of bioactive molecules.
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