Methods for Computer-Aided Chemical Biology. Part 4: Selectivity Searching for Ion Channel Ligands and Mapping of Molecular Fragments as Selectivity Markers

Predicting subtype selectivity of dopamine receptor ligands with three-dimensional biologically relevant spectrum

We applied a novel molecular descriptor, three-dimensional biologically relevant spectrum (BRS-3D), in subtype selectivity prediction of dopamine receptor (DR) ligands. BRS-3D is a shape similarity profile calculated by superimposing the objective compounds against 300 template ligands from sc-PDB. First, we constructed five subtype selectivity regression models between DR subtypes D1-D2, D1-D3, D2-D3, D2-D4, and D3-D4. The models' 10-fold cross-validation-squared correlation coefficient (Q² , for training sets) and determination coefficient (R² , for test sets) were in the range of 0.5-0.7 and 0.6-0.8, respectively. Then, four pair-wise (D1-D2, D2-D3, D2-D4, and D3-D4) and a multitype (D2, D3, and D4) classification models were developed with the prediction accuracies around or over 90% (for test sets). Lastly, we compared the performances of the models developed on BRS-3D and classical descriptors. The results showed that BRS-3D performed similarly to classical 2D descriptors and better than other 3D descriptors. Combining BRS-3D and 2D descriptors can further improve the prediction performance. These results confirmed the capacity of BRS-3D in the prediction of DR subtype-selective ligands.

Self Organizing Map-Based Classification of Cathepsin k and S Inhibitors with Different Selectivity Profiles Using Different Structural Molecular Fingerprints: Design and Application for Discovery of Novel Hits

The main step in a successful drug discovery pipeline is the identification of small potent compounds that selectively bind to the target of interest with high affinity. However, there is still a shortage of efficient and accurate computational methods with powerful capability to study and hence predict compound selectivity properties. In this work, we propose an affordable machine learning method to perform compound selectivity classification and prediction. For this purpose, we have collected compounds with reported activity and built a selectivity database formed of 153 cathepsin K and S inhibitors that are considered of medicinal interest. This database has three compound sets, two K/S and S/K selective ones and one non-selective KS one. We have subjected this database to the selectivity classification tool ‘Emergent Self-Organizing Maps’ for exploring its capability to differentiate selective cathepsin inhibitors for one target over the other. The method exhibited good clustering performance for selective ligands with high accuracy (up to 100 %). Among the possibilites, BAPs and MACCS molecular structural fingerprints were used for such a classification. The results exhibited the ability of the method for structure-selectivity relationship interpretation and selectivity markers were identified for the design of further novel inhibitors with high activity and target selectivity.

Follow up: Compound data sets and software tools for chemoinformatics and medicinal chemistry applications: update and data transfer

In 2012, we reported 30 compound data sets and/or programs developed in our laboratory in a data article and made them freely available to the scientific community to support chemoinformatics and computational medicinal chemistry applications. These data sets and computational tools were provided for download from our website. Since publication of this data article, we have generated 13 new data sets with which we further extend our collection of publicly available data and tools. Due to changes in web servers and website architectures, data accessibility has recently been limited at times. Therefore, we have also transferred our data sets and tools to a public repository to ensure full and stable accessibility. To aid in data selection, we have classified the data sets according to scientific subject areas. Herein, we describe new data sets, introduce the data organization scheme, summarize the database content and provide detailed access information in ZENODO (doi: 10.5281/zenodo.8451 and doi:10.5281/zenodo.8455).

Emerging approaches to probing ion channel structure and function

Ion channels, as membrane proteins, are the sensors of the cell. They act as the first line of communication with the world beyond the plasma membrane and transduce changes in the external and internal environments into unique electrical signals to shape the responses of excitable cells. Because of their importance in cellular communication, ion channels have been intensively studied at the structural and functional levels. Here, we summarize the diverse approaches, including molecular and cellular, chemical, optical, biophysical, and computational, used to probe the structural and functional rearrangements that occur during channel activation (or sensitization), inactivation (or desensitization), and various forms of modulation. The emerging insights into the structure and function of ion channels by multidisciplinary approaches allow the development of new pharmacotherapies as well as new tools useful in controlling cellular activity.

Freely available compound data sets and software tools for chemoinformatics and computational medicinal chemistry applications

We have generated a number of  compound data sets and programs for different types of applications in pharmaceutical research. These data sets and programs were originally designed for our research projects and are made publicly available. Without consulting original literature sources, it is difficult to understand specific features of data sets and software tools, basic ideas underlying their design, and applicability domains. Currently, 30 different entries are available for download from our website. In this data article, we provide an overview of the data and tools we make available and designate the areas of research for which they should be useful. For selected data sets and methods/programs, detailed descriptions are given. This article should help interested readers to select data and tools for specific computational investigations.

Computational medicinal chemistry in fragment-based drug discovery: what, how and when

The use of fragment-based drug discovery (FBDD) has increased in the last decade due to the encouraging results obtained to date. In this scenario, computational approaches, together with experimental information, play an important role to guide and speed up the process. By default, FBDD is generally considered as a constructive approach. However, such additive behavior is not always present, therefore, simple fragment maturation will not always deliver the expected results. In this review, computational approaches utilized in FBDD are reported together with real case studies, where applicability domains are exemplified, in order to analyze them, and then, maximize their performance and reliability. Thus, a proper use of these computational tools can minimize misleading conclusions, keeping the credit on FBDD strategy, as well as achieve higher impact in the drug-discovery process. FBDD goes one step beyond a simple constructive approach. A broad set of computational tools: docking, R group quantitative structure–activity relationship, fragmentation tools, fragments management tools, patents analysis and fragment-hopping, for example, can be utilized in FBDD, providing a clear positive impact if they are utilized in the proper scenario – what, how and when. An initial assessment of additive/non-additive behavior is a critical point to define the most convenient approach for fragments elaboration.