Extracting Compound Profiling Matrices from Screening Data

Set-Theoretic Formalism for Treating Ligand-Target Datasets

Data on ligand–target (LT) interactions has played a growing role in drug research for several decades. Even though the amount of data has grown significantly in size and coverage during this period, most datasets remain difficult to analyze because of their extreme sparsity, as there is no activity data whatsoever for many LT pairs. Even within clusters of data there tends to be a lack of data completeness, making the analysis of LT datasets problematic. The current effort extends earlier works on the development of set-theoretic formalisms for treating thresholded LT datasets. Unlike many approaches that do not address pairs of unknown interaction, the current work specifically takes account of their presence in addition to that of active and inactive pairs. Because a given LT pair can be in any one of three states, the binary logic of classical set-theoretic methods does not strictly apply. The current work develops a formalism, based on ternary set-theoretic relations, for treating thresholded LT datasets. It also describes an extension of the concept of data completeness, which is typically applied to sets of ligands and targets, to the local data completeness of individual ligands and targets. The set-theoretic formalism is applied to the analysis of simple and joint polypharmacologies based on LT activity profiles, and it is shown that null pairs provide a means for determining bounds to these values. The methodology is applied to a dataset of protein kinase inhibitors as an illustration of the method. Although not dealt with here, work is currently underway on a more refined treatment of activity values that is based on increasing the number of activity classes.

Public resources for chemical probes: the journey so far and the road ahead

High-quality small molecule chemical probes are extremely valuable for biological research and target validation. However, frequent use of flawed small-molecule inhibitors produces misleading results and diminishes the robustness of biomedical research. Several public resources are available to facilitate assessment and selection of better chemical probes for specific protein targets. Here, we review chemical probe resources, discuss their current strengths and limitations, and make recommendations for further improvements. Expert review resources provide in-depth analysis but currently cover only a limited portion of the liganded proteome. Computational resources encompass more proteins and are regularly updated, but have limitations in data availability and curation. We show how biomedical scientists may use these resources to choose the best available chemical probes for their research.

The impact of chemoinformatics on drug discovery in the pharmaceutical industry

Introduction: Even though there have been substantial advances in our understanding of biological systems, research in drug discovery is only just now beginning to utilize this type of information. The single-target paradigm, which exemplifies the reductionist approach, remains a mainstay of drug research today. A deeper view of the complexity involved in drug discovery is necessary to advance on this field.Areas covered: This perspective provides a summary of research areas where cheminformatics has played a key role in drug discovery, including of the available resources as well as a personal perspective of the challenges still faced in the field.Expert opinion: Although great strides have been made in the handling and analysis of biological and pharmacological data, more must be done to link the data to biological pathways. This is crucial if one is to understand how drugs modify disease phenotypes, although this will involve a shift from the single drug/single target paradigm that remains a mainstay of drug research. Moreover, such a shift would require an increased awareness of the role of physiology in the mechanism of drug action, which will require the introduction of new mathematical, computer, and biological methods for chemoinformaticians to be trained in.

Nuclear Receptors Database Including Negative Data (NR-DBIND): A Database Dedicated to Nuclear Receptors Binding Data Including Negative Data and Pharmacological Profile

Nuclear receptors (NRs) are transcription factors that regulate gene expression in various physiological processes through their interactions with small hydrophobic molecules. They constitute an important class of targets for drugs and endocrine disruptors and are widely studied for both health and environment concerns. Since the integration of negative data can be critical for accurate modeling of ligand activity profiles, we manually collected and annotated NRs interaction data (positive and negative) through a sharp review of the corresponding literature. 15 116 positive and negative interactions data are provided for 28 NRs together with 593 PDB structures in the freely available Nuclear Receptors Database Including Negative Data ( http://nr-dbind.drugdesign.fr ). The NR-DBIND contains the most extensive information about interaction data on NRs, which should bring valuable information to chemists, biologists, pharmacologists and toxicologists.

Artificial Intelligence in Drug Design

Artificial Intelligence (AI) plays a pivotal role in drug discovery. In particular artificial neural networks such as deep neural networks or recurrent networks drive this area. Numerous applications in property or activity predictions like physicochemical and ADMET properties have recently appeared and underpin the strength of this technology in quantitative structure-property relationships (QSPR) or quantitative structure-activity relationships (QSAR). Artificial intelligence in de novo design drives the generation of meaningful new biologically active molecules towards desired properties. Several examples establish the strength of artificial intelligence in this field. Combination with synthesis planning and ease of synthesis is feasible and more and more automated drug discovery by computers is expected in the near future.

Prediction of Compound Profiling Matrices, Part II: Relative Performance of Multitask Deep Learning and Random Forest Classification on the Basis of Varying Amounts of Training Data

Currently, there is a high level of interest in deep learning and multitask learning in many scientific fields including the life sciences and chemistry. Herein, we investigate the performance of multitask deep neural networks (MT-DNNs) compared to random forest (RF) classification, a standard method in machine learning, in predicting compound profiling experiments. Predictions were carried out on a large profiling matrix extracted from biological screening data. For model building, submatrices with varying data density of 5-100% were generated to investigate the influence of data sparseness on prediction performance. MT-DNN models were directly compared to RF models, and control calculations were also carried out using single-task DNNs (ST-DNNs). On the basis of compound recall, the performance of ST-DNN was consistently lower than that of the other methods. Compared to RF, MT-DNN models only yielded better prediction performance for individual assays in the profiling matrix when training data were very sparse. However, when the matrix density increased to at least 25-45%, per-assay RF models met or partly exceeded the prediction performance of MT-DNN models. When the average performances of RF and MT-DNN over the grid of all targets were compared, MT-DNN was slightly superior to RF, which was a likely consequence of multitask learning. Overall, there was no consistent advantage of MT-DNN over standard RF classification in predicting the results of compound profiling assays under varying conditions. In the presence of very sparse training data, prediction performance was limited. Under these challenging conditions, MT-DNN was the preferred approach. When more training data became available and prediction performance increased, RF performance was not inferior to MT-DNN.

Computationally derived compound profiling matrices

Aim:

Screening of compounds against panels of targets yields profiling matrices. Such matrices are excellent test cases for the analysis and prediction of ligand–target interactions. We made three matrices freely available that were extracted from public screening data.

Methodology:

A new algorithm was used to derive complete profiling matrices from assay data.

Data:

Two profiling matrices were derived from confirmatory assays containing 53 different targets and 109,925 and 143,310 distinct compounds, respectively. A third matrix was extracted from primary screening assays covering 171 different targets and 224,251 compounds.

Next steps:

Profiling matrices can be used to test computational chemogenomics methods for their ability to predict ligand–target pairs. Additional matrices will be generated for individual target families.

Screening of a given number of small molecules in different assays produces a so-called profiling matrix. This matrix reports for each compound inactivity or activity in all assays. Such profiling matrices are frequently produced in the pharmaceutical industry but rarely disclosed. We have recently reported a computational methodology to derive such matrices from independently conducted biological assays. Herein, we describe three large profiling matrices we have extracted from many experimental screens and made publicly available. These matrices should be helpful to investigators studying the interactions of small molecules with different biological targets.

Shown is a small compound profiling matrix resulting from assaying four compounds (rows) against four target proteins (columns). ‘+’ and ‘−’ signs denote compound activity and inactivity, respectively.

Prediction of Compound Profiling Matrices Using Machine Learning

Screening of compound libraries against panels of targets yields profiling matrices. Such matrices typically contain structurally diverse screening compounds, large numbers of inactives, and small numbers of hits per assay. As such, they represent interesting and challenging test cases for computational screening and activity predictions. In this work, modeling of large compound profiling matrices was attempted that were extracted from publicly available screening data. Different machine learning methods including deep learning were compared and different prediction strategies explored. Prediction accuracy varied for assays with different numbers of active compounds, and alternative machine learning approaches often produced comparable results. Deep learning did not further increase the prediction accuracy of standard methods such as random forests or support vector machines. Target-based random forest models were prioritized and yielded successful predictions of active compounds for many assays.