Methods for Computer-Aided Chemical Biology. Part 3: Analysis of Structure–Selectivity Relationships through Single- or Dual-Step Selectivity Searching and Bayesian Classification

Aeration strategy based on numerical modelling and the response mechanism of microbial communities under various operating conditions

Aeration system is the main energy consumer in a wastewater treatment process. In this paper, the Naive Bayes classification (NBC) algorithm and response surface method (RSM) were firstly used to establish a methodology to improve the aeration efficiency and estimate effluent quality. Lab-scale experiments were conducted to verify the model. The errors between experimental values and predicted values were 3.36, -0.67 and -3.78% at operating temperatures of 20, 30 and 35 °C, indicating the applicability. To further elucidate the biological mechanisms of the experimental results, the microbial community composition was investigated under various operating conditions, the results shows that aerobic heterotrophic bacteria (HET) activity and COD removal efficiency were promoted at 30 °C. AOB and NOB activity and NH₄⁺-N removal efficiency were promoted at 30-35 °C. These findings together suggest that operating temperature is crucial for activated sludge treatment, which should be considered when regulating DO content or aeration rate in practical application.

Prediction of activity and selectivity profiles of human Carbonic Anhydrase inhibitors using machine learning classification models

The development of selective inhibitors of the clinically relevant human Carbonic Anhydrase (hCA) isoforms IX and XII has become a major topic in drug research, due to their deregulation in several types of cancer. Indeed, the selective inhibition of these two isoforms, especially with respect to the homeostatic isoform II, holds great promise to develop anticancer drugs with limited side effects. Therefore, the development of in silico models able to predict the activity and selectivity against the desired isoform(s) is of central interest. In this work, we have developed a series of machine learning classification models, trained on high confidence data extracted from ChEMBL, able to predict the activity and selectivity profiles of ligands for human Carbonic Anhydrase isoforms II, IX and XII. The training datasets were built with a procedure that made use of flexible bioactivity thresholds to obtain well-balanced active and inactive classes. We used multiple algorithms and sampling sizes to finally select activity models able to classify active or inactive molecules with excellent performances. Remarkably, the results herein reported turned out to be better than those obtained by models built with the classic approach of selecting an a priori activity threshold. The sequential application of such validated models enables virtual screening to be performed in a fast and more reliable way to predict the activity and selectivity profiles against the investigated isoforms.

Comprehensive investigation of selectivity landscape of glycogen synthase kinase-3 inhibitors

Interaction signatures of drug candidates are characteristic to off-target (neutral) and antitarget (negative) effects, inferring reduced efficiency, side-effects and high attrition rate. Today's retroactive scaled-down virtual screening (VS) experiments relying on benchmarking datasets are extensively involved to assess ligand enrichment in the real-world problem. In recent years, unbiased benchmarking sets turned into a tremendous need to assist virtual screening methodologies for emerging drug targets. To date, the benchmarking datasets are quite limited, whereas glycogen synthase kinase-3 (GSK-3) is not included into directories of benchmarking datasets such as DUD-e, MUV, etc. Herein we introduced our in-house algorithm to build an unbiased benchmarking dataset, including highly selective, moderately selective and nonselective inhibitors for a significant therapeutic target - GSK-3, suitable for both ligand-based and structure-based VS approaches. These datasets are unbiased in terms of physico-chemical properties and topological descriptors, as resulted from mean(ROC-AUC) leave-one-out cross-validation (LOO CV). and additional 2 D similarity search. Moreover, we investigated the gradual selectivity dataset by application of multiple 2 D similarity coefficients and distances, 3 D similarity and docking. Besides the resulted links between the enrichment of selective GSK-3 inhibitors and their chemical structures, a database of compounds and their 3 D similarity signatures including cut-off thresholds for enhanced selectivity was generated. 2 D similarity space analysis revealed that selectivity problem cannot be evaluated appropriately with 2 D similarity searching alone. The current analysis provided useful, comprehensive insights, which may facilitate the knowledge-based identification of novel selective GSK-3 inhibitors.Communicated by Ramaswamy H. Sarma.

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.

Predicting selective liver X receptor β agonists using multiple machine learning methods

The classification models for predicting selective LXRβ agonists were firstly established using multiple machine learning methods. The top models can predict selective LXRβ agonists with chemical structure diversity.

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).

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.

Ligand Classifier of Adaptively Boosting Ensemble Decision Stumps (LiCABEDS) and its application on modeling ligand functionality for 5HT-subtype GPCR families

Advanced high-throughput screening (HTS) technologies generate great amounts of bioactivity data, and this data needs to be analyzed and interpreted with attention to understand how these small molecules affect biological systems. As such, there is an increasing demand to develop and adapt cheminformatics algorithms and tools in order to predict molecular and pharmacological properties on the basis of these large data sets. In this manuscript, we report a novel machine-learning-based ligand classification algorithm, named Ligand Classifier of Adaptively Boosting Ensemble Decision Stumps (LiCABEDS), for data-mining and modeling of large chemical data sets to predict pharmacological properties in an efficient and accurate manner. The performance of LiCABEDS was evaluated through predicting GPCR ligand functionality (agonist or antagonist) using four different molecular fingerprints, including Maccs, FP2, Unity, and Molprint 2D fingerprints. Our studies showed that LiCABEDS outperformed two other popular techniques, classification tree and Naive Bayes classifier, on all four types of molecular fingerprints. Parameters in LiCABEDS, including the number of boosting iterations, initialization condition, and a "reject option" boundary, were thoroughly explored and discussed to demonstrate the capability of handling imbalanced data sets, as well as its robustness and flexibility. In addition, the detailed mathematical concepts and theory are also given to address the principle behind statistical prediction models. The LiCABEDS algorithm has been implemented into a user-friendly software package that is accessible online at http://www.cbligand.org/LiCABEDS/ .

Computational analysis of activity and selectivity cliffs

The exploration of structure-activity relationships (SARs) is a major challenge in medicinal chemistry and usually focuses on compound potency for individual targets. However, selectivity of small molecules that are active against related targets is another critical parameter in chemical lead optimization. Here, an integrative approach for the systematic analysis of SARs and structure-selectivity relationships (SSRs) of small molecules is presented. The computational methodology is described and a cathepsin inhibitor set is used to discuss key aspects of the analysis. Combining a numerical scoring scheme and graphical visualization of molecular networks, the approach enables the identification of different local SAR and SSR environments. Comparative analysis of these environments reveals variable relationships between molecular structure, potency, and selectivity. Furthermore, key compounds are identified that are involved in the formation of activity and/or selectivity cliffs and often display structural features that determine compound selectivity.

Application of support vector machine-based ranking strategies to search for target-selective compounds

Support vector machine (SVM)-based selectivity searching has recently been introduced to identify compounds in virtual screening libraries that are not only active for a target protein, but also selective for this target over a closely related member of the same protein family. In simulated virtual screening calculations, SVM-based strategies termed preference ranking and one-versus-all ranking were successfully applied to rank a database and enrich high-ranking positions with selective compounds while removing nonselective molecules from high ranks. In contrast to the original SVM approach developed for binary classification, these strategies enable learning from more than two classes, considering that distinguishing between selective, promiscuously active, and inactive compounds gives rise to a three-class prediction problem. In this chapter, we describe the extension of the one-versus-all strategy to four training classes. Furthermore, we present an adaptation of the preference ranking strategy that leads to higher recall of selective compounds than previously investigated approaches and is applicable in situations where the removal of nonselective compounds from high-ranking positions is not required.

Molecular test systems for computational selectivity studies and systematic analysis of compound selectivity profiles

For chemical genetics and chemical biology, an important task is the identification of small molecules that are selective against individual targets and can be used as molecular probes for specific biological functions. To aid in the development of computational methods for selectivity analysis, molecular benchmark systems have been developed that capture compound selectivity data for pairs of targets. These molecular test systems are utilized for "selectivity searching" and the analysis of structure-selectivity relationships. Going beyond binary selectivity sets focusing on target pairs, a methodological framework, Molecular Formal Concept Analysis (MolFCA), is described for the definition and systematic mining of compound selectivity profiles.

From structure-activity to structure-selectivity relationships: quantitative assessment, selectivity cliffs, and key compounds

The exploration of structure-activity relationships (SARs) in chemical lead optimization is mostly focused on activity against single targets. Because many active compounds have the potential to act against multiple targets, achieving a sufficient degree of target selectivity often becomes a major issue during optimization. Herein we report a data analysis approach to explore compound selectivity in a systematic and quantitative manner. Sets of compounds that are active against multiple targets provide a basis for exploring structure-selectivity relationships (SSRs). Compound similarity and selectivity data are analyzed with the aid of network-like similarity graphs (NSGs), which organize molecular networks on the basis of similarity relationships and SAR index (SARI) values. For this purpose, the SARI framework has been adapted to quantify SSRs. Using sets of compounds with differential activity against four cathepsin thiol proteases, we show that SSRs can be quantitatively described and categorized. Furthermore, local SSR environments are identified, the analysis of which provides insight into compound selectivity determinants at the molecular level. These environments often contain "selectivity cliffs" formed by pairs or groups of similar compounds with significantly different selectivity. Moreover, key compounds are identified that determine characteristic features of single-target SARs and dual-target SSRs. The comparison of compounds involved in the formation of selectivity cliffs often reveals chemical modifications that render compounds target selective.

Ligand prediction from protein sequence and small molecule information using support vector machines and fingerprint descriptors

Support vector machine (SVM) database search strategies are presented that aim at the identification of small molecule ligands for targets for which no ligand information is currently available. In pharmaceutical research and chemical biology, this situation is faced, for example, when studying orphan targets or newly identified members of protein families. To investigate methods for de novo ligand identification in the absence of known three-dimensional target structures or active molecules, we have focused on combining sequence and ligand information for closely and distantly related proteins. To provide a basis for these investigations, a set of 11 protease targets from different families was assembled together with more than 2000 inhibitors directed against individual proteases. We have compared SVM approaches that combine protein sequence and ligand information in different ways and utilize 2D fingerprints as ligand descriptors. These methodologies were applied to search for inhibitors of individual proteases not taken into account during learning. A target sequence-ligand kernel and, in particular, a linear combination of multiple target-directed SVMs consistently identified inhibitors with high accuracy including test cases where homology-based similarity searching using data fusion and conventional SVM ranking nearly or completely failed. The SVM linear combination and target-ligand kernel methods described herein are intuitive and straightforward to adopt for ligand prediction against other targets.

Searching for target-selective compounds using different combinations of multiclass support vector machine ranking methods, kernel functions, and fingerprint descriptors

The identification of small chemical compounds that are selective for a target protein over one or more closely related members of the same family is of high relevance for applications in chemical biology. Conventional 2D similarity searching using known selective molecules as templates has recently been found to preferentially detect selective over non-selective and inactive database compounds. To improve the initially observed search performance, we have attempted to use 2D fingerprints as descriptors for support vector machine (SVM)-based selectivity searching. Different from typically applied binary SVM compound classification, SVM analysis has been adapted here for multiclass predictions and compound ranking to distinguish between selective, active but non-selective, and inactive compounds. In systematic database search calculations, we tested combinations of four alternative SVM ranking schemes, four different kernel functions, and four fingerprints and were able to further improve selectivity search performance by effectively removing non-selective molecules from high ranking positions while retaining high recall of selective compounds.