Genome-scale screening of drug-target associations relevant to Ki using a chemogenomics approach

Drug repurposing based on the DTD-GNN graph neural network: revealing the relationships among drugs, targets and diseases

MOTIVATION

The rational modelling of the relationship among drugs, targets and diseases is crucial for drug retargeting. While significant progress has been made in studying binary relationships, further research is needed to deepen our understanding of ternary relationships. The application of graph neural networks in drug retargeting is increasing, but further research is needed to determine the appropriate modelling method for ternary relationships and how to capture their complex multi-feature structure.

RESULTS

The aim of this study was to construct relationships among drug, targets and diseases. To represent the complex relationships among these entities, we used a heterogeneous graph structure. Additionally, we propose a DTD-GNN model that combines graph convolutional networks and graph attention networks to learn feature representations and association information, facilitating a more thorough exploration of the relationships. The experimental results demonstrate that the DTD-GNN model outperforms other graph neural network models in terms of AUC, Precision, and F1-score. The study has important implications for gaining a comprehensive understanding of the relationships between drugs and diseases, as well as for further research and application in exploring the mechanisms of drug-disease interactions. The study reveals these relationships, providing possibilities for innovative therapeutic strategies in medicine.

In-silico target prediction by ensemble chemogenomic model based on multi-scale information of chemical structures and protein sequences

Identification and validation of bioactive small-molecule targets is a significant challenge in drug discovery. In recent years, various in-silico approaches have been proposed to expedite time- and resource-consuming experiments for target detection. Herein, we developed several chemogenomic models for target prediction based on multi-scale information of chemical structures and protein sequences. By combining the information of a compound with multiple protein targets together and putting these compound-target pairs into a well-established model, the scores to indicate whether there are interactions between compounds and targets can be derived, and thus a target prediction task can be completed by sorting the outputted scores. To improve the prediction performance, we constructed several chemogenomic models using multi-scale information of chemical structures and protein sequences, and the ensemble model with the best performance was used as our final model. The model was validated by various strategies and external datasets and the promising target prediction capability of the model, i.e., the fraction of known targets identified in the top-k (1 to 10) list of the potential target candidates suggested by the model, was confirmed. Compared with multiple state-of-art target prediction methods, our model showed equivalent or better predictive ability in terms of the top-k predictions. It is expected that our method can be utilized as a powerful computational tool to narrow down the potential targets for experimental testing.

MHADTI: predicting drug-target interactions via multiview heterogeneous information network embedding with hierarchical attention mechanisms

MOTIVATION

Discovering the drug-target interactions (DTIs) is a crucial step in drug development such as the identification of drug side effects and drug repositioning. Since identifying DTIs by web-biological experiments is time-consuming and costly, many computational-based approaches have been proposed and have become an efficient manner to infer the potential interactions. Although extensive effort is invested to solve this task, the prediction accuracy still needs to be improved. More especially, heterogeneous network-based approaches do not fully consider the complex structure and rich semantic information in these heterogeneous networks. Therefore, it is still a challenge to predict DTIs efficiently.

RESULTS

In this study, we develop a novel method via Multiview heterogeneous information network embedding with Hierarchical Attention mechanisms to discover potential Drug-Target Interactions (MHADTI). Firstly, MHADTI constructs different similarity networks for drugs and targets by utilizing their multisource information. Combined with the known DTI network, three drug-target heterogeneous information networks (HINs) with different views are established. Secondly, MHADTI learns embeddings of drugs and targets from multiview HINs with hierarchical attention mechanisms, which include the node-level, semantic-level and graph-level attentions. Lastly, MHADTI employs the multilayer perceptron to predict DTIs with the learned deep feature representations. The hierarchical attention mechanisms could fully consider the importance of nodes, meta-paths and graphs in learning the feature representations of drugs and targets, which makes their embeddings more comprehensively. Extensive experimental results demonstrate that MHADTI performs better than other SOTA prediction models. Moreover, analysis of prediction results for some interested drugs and targets further indicates that MHADTI has advantages in discovering DTIs.

AVAILABILITY AND IMPLEMENTATION

https://github.com/pxystudy/MHADTI.

Side effect prediction based on drug-induced gene expression profiles and random forest with iterative feature selection

One in every ten drug candidates fail in clinical trials mainly due to efficacy and safety related issues, despite in-depth preclinical testing. Even some of the approved drugs such as chemotherapeutics are notorious for their side effects that are burdensome on patients. In order to pave the way for new therapeutics with more tolerable side effects, the mechanisms underlying side effects need to be fully elucidated. In this work, we addressed the common side effects of chemotherapeutics, namely alopecia, diarrhea and edema. A strategy based on Random Forest algorithm unveiled an expression signature involving 40 genes that predicted these side effects with an accuracy of 89%. We further characterized the resulting signature and its association with the side effects using functional enrichment analysis and protein-protein interaction networks. This work contributes to the ongoing efforts in drug development for early identification of side effects to use the resources more effectively.

A unified drug-target interaction prediction framework based on knowledge graph and recommendation system

Prediction of drug-target interactions (DTI) plays a vital role in drug development in various areas, such as virtual screening, drug repurposing and identification of potential drug side effects. Despite extensive efforts have been invested in perfecting DTI prediction, existing methods still suffer from the high sparsity of DTI datasets and the cold start problem. Here, we develop KGE_NFM, a unified framework for DTI prediction by combining knowledge graph (KG) and recommendation system. This framework firstly learns a low-dimensional representation for various entities in the KG, and then integrates the multimodal information via neural factorization machine (NFM). KGE_NFM is evaluated under three realistic scenarios, and achieves accurate and robust predictions on four benchmark datasets, especially in the scenario of the cold start for proteins. Our results indicate that KGE_NFM provides valuable insight to integrate KG and recommendation system-based techniques into a unified framework for novel DTI discovery.

Development of a proteochemometric-based support vector machine model for predicting bioactive molecules of tubulin receptors

Microtubules are receiving enormous interest in drug discovery due to the important roles they play in cellular functions. Targeting tubulin polymerization presents an excellent opportunity for the development of anti-tubulin drugs. Drug resistance and high toxicity of currently used tubulin-binding agents have necessitated the pursuit of novel drug candidates with increased therapeutic potency. The design of novel drug candidates can be achieved using efficient computational techniques to support existing efforts. Proteochemometric (PCM) modeling is a computational technique that can be employed to elucidate the bioactivity relations between related targets and multiple ligands. We have developed a PCM-based Support Vector Machine (SVM) approach for predicting the bioactivity between tubulin receptors and small, drug-like molecules. The bioactivity datasets used for training the SVM algorithm were obtained from the Binding DB database. The SVM-based PCM model yielded a good overall predictive performance with an area under the curve (AUC) of 87%, Matthews correlation coefficient (MCC) of 72%, overall accuracy of 93%, and a classification error of 7%. The algorithm allows the prediction of the likelihood of new interactions based on confidence scores between the query datasets, comprising ligands in SMILES format and protein sequences of tubulin targets. The algorithm has been implemented as a web server known as TubPred, accessible via http://35.167.90.225:5000/ .

Drug Discovery of Spinal Muscular Atrophy (SMA) from the Computational Perspective: A Comprehensive Review

Spinal muscular atrophy (SMA), one of the leading inherited causes of child mortality, is a rare neuromuscular disease arising from loss-of-function mutations of the survival motor neuron 1 (SMN1) gene, which encodes the SMN protein. When lacking the SMN protein in neurons, patients suffer from muscle weakness and atrophy, and in the severe cases, respiratory failure and death. Several therapeutic approaches show promise with human testing and three medications have been approved by the U.S. Food and Drug Administration (FDA) to date. Despite the shown promise of these approved therapies, there are some crucial limitations, one of the most important being the cost. The FDA-approved drugs are high-priced and are shortlisted among the most expensive treatments in the world. The price is still far beyond affordable and may serve as a burden for patients. The blooming of the biomedical data and advancement of computational approaches have opened new possibilities for SMA therapeutic development. This article highlights the present status of computationally aided approaches, including in silico drug repurposing, network driven drug discovery as well as artificial intelligence (AI)-assisted drug discovery, and discusses the future prospects.

Machine learning approaches and databases for prediction of drug-target interaction: a survey paper

The task of predicting the interactions between drugs and targets plays a key role in the process of drug discovery. There is a need to develop novel and efficient prediction approaches in order to avoid costly and laborious yet not-always-deterministic experiments to determine drug–target interactions (DTIs) by experiments alone. These approaches should be capable of identifying the potential DTIs in a timely manner. In this article, we describe the data required for the task of DTI prediction followed by a comprehensive catalog consisting of machine learning methods and databases, which have been proposed and utilized to predict DTIs. The advantages and disadvantages of each set of methods are also briefly discussed. Lastly, the challenges one may face in prediction of DTI using machine learning approaches are highlighted and we conclude by shedding some lights on important future research directions.

Current progress and future perspectives of polypharmacology : From the view of non-small cell lung cancer

A pre-eminent subtype of lung carcinoma, Non-small cell lung cancer accounts for paramount causes of cancer-associated mortality worldwide. Undeterred by the endeavour in the treatment strategies, the overall cure and survival rates for NSCLC remain substandard, particularly in metastatic diseases. Moreover, the emergence of resistance to classic anticancer drugs further deteriorates the situation. These demanding circumstances culminate the need of extended and revamped research for the establishment of upcoming generation cancer therapeutics. Drug repositioning introduces an affordable and efficient strategy to discover novel drug action, especially when integrated with recent systems biology driven stratagem. This review illustrates the trendsetting approaches in repurposing along with their numerous success stories with an emphasize on the NSCLC therapeutics. Indeed, these novel hits, in combination with conventional anticancer agents, will ideally make their way the clinics and strengthen the therapeutic arsenal to combat drug resistance in the near future.

Netpredictor: R and Shiny package to perform drug-target network analysis and prediction of missing links

BACKGROUND

Netpredictor is an R package for prediction of missing links in any given unipartite or bipartite network. The package provides utilities to compute missing links in a bipartite and well as unipartite networks using Random Walk with Restart and Network inference algorithm and a combination of both. The package also allows computation of Bipartite network properties, visualization of communities for two different sets of nodes, and calculation of significant interactions between two sets of nodes using permutation based testing. The application can also be used to search for top-K shortest paths between interactome and use enrichment analysis for disease, pathway and ontology. The R standalone package (including detailed introductory vignettes) and associated R Shiny web application is available under the GPL-2 Open Source license and is freely available to download.

RESULTS

We compared different algorithms performance in different small datasets and found random walk supersedes rest of the algorithms. The package is developed to perform network based prediction of unipartite and bipartite networks and use the results to understand the functionality of proteins in an interactome using enrichment analysis.

CONCLUSION

The rapid application development envrionment like shiny, helps non programmers to develop fast rich visualization apps and we beleieve it would continue to grow in future with further enhancements. We plan to update our algorithms in the package in near future and help scientist to analyse data in a much streamlined fashion.

PyBioMed: a python library for various molecular representations of chemicals, proteins and DNAs and their interactions

Background

With the increasing development of biotechnology and informatics technology, publicly available data in chemistry and biology are undergoing explosive growth. Such wealthy information in these data needs to be extracted and transformed to useful knowledge by various data mining methods. Considering the amazing rate at which data are accumulated in chemistry and biology fields, new tools that process and interpret large and complex interaction data are increasingly important. So far, there are no suitable toolkits that can effectively link the chemical and biological space in view of molecular representation. To further explore these complex data, an integrated toolkit for various molecular representation is urgently needed which could be easily integrated with data mining algorithms to start a full data analysis pipeline.

Results

Herein, the python library PyBioMed is presented, which comprises functionalities for online download for various molecular objects by providing different IDs, the pretreatment of molecular structures, the computation of various molecular descriptors for chemicals, proteins, DNAs and their interactions. PyBioMed is a feature-rich and highly customized python library used for the characterization of various complex chemical and biological molecules and interaction samples. The current version of PyBioMed could calculate 775 chemical descriptors and 19 kinds of chemical fingerprints, 9920 protein descriptors based on protein sequences, more than 6000 DNA descriptors from nucleotide sequences, and interaction descriptors from pairwise samples using three different combining strategies. Several examples and five real-life applications were provided to clearly guide the users how to use PyBioMed as an integral part of data analysis projects. By using PyBioMed, users are able to start a full pipelining from getting molecular data, pretreating molecules, molecular representation to constructing machine learning models conveniently.

Conclusion

PyBioMed provides various user-friendly and highly customized APIs to calculate various features of biological molecules and complex interaction samples conveniently, which aims at building integrated analysis pipelines from data acquisition, data checking, and descriptor calculation to modeling. PyBioMed is freely available at http://projects.scbdd.com/pybiomed.html.

PhID: An Open-Access Integrated Pharmacology Interactions Database for Drugs, Targets, Diseases, Genes, Side-Effects, and Pathways

The current network pharmacology study encountered a bottleneck with a lot of public data scattered in different databases. There is a lack of an open-access and consolidated platform that integrates this information for systemic research. To address this issue, we have developed PhID, an integrated pharmacology database which integrates >400 000 pharmacology elements (drug, target, disease, gene, side-effect, and pathway) and >200 000 element interactions in branches of public databases. PhID has three major applications: (1) assisting scientists searching through the overwhelming amount of pharmacology element interaction data by names, public IDs, molecule structures, or molecular substructures; (2) helping visualizing pharmacology elements and their interactions with a web-based network graph; and (3) providing prediction of drug-target interactions through two modules: PreDPI-ki and FIM, by which users can predict drug-target interactions of PhID entities or some drug-target pairs of their own interest. To get a systems-level understanding of drug action and disease complexity, PhID as a network pharmacology tool was established from the perspective of data layer, visualization layer, and prediction model layer to present information untapped by current databases.

BioTriangle: a web-accessible platform for generating various molecular representations for chemicals, proteins, DNAs/RNAs and their interactions

BACKGROUND

More and more evidences from network biology indicate that most cellular components exert their functions through interactions with other cellular components, such as proteins, DNAs, RNAs and small molecules. The rapidly increasing amount of publicly available data in biology and chemistry enables researchers to revisit interaction problems by systematic integration and analysis of heterogeneous data. Currently, some tools have been developed to represent these components. However, they have some limitations and only focus on the analysis of either small molecules or proteins or DNAs/RNAs. To the best of our knowledge, there is still a lack of freely-available, easy-to-use and integrated platforms for generating molecular descriptors of DNAs/RNAs, proteins, small molecules and their interactions.

RESULTS

Herein, we developed a comprehensive molecular representation platform, called BioTriangle, to emphasize the integration of cheminformatics and bioinformatics into a molecular informatics platform for computational biology study. It contains a feature-rich toolkit used for the characterization of various biological molecules and complex interaction samples including chemicals, proteins, DNAs/RNAs and even their interactions. By using BioTriangle, users are able to start a full pipelining from getting molecular data, molecular representation to constructing machine learning models conveniently.

CONCLUSION

BioTriangle provides a user-friendly interface to calculate various features of biological molecules and complex interaction samples conveniently. The computing tasks can be submitted and performed simply in a browser without any sophisticated installation and configuration process. BioTriangle is freely available at http://biotriangle.scbdd.com.Graphical abstractAn overview of BioTriangle. A platform for generating various molecular representations for chemicals, proteins, DNAs/RNAs and their interactions.

TargetNet: a web service for predicting potential drug-target interaction profiling via multi-target SAR models

Drug-target interactions (DTIs) are central to current drug discovery processes and public health fields. Analyzing the DTI profiling of the drugs helps to infer drug indications, adverse drug reactions, drug-drug interactions, and drug mode of actions. Therefore, it is of high importance to reliably and fast predict DTI profiling of the drugs on a genome-scale level. Here, we develop the TargetNet server, which can make real-time DTI predictions based only on molecular structures, following the spirit of multi-target SAR methodology. Naïve Bayes models together with various molecular fingerprints were employed to construct prediction models. Ensemble learning from these fingerprints was also provided to improve the prediction ability. When the user submits a molecule, the server will predict the activity of the user's molecule across 623 human proteins by the established high quality SAR model, thus generating a DTI profiling that can be used as a feature vector of chemicals for wide applications. The 623 SAR models related to 623 human proteins were strictly evaluated and validated by several model validation strategies, resulting in the AUC scores of 75-100 %. We applied the generated DTI profiling to successfully predict potential targets, toxicity classification, drug-drug interactions, and drug mode of action, which sufficiently demonstrated the wide application value of the potential DTI profiling. The TargetNet webserver is designed based on the Django framework in Python, and is freely accessible at http://targetnet.scbdd.com .

In silico methods for drug repurposing and pharmacology

Data in the biological, chemical, and clinical domains are accumulating at ever-increasing rates and have the potential to accelerate and inform drug development in new ways. Challenges and opportunities now lie in developing analytic tools to transform these often complex and heterogeneous data into testable hypotheses and actionable insights. This is the aim of computational pharmacology, which uses in silico techniques to better understand and predict how drugs affect biological systems, which can in turn improve clinical use, avoid unwanted side effects, and guide selection and development of better treatments. One exciting application of computational pharmacology is drug repurposing-finding new uses for existing drugs. Already yielding many promising candidates, this strategy has the potential to improve the efficiency of the drug development process and reach patient populations with previously unmet needs such as those with rare diseases. While current techniques in computational pharmacology and drug repurposing often focus on just a single data modality such as gene expression or drug-target interactions, we argue that methods such as matrix factorization that can integrate data within and across diverse data types have the potential to improve predictive performance and provide a fuller picture of a drug's pharmacological action. WIREs Syst Biol Med 2016, 8:186-210. doi: 10.1002/wsbm.1337 For further resources related to this article, please visit the WIREs website.

Pred-binding: large-scale protein-ligand binding affinity prediction

Drug target interactions (DTIs) are crucial in pharmacology and drug discovery. Presently, experimental determination of compound-protein interactions remains challenging because of funding investment and difficulties of purifying proteins. In this study, we proposed two in silico models based on support vector machine (SVM) and random forest (RF), using 1589 molecular descriptors and 1080 protein descriptors in 9948 ligand-protein pairs to predict DTIs that were quantified by Ki values. The cross-validation coefficient of determination of 0.6079 for SVM and 0.6267 for RF were obtained, respectively. In addition, the two-dimensional (2D) autocorrelation, topological charge indices and three-dimensional (3D)-MoRSE descriptors of compounds, the autocorrelation descriptors and the amphiphilic pseudo-amino acid composition of protein are found most important for Ki predictions. These models provide a new opportunity for the prediction of ligand-receptor interactions that will facilitate the target discovery and toxicity evaluation in drug development.

Predicting target-ligand interactions using protein ligand-binding site and ligand substructures

Background

Cell proliferation, differentiation, Gene expression, metabolism, immunization and signal transduction require the participation of ligands and targets. It is a great challenge to identify rules governing molecular recognition between chemical topological substructures of ligands and the binding sites of the targets.

Methods

We suppose that the ligand-target interactions are determined by ligand substructures as well as the physical-chemical properties of the binding sites. Therefore, we propose a fragment interaction model (FIM) to describe the interactions between ligands and targets, with the purpose of facilitating the chemical interpretation of ligand-target binding. First we extract target-ligand complexes from sc-PDB database, based on which, we get the target binding sites and the ligands. Then we represent each binding site as a fragment vector based on a target fragment dictionary that is composed of 199 clusters (denoted as fragements in this work) obtained by clustering 4200 trimers according to their physical-chemical properties. And then, we represent each ligand as a substructure vector based on a dictionary containing 747 substructures. Finally, we build the FIM by generating the interaction matrix M (representing the fragment interaction network), and the FIM can later be used for predicting unknown ligand-target interactions as well as providing the binding details of the interactions.

Results

The five-fold cross validation results show that the proposed model can get higher AUC score (92%) than three prevalence algorithms CS-PD (80%), BLM-NII (85%) and RF (85%), demonstrating the remarkable predictive ability of FIM. We also show that the ligand binding sites (local information) overweight the sequence similarities (global information) in ligand-target binding, and introducing too much global information would be harmful to the predictive ability. Moreover, The derived fragment interaction network can provide the chemical insights on the interactions.

Conclusions

The target and ligand bindings are local events, and the local information dominate the binding ability. Though integrating of the global information can promote the predictive ability, the role is very limited. The fragment interaction network is helpful for understanding the mechanism of the ligand-target interaction.

Polypharmacology modelling using proteochemometrics (PCM): recent methodological developments, applications to target families, and future prospects

Proteochemometric (PCM) modelling is a computational method to model the bioactivity of multiple ligands against multiple related protein targets simultaneously.

Computational Prediction of DrugTarget Interactions Using Chemical, Biological, and Network Features

Drugtarget interactions (DTIs) are central to current drug discovery processes. Efforts have been devoted to the development of methodology for predicting DTIs and drugtarget interaction networks. Most existing methods mainly focus on the application of information about drug or protein structure features. In the present work, we proposed a computational method for DTI prediction by combining the information from chemical, biological and network properties. The method was developed based on a learning algorithm-random forest (RF) combined with integrated features for predicting DTIs. Four classes of drugtarget interaction networks in humans involving enzymes, ion channels, G-protein-coupled receptors (GPCRs) and nuclear receptors, are independently used for establishing predictive models. The RF models gave prediction accuracy of 93.52 %, 94.84 %, 89.68 % and 84.72 % for four pharmaceutically useful datasets, respectively. The prediction ability of our approach is comparative to or even better than that of other DTI prediction methods. These comparative results demonstrated the relevance of the network topology as source of information for predicting DTIs. Further analysis confirmed that among our top ranked predictions of DTIs, several DTIs are supported by databases, while the others represent novel potential DTIs. We believe that our proposed approach can help to limit the search space of DTIs and provide a new way towards repositioning old drugs and identifying targets.

Managing large-scale genomic datasets and translation into clinical practice

OBJECTIVE

To summarize excellent current research in the field of Bioinformatics and Translational Informatics with application in the health domain.

METHOD

We provide a synopsis of the articles selected for the IMIA Yearbook 2014, from which we attempt to derive a synthetic overview of current and future activities in the field. A first step of selection was performed by querying MEDLINE with a list of MeSH descriptors completed by a list of terms adapted to the section. Each section editor evaluated independently the set of 1,851 articles and 15 articles were retained for peer-review.

RESULTS

The selection and evaluation process of this Yearbook's section on Bioinformatics and Translational Informatics yielded three excellent articles regarding data management and genome medicine. In the first article, the authors present VEST (Variant Effect Scoring Tool) which is a supervised machine learning tool for prioritizing variants found in exome sequencing projects that are more likely involved in human Mendelian diseases. In the second article, the authors show how to infer surnames of male individuals by crossing anonymous publicly available genomic data from the Y chromosome and public genealogy data banks. The third article presents a statistical framework called iCluster+ that can perform pattern discovery in integrated cancer genomic data. This framework was able to determine different tumor subtypes in colon cancer.

CONCLUSIONS

The current research activities still attest the continuous convergence of Bioinformatics and Medical Informatics, with a focus this year on large-scale biological, genomic, and Electronic Health Records data. Indeed, there is a need for powerful tools for managing and interpreting complex data, but also a need for user-friendly tools developed for the clinicians in their daily practice. All the recent research and development efforts are contributing to the challenge of impacting clinically the results and even going towards a personalized medicine in the near future.

VNP: Interactive Visual Network Pharmacology of Diseases, Targets, and Drugs

In drug discovery, promiscuous targets, multifactorial diseases, and "dirty" drugs construct complex network relationships. Network pharmacology description and analysis not only give a systems-level understanding of drug action and disease complexity but can also help to improve the efficiency of target selection and drug design. Visual network pharmacology (VNP) is developed to visualize network pharmacology of targets, diseases, and drugs with a graph network by using disease, target or drug names, chemical structures, or protein sequence. To our knowledge, VNP is the first free interactive VNP server that should be very helpful for systems pharmacology research. VNP is freely available at http://cadd.whu.edu.cn/ditad/vnpsearch.

Benchmarking of protein descriptor sets in proteochemometric modeling (part 2): modeling performance of 13 amino acid descriptor sets

Background

While a large body of work exists on comparing and benchmarking descriptors of molecular structures, a similar comparison of protein descriptor sets is lacking. Hence, in the current work a total of 13 amino acid descriptor sets have been benchmarked with respect to their ability of establishing bioactivity models. The descriptor sets included in the study are Z-scales (3 variants), VHSE, T-scales, ST-scales, MS-WHIM, FASGAI, BLOSUM, a novel protein descriptor set (termed ProtFP (4 variants)), and in addition we created and benchmarked three pairs of descriptor combinations. Prediction performance was evaluated in seven structure-activity benchmarks which comprise Angiotensin Converting Enzyme (ACE) dipeptidic inhibitor data, and three proteochemometric data sets, namely (1) GPCR ligands modeled against a GPCR panel, (2) enzyme inhibitors (NNRTIs) with associated bioactivities against a set of HIV enzyme mutants, and (3) enzyme inhibitors (PIs) with associated bioactivities on a large set of HIV enzyme mutants.

Results

The amino acid descriptor sets compared here show similar performance (<0.1 log units RMSE difference and <0.1 difference in MCC), while errors for individual proteins were in some cases found to be larger than those resulting from descriptor set differences ( > 0.3 log units RMSE difference and >0.7 difference in MCC). Combining different descriptor sets generally leads to better modeling performance than utilizing individual sets. The best performers were Z-scales (3) combined with ProtFP (Feature), or Z-Scales (3) combined with an average Z-Scale value for each target, while ProtFP (PCA8), ST-Scales, and ProtFP (Feature) rank last.

Conclusions

While amino acid descriptor sets capture different aspects of amino acids their ability to be used for bioactivity modeling is still – on average – surprisingly similar. Still, combining sets describing complementary information consistently leads to small but consistent improvement in modeling performance (average MCC 0.01 better, average RMSE 0.01 log units lower). Finally, performance differences exist between the targets compared thereby underlining that choosing an appropriate descriptor set is of fundamental for bioactivity modeling, both from the ligand- as well as the protein side.