Improving Measures of Chemical Structural Similarity Using Machine Learning on Chemical-Genetic Interactions

Medical device similarity analysis: a promising approach to medical device equivalence regulation

BACKGROUND

This study aims to facilitate the identification of similar devices for both, the European Medical Device Regulation (MDR) and the US 510(k) equivalence pathway by leveraging existing data. Both are related to the regulatory pathway of read across for chemicals, where toxicological data from a known substance is transferred to one under investigation, as they aim to streamline the accreditation process for new devices and chemicals.

RESEARCH DESIGN AND METHODS

This study employs latent semantic analysis to generate similarity values, harnessing the US Food and Drug Administration 510k-database, utilizing their 'Device Descriptions' and 'Intended Use' statements.

RESULTS

For the representative inhaler cluster, similarity values up to 0.999 were generated for devices within a 510(k)-predicate tree, whereas values up to 0.124 were gathered for devices outside this group.

CONCLUSION

Traditionally, MDR equivalence involves manual review of many devices, which is laborious. However, our results suggest that the automated calculation of similarity coefficients streamlines this process, thus reducing regulatory effort, which can be beneficial for patients needing medical devices. Although this study is focused on the European perspective, it can find application within 510(k) equivalence regulation. The conceptual approach is reminiscent of chemical fingerprint similarity analysis employed in read-across.

Chemoinformatics Insights on Molecular Jackhammers and Cancer Cells

One of the most challenging tasks in modern medicine is to find novel efficient cancer therapeutic methods with minimal side effects. The recent discovery of several classes of organic molecules known as "molecular jackhammers" is a promising development in this direction. It is known that these molecules can directly target and eliminate cancer cells with no impact on healthy tissues. However, the underlying microscopic picture remains poorly understood. We present a study that utilizes theoretical analysis together with experimental measurements to clarify the microscopic aspects of jackhammers' anticancer activities. Our physical-chemical approach combines statistical analysis with chemoinformatics methods to design and optimize molecular jackhammers. By correlating specific physical-chemical properties of these molecules with their abilities to kill cancer cells, several important structural features are identified and discussed. Although our theoretical analysis enhances understanding of the molecular interactions of jackhammers, it also highlights the need for further research to comprehensively elucidate their mechanisms and to develop a robust physical-chemical framework for the rational design of targeted anticancer drugs.

iSIM: instant similarity

The quantification of molecular similarity has been present since the beginning of cheminformatics. Although several similarity indices and molecular representations have been reported, all of them ultimately reduce to the calculation of molecular similarities of only two objects at a time. Hence, to obtain the average similarity of a set of molecules, all the pairwise comparisons need to be computed, which demands a quadratic scaling in the number of computational resources. Here we propose an exact alternative to this problem: iSIM (instant similarity). iSIM performs comparisons of multiple molecules at the same time and yields the same value as the average pairwise comparisons of molecules represented by binary fingerprints and real-value descriptors. In this work, we introduce the mathematical framework and several applications of iSIM in chemical sampling, visualization, diversity selection, and clustering.

Exploring chemical space, scaffold diversity, and activity landscape of spleen tyrosine kinase active inhibitors

This study aims to comprehensively characterize 576 inhibitors targeting Spleen Tyrosine Kinase (SYK), a non-receptor tyrosine kinase primarily found in haematopoietic cells, with significant relevance to B-cell receptor function. The objective is to gain insights into the structural requirements essential for potent activity, with implications for various therapeutic applications. Through chemoinformatic analyses, we focus on exploring the chemical space, scaffold diversity, and structure-activity relationships (SAR). By leveraging ECFP4 and MACCS fingerprints, we elucidate the relationship between chemical compounds and visualize the network using RDKit and NetworkX platforms. Additionally, compound clustering and visualization of the associated chemical space aid in understanding overall diversity. The outcomes include identifying consensus diversity patterns to assess global chemical space diversity. Furthermore, incorporating pairwise activity differences enhances the activity landscape visualization, revealing heterogeneous SAR patterns. The dataset analysed in this work has three activity cliff generators, CHEMBL3415598, CHEMBL4780257, and CHEMBL3265037, compounds with high affinity to SYK are very similar to compounds analogues with reasonable potency differences. Overall, this study provides a critical analysis of SYK inhibitors, uncovering potential scaffolds and chemical moieties crucial for their activity, thereby advancing the understanding of their therapeutic potential.

Prediction of molecular-specific mutagenic alerts and related mechanisms of chemicals by a convolutional neural network (CNN) model based on SMILES split

Structural alerts (SAs) are essential to identify chemicals for toxicity evaluation and health risk assessment. We constructed a novel SMILES split-based deep learning model (SSDL) that was trained and verified with 5850 chemicals from the ISSSTY database and 384 external test chemicals from published papers. The training accuracy was above 0.90 and the evaluation metrics (precision, recall and F1-score) all reached 0.78 or above on both internal and external test chemicals. In this model, the molecular-specific fragment importance of chemicals was first quantified independently. Then, the SA identification method based on the importance of these fragments was statistically analyzed and verified with the ISSSTY test and external test chemicals containing one of 28 typical SAs, and most of the performances were better than that of expert rules. Furthermore, a mutagenicity mechanism prediction method was developed using 237 chemicals with four known mutagenic mechanisms based on molecular similarity calibrated by the SSDL method and fragment importance, which significantly improved accuracy in three mechanisms and had comparable accuracy in the other one compared to traditional methods. Overall, the SSDL model quantifying fragment toxicity within molecules would be a novel potentially powerful tool in the determination and visualization of molecular-specific SAs and the prediction of mutagenicity mechanisms for environmental or industrial compounds and drugs.

Application of ensemble learning for predicting GABAA receptor agonists

BACKGROUND

Over the past few decades, agonists binding to the benzodiazepine site of the GABAA receptor have been successfully developed as clinical drugs. Different modulators (agonist, antagonist, and reverse agonist) bound to benzodiazepine sites exhibit different or even opposite pharmacological effects, however, their structures are so similar that it is difficult to distinguish them based solely on molecular skeleton. This study aims to develop classification models for predicting the agonists.

METHODS

306 agonists or non-agonists were collected from literature. Six machine learning algorithms including RF, XGBoost, AdaBoost, GBoost, SVM, and ANN algorithms were employed for model development. Using six descriptors including 1D/2D Descriptors, ECFP4, 2D-Pharmacophore, MACCS, PubChem, and Estate fingerprint to characterize chemical structures. The model interpretability was explored by SHAP method.

RESULTS

The best model demonstrated an AUC value of 0.905 and an MCC value of 0.808 for the test set. The PubMac-based model (PubMac-GB) achieved best AUC values of 0.935 for test set. The SHAP analysis results emphasized that MaccsFP62, ECFP_624, ECFP_724, and PubchemFP213 were the crucial molecular features. Applicability domain analysis was also performed to determine reliable prediction boundaries for the model. The PubMac-GB model was applied to virtual screening for potential GABAA agonists and the top 100 compounds were given.

CONCLUSION

Overall, our ensemble learning-based model (PubMac-GB) achieved comparable performance and would be helpful in effectively identifying agonists of GABAA receptors.

Catalyst Design and Feature Engineering to Improve Selectivity and Reactivity in Two Simultaneous Cross-Coupling Reactions

Highly active catalysts are required in numerous industrial fields; therefore, to minimize costs and development time, catalyst design using machine learning has attracted significant attention. This study focused on a reaction system where two types of cross-coupling reactions, namely, Buchwald-Hartwig type cross-coupling (BHCC) and Suzuki-Miyaura type cross-coupling (SMCC) reactions, occur simultaneously. Constructing a machine-learning model that considers all experimental conditions is essential to accurately predict the product yield for both the BHCC and the SMCC reactions. The objective of this study was to establish explanatory variables x that considered all experimental conditions within the reaction system involving simultaneous cross-couplings and to design catalysts that achieve the target yield and the development of novel reactions. To accomplish this, Bayesian optimization was combined with established variables x to design new catalysts and enhance reaction selectivity. Moreover, the catalyst design in this study successfully pioneered new reactions involving Cu, Rh, and Pt catalysts in a reaction system that did not previously react with transition metals other than Ni or Pd.

Open-Source Machine Learning in Computational Chemistry

The field of computational chemistry has seen a significant increase in the integration of machine learning concepts and algorithms. In this Perspective, we surveyed 179 open-source software projects, with corresponding peer-reviewed papers published within the last 5 years, to better understand the topics within the field being investigated by machine learning approaches. For each project, we provide a short description, the link to the code, the accompanying license type, and whether the training data and resulting models are made publicly available. Based on those deposited in GitHub repositories, the most popular employed Python libraries are identified. We hope that this survey will serve as a resource to learn about machine learning or specific architectures thereof by identifying accessible codes with accompanying papers on a topic basis. To this end, we also include computational chemistry open-source software for generating training data and fundamental Python libraries for machine learning. Based on our observations and considering the three pillars of collaborative machine learning work, open data, open source (code), and open models, we provide some suggestions to the community.

Computer Science Technology in Natural Products Research: A Review of Its Applications and Implications

Computational approaches to drug development are rapidly growing in popularity and have been used to produce significant results. Recent developments in information science have expanded databases and chemical informatics knowledge relating to natural products. Natural products have long been well-studied, and a large number of unique structures and remarkable active substances have been reported. Analyzing accumulated natural product knowledge using emerging computational science techniques is expected to yield more new discoveries. In this article, we discuss the current state of natural product research using machine learning. The basic concepts and frameworks of machine learning are summarized. Natural product research that utilizes machine learning is described in terms of the exploration of active compounds, automatic compound design, and application to spectral data. In addition, efforts to develop drugs for intractable diseases will be addressed. Lastly, we discuss key considerations for applying machine learning in this field. This paper aims to promote progress in natural product research by presenting the current state of computational science and chemoinformatics approaches in terms of its applications, strengths, limitations, and implications for the field.

Good practices and recommendations for using and benchmarking computational metabolomics metabolite annotation tools

BACKGROUND

Untargeted metabolomics approaches based on mass spectrometry obtain comprehensive profiles of complex biological samples. However, on average only 10% of the molecules can be annotated. This low annotation rate hampers biochemical interpretation and effective comparison of metabolomics studies. Furthermore, de novo structural characterization of mass spectral data remains a complicated and time-intensive process. Recently, the field of computational metabolomics has gained traction and novel methods have started to enable large-scale and reliable metabolite annotation. Molecular networking and machine learning-based in-silico annotation tools have been shown to greatly assist metabolite characterization in diverse fields such as clinical metabolomics and natural product discovery.

AIM OF REVIEW

We highlight recent advances in computational metabolite annotation workflows with a special focus on their evaluation and comparison with other tools. Whilst the progress is substantial and promising, we also argue that inconsistencies in benchmarking different tools hamper users from selecting the most appropriate and promising method for their research. We summarize benchmarking strategies of the different tools and outline several recommendations for benchmarking and comparing novel tools.

KEY SCIENTIFIC CONCEPTS OF REVIEW

This review focuses on recent advances in mass spectral library-based and machine learning-supported metabolite annotation workflows. We discuss large-scale library matching and analogue search, the current bloom of mass spectral similarity scores, and how molecular networking has changed the field. In addition, the potentials and challenges of machine learning-supported metabolite annotation workflows are highlighted. Overall, recent developments in computational metabolomics have started to fundamentally change metabolomics workflows, and we expect that as a community we will be able to overcome current method performance ambiguities and annotation bottlenecks.

Drug repositioning based on gene expression data for human HER2-positive breast cancer

Human epidermal growth factor receptor 2 (HER2)-positive breast cancer represents approximately 15-30% of all invasive breast cancers. Despite the recent advances in therapeutic practices of HER2 subtype, drug resistance and tumor recurrence still have remained as major problems. Drug discovery is a long and difficult process, so the aim of this study is to find potential new application for existing therapeutic agents. Gene expression data for breast invasive carcinoma were retrieved from The Cancer Genome Atlas (TCGA) database. The normal and tumor samples were analyzed using Linear Models for Microarray Data (LIMMA) R package in order to find the differentially expressed genes (DEGs). These genes were used as entry for the library of integrated network-based cellular signatures (LINCS) L1000CDS² software and suggested 24 repurposed drugs. According to the obtained results, some of these drugs including vorinostat, mocetinostat, alvocidib, CGP-60474, BMS-387032, AT-7519, and curcumin have significant functional similarity and structural correlation with FDA-approved breast cancer drugs. Based on the drug-target network, which consisted of the repurposed drugs and their target genes, the aforementioned drugs had the highest degrees. Moreover, the experimental approach verified curcumin as an effective therapeutic agent for HER2 positive breast cancer. Hence, our work suggested that some repurposed drugs based on gene expression data can be noticed as potential drugs for the treatment of HER2-positive breast cancer.