The ChEMBL Database in 2023: a drug discovery platform spanning multiple bioactivity data types and time periods.

ChEMBL (https://www.ebi.ac.uk/chembl/) is a manually curated, high-quality, large-scale, open, FAIR and Global Core Biodata Resource of bioactive molecules with drug-like properties, previously described in the 2012, 2014, 2017 and 2019 Nucleic Acids Research Database Issues. Since its introduction in 2009, ChEMBL's content has changed dramatically in size and diversity of data types. Through incorporation of multiple new datasets from depositors since the 2019 update, ChEMBL now contains slightly more bioactivity data from deposited data vs data extracted from literature. In collaboration with the EUbOPEN consortium, chemical probe data is now regularly deposited into ChEMBL. Release 27 made curated data available for compounds screened for potential anti-SARS-CoV-2 activity from several large-scale drug repurposing screens. In addition, new patent bioactivity data have been added to the latest ChEMBL releases, and various new features have been incorporated, including a Natural Product likeness score, updated flags for Natural Products, a new flag for Chemical Probes, and the initial annotation of the action type for ∼270 000 bioactivity measurements.

Artificial intelligence for natural product drug discovery.

Developments in computational omics technologies have provided new means to access the hidden diversity of natural products, unearthing new potential for drug discovery. In parallel, artificial intelligence approaches such as machine learning have led to exciting developments in the computational drug design field, facilitating biological activity prediction and de novo drug design for molecular targets of interest. Here, we describe current and future synergies between these developments to effectively identify drug candidates from the plethora of molecules produced by nature. We also discuss how to address key challenges in realizing the potential of these synergies, such as the need for high-quality datasets to train deep learning algorithms and appropriate strategies for algorithm validation.

School of cheminformatics in Latin America.

We report the major highlights of the School of Cheminformatics in Latin America, Mexico City, November 24-25, 2022. Six lectures, one workshop, and one roundtable with four editors were presented during an online public event with speakers from academia, big pharma, and public research institutions. One thousand one hundred eighty-one students and academics from seventy-nine countries registered for the meeting. As part of the meeting, advances in enumeration and visualization of chemical space, applications in natural product-based drug discovery, drug discovery for neglected diseases, toxicity prediction, and general guidelines for data analysis were discussed. Experts from ChEMBL presented a workshop on how to use the resources of this major compounds database used in cheminformatics. The school also included a round table with editors of cheminformatics journals. The full program of the meeting and the recordings of the sessions are publicly available at https://www.youtube.com/@SchoolChemInfLA/featured .

EMBL's European Bioinformatics Institute (EMBL-EBI) in 2022.

The European Molecular Biology Laboratory's European Bioinformatics Institute (EMBL-EBI) is one of the world's leading sources of public biomolecular data. Based at the Wellcome Genome Campus in Hinxton, UK, EMBL-EBI is one of six sites of the European Molecular Biology Laboratory (EMBL), Europe's only intergovernmental life sciences organisation. This overview summarises the status of services that EMBL-EBI data resources provide to scientific communities globally. The scale, openness, rich metadata and extensive curation of EMBL-EBI added-value databases makes them particularly well-suited as training sets for deep learning, machine learning and artificial intelligence applications, a selection of which are described here. The data resources at EMBL-EBI can catalyse such developments because they offer sustainable, high-quality data, collected in some cases over decades and made openly availability to any researcher, globally. Our aim is for EMBL-EBI data resources to keep providing the foundations for tools and research insights that transform fields across the life sciences.

Amino acids in transmembrane helix 1 confer major functional differences between human and mouse orthologs of the polyspecific membrane transporter OCT1.

Organic cation transporter 1 (OCT1) is a membrane transporter that affects hepatic uptake of cationic and weakly basic drugs. OCT1 transports structurally highly diverse substrates. The mechanisms conferring this polyspecificity are unknown. Here, we analyzed differences in transport kinetics between human and mouse OCT1 orthologs to identify amino acids that contribute to the polyspecificity of OCT1. Following stable transfection of HEK293 cells, we observed more than twofold differences in the transport kinetics of 22 out of 28 tested substrates. We found that the ß2-adrenergic drug fenoterol was transported with eightfold higher affinity but at ninefold lower capacity by human OCT1. In contrast, the anticholinergic drug trospium was transported with 11-fold higher affinity but at ninefold lower capacity by mouse Oct1. Using human-mouse chimeric constructs and site-directed mutagenesis, we identified nonconserved amino acids Cys36 and Phe32 as responsible for the species-specific differences in fenoterol and trospium uptake. Substitution of Cys36 (human) to Tyr36 (mouse) caused a reversal of the affinity and capacity of fenoterol but not trospium uptake. Substitution of Phe32 to Leu32 caused reversal of trospium but not fenoterol uptake kinetics. Comparison of the uptake of structurally similar ß2-adrenergics and molecular docking analyses indicated the second phenol ring, 3.3 to 4.8 Å from the protonated amino group, as essential for the affinity for fenoterol conferred by Cys36. This is the first study to report single amino acids as determinants of OCT1 polyspecificity. Our findings suggest that structure-function data of OCT1 is not directly transferrable between substrates or species.

Identifying Novel Inhibitors for Hepatic Organic Anion Transporting Polypeptides by Machine Learning-Based Virtual Screening.

Integration of statistical learning methods with structure-based modeling approaches is a contemporary strategy to identify novel lead compounds in drug discovery. Hepatic organic anion transporting polypeptides (OATP1B1, OATP1B3, and OATP2B1) are classical off-targets, and it is well recognized that their ability to interfere with a wide range of chemically unrelated drugs, environmental chemicals, or food additives can lead to unwanted adverse effects like liver toxicity and drug-drug or drug-food interactions. Therefore, the identification of novel (tool) compounds for hepatic OATPs by virtual screening approaches and subsequent experimental validation is a major asset for elucidating structure-function relationships of (related) transporters: they enhance our understanding about molecular determinants and structural aspects of hepatic OATPs driving ligand binding and selectivity. In the present study, we performed a consensus virtual screening approach by using different types of machine learning models (proteochemometric models, conformal prediction models, and XGBoost models for hepatic OATPs), followed by molecular docking of preselected hits using previously established structural models for hepatic OATPs. Screening the diverse REAL drug-like set (Enamine) shows a comparable hit rate for OATP1B1 (36% actives) and OATP1B3 (32% actives), while the hit rate for OATP2B1 was even higher (66% actives). Percentage inhibition values for 44 selected compounds were determined using dedicated in vitro assays and guided the prioritization of several highly potent novel hepatic OATP inhibitors: six (strong) OATP2B1 inhibitors (IC50 values ranging from 0.04 to 6 µM), three OATP1B1 inhibitors (2.69 to 10 µM), and five OATP1B3 inhibitors (1.53 to 10 µM) were identified. Strikingly, two novel OATP2B1 inhibitors were uncovered (C7 and H5) which show high affinity (IC50 values: 40 nM and 390 nM) comparable to the recently described estrone-based inhibitor (IC50 = 41 nM). A molecularly detailed explanation for the observed differences in ligand binding to the three transporters is given by means of structural comparison of the detected binding sites and docking poses.

Integrate mechanistic evidence from new approach methodologies (NAMs) into a read-across assessment to characterise trends in shared mode of action.

Read-across approaches often remain inconclusive as they do not provide sufficient evidence on a common mode of action across the category members. This read-across case study on thirteen, structurally similar, branched aliphatic carboxylic acids investigates the concept of using human-based new approach methods, such as in vitro and in silico models, to demonstrate biological similarity. Five out of the thirteen analogues have preclinical in vivo studies. Three out of them induced lipid accumulation or hypertrophy in preclinical studies with repeated exposure, which leads to the read-across hypothesis that the analogues can potentially induce hepatic steatosis. To confirm the selection of analogues, the expression patterns of the induced differentially expressed genes (DEGs) were analysed in a human liver model. With increasing dose, the expression pattern within the tested analogues got more similar, which serves as a first indication of a common mode of action and suggests differences in the potency of the analogues. Hepatic steatosis is a well-known adverse outcome, for which over 55 adverse outcome pathways have been identified. The resulting adverse outcome pathway (AOP) network, comprised a total 43 MIEs/KEs and enabled the design of an in vitro testing battery. From the AOP network, ten MIEs, early and late KEs were tested to systematically investigate a common mode of action among the grouped compounds. The targeted testing of AOP specific MIE/KEs shows that biological activity in the category decreases with side chain length. A similar trend was evident in measuring liver alterations in zebra fish embryos. However, activation of single MIEs or early KEs at in vivo relevant doses did not necessarily progress to the late KE "lipid accumulation". KEs not related to the read-across hypothesis, testing for example general mitochondrial stress responses in liver cells, showed no trend or biological similarity. Testing scope is a key issue in the design of in vitro test batteries. The Dempster-Shafer decision theory predicted those analogues with in vivo reference data correctly using one human liver model or the CALUX reporter assays. The case study shows that the read-across hypothesis is the key element to designing the testing strategy. In the case of a good mechanistic understanding, an AOP facilitates the selection of reliable human in vitro models to demonstrate a common mode of action. Testing DEGs, MIEs and early KEs served to show biological similarity, whereas the late KEs become important for confirmation, as progression from MIEs to AO is not always guaranteed.

New approach methods (NAMs) supporting read-across: Two neurotoxicity AOP-based IATA case studies

Read-across approaches are considered key in moving away from in vivo animal testing towards addressing data-gaps using new approach methods (NAMs). Ample successful examples are still required to substantiate this strategy. Here we present and discuss the learnings from two OECD IATA endorsed read-across case studies. They involve two classes of pesticides ­ rotenoids and strobilurins ­ each having a defined mode-of-action that is assessed for its neurological hazard by means of an AOP-based testing strategy coupled to toxicokinetic simulations of human tissue concentrations. The endpoint in question is potential mitochondrial respiratory chain mediated neurotoxicity, specifically through inhibition of complex I or III. An AOP linking inhibition of mitochondrial respiratory chain complex I to the degeneration of dopaminergic neurons formed the basis for both cases but was deployed in two different regulatory contexts. The two cases also exemplify several different read-across concepts: analogue versus category approach, consolidated versus putative AOP, positive versus negative prediction (i.e., neurotoxicity versus low potential for neurotoxicity), and structural versus biological similarity. We applied a range of NAMs to explore the toxicodynamic properties of the compounds, e.g., in silico docking as well as in vitro assays and readouts ­ including transcriptomics ­ in various cell systems, all anchored to the relevant AOPs. Interestingly, although some of the data addressing certain elements of the read-across were associated with high uncertainty, their impact on the overall read-across conclusion remained limited. Coupled to the elaborate regulatory review that the two cases underwent, we propose some generic learnings of AOP-based testing strategies supporting read-across.

Data-Driven Ensemble Docking to Map Molecular Interactions of Steroid Analogs with Hepatic Organic Anion Transporting Polypeptides.

Hepatic organic anion transporting polypeptides-OATP1B1, OATP1B3, and OATP2B1-are expressed at the basolateral membrane of hepatocytes, being responsible for the uptake of a wide range of natural substrates and structurally unrelated pharmaceuticals. Impaired function of hepatic OATPs has been linked to clinically relevant drug-drug interactions leading to altered pharmacokinetics of administered drugs. Therefore, understanding the commonalities and differences across the three transporters represents useful knowledge to guide the drug discovery process at an early stage. Unfortunately, such efforts remain challenging because of the lack of experimentally resolved protein structures for any member of the OATP family. In this study, we established a rigorous computational protocol to generate and validate structural models for hepatic OATPs. The multistep procedure is based on the systematic exploration of available protein structures with shared protein folding using normal-mode analysis, the calculation of multiple template backbones from elastic network models, the utilization of multiple template conformations to generate OATP structural models with various degrees of conformational flexibility, and the prioritization of models on the basis of enrichment docking. We employed the resulting OATP models of OATP1B1, OATP1B3, and OATP2B1 to elucidate binding modes of steroid analogs in the three transporters. Steroid conjugates have been recognized as endogenous substrates of these transporters. Thus, investigating this data set delivers insights into mechanisms of substrate recognition. In silico predictions were complemented with in vitro studies measuring the bioactivity of a compound set on OATP expressing cell lines. Important structural determinants conferring shared and distinct binding patterns of steroid analogs in the three transporters have been identified. Overall, this comparative study provides novel insights into hepatic OATP-ligand interactions and selectivity. Furthermore, the integrative computational workflow for structure-based modeling can be leveraged for other pharmaceutical targets of interest.

Combining In Vivo Data with In Silico Predictions for Modeling Hepatic Steatosis by Using Stratified Bagging and Conformal Prediction.

Hepatic steatosis (fatty liver) is a severe liver disease induced by the excessive accumulation of fatty acids in hepatocytes. In this study, we developed reliable in silico models for predicting hepatic steatosis on the basis of an in vivo data set of 1041 compounds measured in rodent studies with repeated oral exposure. The imbalanced nature of the data set (1:8, with the "steatotic" compounds belonging to the minority class) required the use of meta-classifiers-bagging with stratified under-sampling and Mondrian conformal prediction-on top of the base classifier random forest. One major goal was the investigation of the influence of different descriptor combinations on model performance (tested by predicting an external validation set): physicochemical descriptors (RDKit), ToxPrint features, as well as predictions from in silico nuclear receptor and transporter models. All models based upon descriptor combinations including physicochemical features led to reasonable balanced accuracies (BAs between 0.65 and 0.69 for the respective models). Combining physicochemical features with transporter predictions and further with ToxPrint features gave the best performing model (BAs up to 0.7 and efficiencies of 0.82). Whereas both meta-classifiers proved useful for this highly imbalanced toxicity data set, the conformal prediction framework also guarantees the error level and thus might be favored for future studies in the field of predictive toxicology.

A ligand-based computational drug repurposing pipeline using KNIME and Programmatic Data Access: case studies for rare diseases and COVID-19.

Biomedical information mining is increasingly recognized as a promising technique to accelerate drug discovery and development. Especially, integrative approaches which mine data from several (open) data sources have become more attractive with the increasing possibilities to programmatically access data through Application Programming Interfaces (APIs). The use of open data in conjunction with free, platform-independent analytic tools provides the additional advantage of flexibility, re-usability, and transparency. Here, we present a strategy for performing ligand-based in silico drug repurposing with the analytics platform KNIME. We demonstrate the usefulness of the developed workflow on the basis of two different use cases: a rare disease (here: Glucose Transporter Type 1 (GLUT-1) deficiency), and a new disease (here: COVID 19). The workflow includes a targeted download of data through web services, data curation, detection of enriched structural patterns, as well as substructure searches in DrugBank and a recently deposited data set of antiviral drugs provided by Chemical Abstracts Service. Developed workflows, tutorials with detailed step-by-step instructions, and the information gained by the analysis of data for GLUT-1 deficiency syndrome and COVID-19 are made freely available to the scientific community. The provided framework can be reused by researchers for other in silico drug repurposing projects, and it should serve as a valuable teaching resource for conveying integrative data mining strategies.

Moving targets in drug discovery.

Drug Discovery is a lengthy and costly process and has faced a period of declining productivity within the last two decades resulting in increasing importance of integrative data-driven approaches. In this paper, data mining and integration is leveraged to inspect target innovation trends in drug discovery. The study highlights protein families and classes that have received more attention and those that have just emerged in the scientific literature, thus highlighting novel opportunities for drug intervention. In order to delineate the evolution of target-driven research interest from a biological perspective, trends in biological process annotations from Gene Ontology and disease annotations from DisGeNET are captured. The analysis reveals an increasing interest in targets related to immune system processes, and a recurrent trend for targets involved in circulatory system processes. At the level of diseases, targets associated with cancer-related pathologies, intellectual disability, and schizophrenia are increasingly investigated in recent years. The methodology enables researchers to capture trends in research attention in target space at an early stage during the drug discovery process. Workflows, scripts, and data used in this study are publicly available from https://github.com/BZdrazil/Moving_Targets . An interactive web application allows the customized exploration of target, biological process, and disease trends (available at https://rguha.shinyapps.io/MovingTargets/ ).

Differences in Metformin and Thiamine Uptake between Human and Mouse Organic Cation Transporter 1: Structural Determinants and Potential Consequences for Intrahepatic Concentrations.

The most commonly used oral antidiabetic drug, metformin, is a substrate of the hepatic uptake transporter OCT1 (gene name SLC22A1). However, OCT1 deficiency leads to more pronounced reductions of metformin concentrations in mouse than in human liver. Similarly, the effects of OCT1 deficiency on the pharmacokinetics of thiamine were reported to differ between human and mouse. Here, we compared the uptake characteristics of metformin and thiamine between human and mouse OCT1 using stably transfected human embryonic kidney 293 cells. The affinity for metformin was 4.9-fold lower in human than in mouse OCT1, resulting in a 6.5-fold lower intrinsic clearance. Therefore, the estimated liver-to-blood partition coefficient is only 3.34 in human compared with 14.4 in mouse and may contribute to higher intrahepatic concentrations in mice. Similarly, the affinity for thiamine was 9.5-fold lower in human than in mouse OCT1. Using human-mouse chimeric OCT1, we showed that simultaneous substitution of transmembrane helices TMH2 and TMH3 resulted in the reversal of affinity for metformin. Using homology modeling, we suggest several explanations, of which a different interaction of Leu155 (human TMH2) compared with Val156 (mouse TMH2) with residues in TMH3 had the strongest experimental support. In conclusion, the contribution of human OCT1 to the cellular uptake of thiamine and especially of metformin may be much lower than that of mouse OCT1. This may lead to an overestimation of the effects of OCT1 on hepatic concentrations in humans when using mouse as a model. In addition, comparative analyses of human and mouse orthologs may help reveal mechanisms of OCT1 transport. SIGNIFICANCE STATEMENT: OCT1 is a major hepatic uptake transporter of metformin and thiamine, but this study reports strong differences in the affinity for both compounds between human and mouse OCT1. Consequently, intrahepatic metformin concentrations could be much higher in mice than in humans, impacting metformin actions and representing a strong limitation of using rodent animal models for predictions of OCT1-related pharmacokinetics and efficacy in humans. Furthermore, OCT1 transmembrane helices TMH2 and TMH3 were identified to confer the observed species-specific differences in metformin affinity.