PubChem BioAssay Database Research Articles

Development and validation of PAMPA-BBB QSAR model to predict brain penetration potential of novel drug candidates.

Efficiently circumventing the blood-brain barrier (BBB) poses a major hurdle in the development of drugs that target the central nervous system. Although there are several methods to determine BBB permeability of small molecules, the Parallel Artificial Membrane Permeability Assay (PAMPA) is one of the most common assays in drug discovery due to its robust and high-throughput nature. Drug discovery is a long and costly venture, thus, any advances to streamline this process are beneficial. In this study, ∼2,000 compounds from over 60 NCATS projects were screened in the PAMPA-BBB assay to develop a quantitative structure-activity relationship model to predict BBB permeability of small molecules. After analyzing both state-of-the-art and latest machine learning methods, we found that random forest based on RDKit descriptors as additional features provided the best training balanced accuracy (0.70 ± 0.015) and a message-passing variant of graph convolutional neural network that uses RDKit descriptors provided the highest balanced accuracy (0.72) on a prospective validation set. Finally, we correlated in vitro PAMPA-BBB data with in vivo brain permeation data in rodents to observe a categorical correlation of 77%, suggesting that models developed using data from PAMPA-BBB can forecast in vivo brain permeability. Given that majority of prior research has relied on in vitro or in vivo data for assessing BBB permeability, our model, developed using the largest PAMPA-BBB dataset to date, offers an orthogonal means to estimate BBB permeability of small molecules. We deposited a subset of our data into PubChem bioassay database (AID: 1845228) and deployed the best performing model on the NCATS Open Data ADME portal (https://opendata.ncats.nih.gov/adme/). These initiatives were undertaken with the aim of providing valuable resources for the drug discovery community.

Prediction of Cytochrome P450 Inhibition Using a Deep Learning Approach and Substructure Pattern Recognition.

Cytochrome P450 (CYP) is a family of enzymes that are responsible for about 75% of all metabolic reactions. Among them, CYP1A2, CYP2C9, CYP2C19, CYP2D6, and CYP3A4 participate in the metabolism of most drugs and mediate many adverse drug reactions. Therefore, it is necessary to estimate the chemical inhibition of Cytochrome P450 enzymes in drug discovery and the food industry. In the past few decades, many computational models have been reported, and some provided good performance. However, there are still several issues that should be resolved for these models, such as single isoform, models with unbalanced performance, lack of structural characteristics analysis, and poor availability. In the present study, the deep learning models based on python using the Keras framework and TensorFlow were developed for the chemical inhibition of each CYP isoform. These models were established based on a large data set containing 85715 compounds extracted from the PubChem bioassay database. On external validation, the models provided good AUC values with 0.97, 0.94, 0.94, 0.96, and 0.94 for CYP1A2, CYP2C9, CYP2C19, CYP2D6, and CYP3A4, respectively. The models can be freely accessed on the Web server named CYPi-DNNpredictor (cypi.sapredictor.cn), and the codes for the model were made open source in the Supporting Information. In addition, we also analyzed the structural characteristics of chemicals with CYP450 inhibition and detected the structural alerts (SAs), which should be responsible for the inhibition. The SAs were also made available online, named CYPi-SAdetector (cypisa.sapredictor.cn). The models can be used as a powerful tool for the prediction of CYP450 inhibitors, and the SAs should provide useful information for the mechanisms of Cytochrome P450 inhibition.

In Silico Identification of Potential Inhibitors of the SARS-CoV-2 Main Protease among a PubChem Database of Avian Infectious Bronchitis Virus 3CLPro Inhibitors.

Remarkable structural homologies between the main proteases of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and the avian infectious bronchitis virus (IBV) were revealed by comparative amino-acid sequence and 3D structural alignment. Assessing whether reported IBV 3CLPro inhibitors could also interact with SARS-CoV-2 has been undertaken in silico using a PubChem BioAssay database of 388 compounds active on the avian infectious bronchitis virus 3C-like protease. Docking studies of this database on the SARS-CoV-2 protease resulted in the identification of four covalent inhibitors targeting the catalytic cysteine residue and five non-covalent inhibitors for which the binding was further investigated by molecular dynamics (MD) simulations. Predictive ADMET calculations on the nine compounds suggest promising pharmacokinetic properties.

Predictive modeling and therapeutic repurposing of natural compounds against the receptor-binding domain of SARS-CoV-2

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) is a member of the Coronaviridae family, causing major destructions to human life directly and indirectly to the economic crisis around the world. Although there is significant reporting on the whole genome sequences and updated data for the different receptors are widely analyzed and screened to find a proper medication. Only a few bioassay experiments were completed against SARS-CoV-2 spike protein. We collected the compounds dataset from the PubChem Bioassay database having 1786 compounds and split it into the ratio of 80–20% for model training and testing purposes, respectively. Initially, we have created 11 models and validated them using a fivefold validation strategy. The hybrid consensus model shows a predictive accuracy of 95.5% for training and 94% for the test dataset. The model was applied to screen a virtual chemical library of Natural products of 2598 compounds. Our consensus model has successfully identified 75 compounds with an accuracy range of 70–100% as active compounds against SARS-CoV-2 RBD protein. The output of ML data (75 compounds) was taken for the molecular docking and dynamics simulation studies. In the complete analysis, the Epirubicin and Daunorubicin have shown the docking score of −9.937 and −9.812, respectively, and performed well in the molecular dynamics simulation studies. Also, Pirarubicin, an analogue of anthracycline, has widely been used due to its lower cardiotoxicity. It shows the docking score of −9.658, which also performed well during the complete analysis. Hence, after the following comprehensive pipeline-based study, these drugs can be further tested in vivo for further human utilization. Communicated by Ramaswamy H. Sarma

Modeling Structure-Activity Relationship of AMPK Activation.

The adenosine monophosphate activated protein kinase (AMPK) is critical in the regulation of important cellular functions such as lipid, glucose, and protein metabolism; mitochondrial biogenesis and autophagy; and cellular growth. In many diseases—such as metabolic syndrome, obesity, diabetes, and also cancer—activation of AMPK is beneficial. Therefore, there is growing interest in AMPK activators that act either by direct action on the enzyme itself or by indirect activation of upstream regulators. Many natural compounds have been described that activate AMPK indirectly. These compounds are usually contained in mixtures with a variety of structurally different other compounds, which in turn can also alter the activity of AMPK via one or more pathways. For these compounds, experiments are complicated, since the required pure substances are often not yet isolated and/or therefore not sufficiently available. Therefore, our goal was to develop a screening tool that could handle the profound heterogeneity in activation pathways of the AMPK. Since machine learning algorithms can model complex (unknown) relationships and patterns, some of these methods (random forest, support vector machines, stochastic gradient boosting, logistic regression, and deep neural network) were applied and validated using a database, comprising of 904 activating and 799 neutral or inhibiting compounds identified by extensive PubMed literature search and PubChem Bioassay database. All models showed unexpectedly high classification accuracy in training, but more importantly in predicting the unseen test data. These models are therefore suitable tools for rapid in silico screening of established substances or multicomponent mixtures and can be used to identify compounds of interest for further testing.

Prediction of Micronucleus Assay Outcome Using In Vivo Activity Data and Molecular Structure Features.

In vivo micronucleus assay is the widely used genotoxic test to determine the extent of chromosomal aberrations caused by the chemicals in human beings, which plays a significant role in the drug discovery paradigm. To reduce the uncertainties of the in vivo experiments and the expenses, we intended to develop novel machine learning-based tools to predict the toxicity of the compounds with high precision. A total of 372 compounds with known toxicity information were retrieved from the PubChem Bioassay database and literature. The fingerprints and descriptors of the compounds were generated using PaDEL and ChemSAR, respectively, for the analysis. The performance of the models was assessed using the three tires of evaluation strategies such as fivefold, tenfold, and validation by external dataset. Further, structural alerts causing genotoxicity of the compounds were identified using SARpy method. Of note, fingerprint-based random forest model built in our analysis is able to demonstrate the highest accuracy of about 0.97 during tenfold cross-validation. In essence, our study highlights that structural alerts such as chlorocyclohexane and trimethylamine are likely to be the leading cause of toxicity in humans. Indeed, we believe that random forest model generated in this study is appropriate for reduction of test animals and should be considered in the future for the good practice of animal welfare.

Retrospective assessment of rat liver microsomal stability at NCATS: data and QSAR models

Hepatic metabolic stability is a key pharmacokinetic parameter in drug discovery. Metabolic stability is usually assessed in microsomal fractions and only the best compounds progress in the drug discovery process. A high-throughput single time point substrate depletion assay in rat liver microsomes (RLM) is employed at the National Center for Advancing Translational Sciences. Between 2012 and 2020, RLM stability data was generated for ~ 24,000 compounds from more than 250 projects that cover a wide range of pharmacological targets and cellular pathways. Although a crucial endpoint, little or no data exists in the public domain. In this study, computational models were developed for predicting RLM stability using different machine learning methods. In addition, a retrospective time-split validation was performed, and local models were built for projects that performed poorly with global models. Further analysis revealed inherent medicinal chemistry knowledge potentially useful to chemists in the pursuit of synthesizing metabolically stable compounds. In addition, we deposited experimental data for ~ 2500 compounds in the PubChem bioassay database (AID: 1508591). The global prediction models are made publicly accessible (https://opendata.ncats.nih.gov/adme). This is to the best of our knowledge, the first publicly available RLM prediction model built using high-quality data generated at a single laboratory.

Benchmarking Data Sets from PubChem BioAssay Data: Current Scenario and Room for Improvement.

Developing realistic data sets for evaluating virtual screening methods is a task that has been tackled by the cheminformatics community for many years. Numerous artificially constructed data collections were developed, such as DUD, DUD-E, or DEKOIS. However, they all suffer from multiple drawbacks, one of which is the absence of experimental results confirming the impotence of presumably inactive molecules, leading to possible false negatives in the ligand sets. In light of this problem, the PubChem BioAssay database, an open-access repository providing the bioactivity information of compounds that were already tested on a biological target, is now a recommended source for data set construction. Nevertheless, there exist several issues with the use of such data that need to be properly addressed. In this article, an overview of benchmarking data collections built upon experimental PubChem BioAssay input is provided, along with a thorough discussion of noteworthy issues that one must consider during the design of new ligand sets from this database. The points raised in this review are expected to guide future developments in this regard, in hopes of offering better evaluation tools for novel in silico screening procedures.

Learning-to-rank technique based on ignoring meaningless ranking orders between compounds

Learning-to-rank, which is a machine-learning technique for information retrieval, was recently introduced to ligand-based virtual screening to reduce the costs of developing a new drug. The quality of a rank prediction model depends on experimental data such as the compound activity values used for learning. However, activity values that contain a large error could lead to the observation of meaningless order relations at a certain rate. This motivated us to develop a novel learning-to-rank method that ignores two meaningless types of order ranking: those between compounds with similar activity and those between inactive compounds. We evaluated the proposed method using five high-throughput screening assay datasets from the PubChem BioAssay database. The results demonstrated that the proposed method could improve the accuracy of the prediction results by ignoring meaningless ranking orders to overcome the virtual screening problem. We confirmed that, although the proposed method is based on a simple idea, it facilitates accurate virtual screening. The source code is publicly available at https://github.com/akiyamalab/SPDRank.

Chemical Space and Biological Target Network of Anti-Inflammatory Natural Products.

Natural products (NPs) are a promising source of anti-inflammatory molecules for the development of drugs. Despite there being an abundance of reports of large numbers of NPs having bioactivity in preliminary cell-based assays of anti-inflammatory potential, their further optimization and exploration are limited by the lack of a comprehensive understanding of their effective scaffold structure or biological targets. To facilitate target-based studies of anti-inflammatory NPs, the details of 665 NPs reported to have anti-inflammatory activity were extracted from the literature and compiled into a data set we termed InflamNat. The physicochemical properties of the NPs were analyzed, and the distribution of their structures and scaffolds is presented. A compound-target network was constructed from data in the PubChem Bioassay database. The results demonstrated that, compared to natural anticancer compounds in the NPACT database, compounds from the InflamNat data set contained a comparable distribution of compound types but with a higher proportion satisfying Lipinski's rule. The all-atom structures and scaffold of the compounds were diverse and barely convergent, with flavonoids and triterpenoids being the groups with the greatest abundance. The biological targets of the InflamNat compounds were identified as belonging to a variety of protein families that had varied function. Seventy-two percent of InflamNat compounds involved in the network were identified as having more than one biological target, highlighting the potential for multitarget anti-inflammatory drug development. In conclusion, anti-inflammatory NPs provide a good library for the screening of target-based leads or fragment-based drug design. Thus, elucidation of their biological targets is fundamental for either a specific single-target or multitarget drug development strategy. Meanwhile, a large proportion of the chemical space of anti-inflammatory NPs is still unexplored, with novel active scaffolds remaining to be discovered.

Discovery of Mcl-1 inhibitors from integrated high throughput and virtual screening

Protein-protein interactions (PPIs) represent important and promising therapeutic targets that are associated with the regulation of various molecular pathways, particularly in cancer. Although they were once considered “undruggable,” the recent advances in screening strategies, structure-based design, and elucidating the nature of hot spots on PPI interfaces, have led to the discovery and development of successful small-molecule inhibitors. In this report, we are describing an integrated high-throughput and computational screening approach to enable the discovery of small-molecule PPI inhibitors of the anti-apoptotic protein, Mcl-1. Applying this strategy, followed by biochemical, biophysical, and biological characterization, nineteen new chemical scaffolds were discovered and validated as Mcl-1 inhibitors. A novel series of Mcl-1 inhibitors was designed and synthesized based on the identified difuryl-triazine core scaffold and structure-activity studies were undertaken to improve the binding affinity to Mcl-1. Compounds with improved in vitro binding potency demonstrated on-target activity in cell-based studies. The obtained results demonstrate that structure-based analysis complements the experimental high-throughput screening in identifying novel PPI inhibitor scaffolds and guides follow-up medicinal chemistry efforts. Furthermore, our work provides an example that can be applied to the analysis of available screening data against numerous targets in the PubChem BioAssay Database, leading to the identification of promising lead compounds, fuelling drug discovery pipelines.

DPubChem: a web tool for QSAR modeling and high-throughput virtual screening

High-throughput screening (HTS) performs the experimental testing of a large number of chemical compounds aiming to identify those active in the considered assay. Alternatively, faster and cheaper methods of large-scale virtual screening are performed computationally through quantitative structure-activity relationship (QSAR) models. However, the vast amount of available HTS heterogeneous data and the imbalanced ratio of active to inactive compounds in an assay make this a challenging problem. Although different QSAR models have been proposed, they have certain limitations, e.g., high false positive rates, complicated user interface, and limited utilization options. Therefore, we developed DPubChem, a novel web tool for deriving QSAR models that implement the state-of-the-art machine-learning techniques to enhance the precision of the models and enable efficient analyses of experiments from PubChem BioAssay database. DPubChem also has a simple interface that provides various options to users. DPubChem predicted active compounds for 300 datasets with an average geometric mean and F1 score of 76.68% and 76.53%, respectively. Furthermore, DPubChem builds interaction networks that highlight novel predicted links between chemical compounds and biological assays. Using such a network, DPubChem successfully suggested a novel drug for the Niemann-Pick type C disease. DPubChem is freely available at www.cbrc.kaust.edu.sa/dpubchem.

Prediction of Human Cytochrome P450 Inhibition Using a Multitask Deep Autoencoder Neural Network.

Adverse side effects of drug-drug interactions induced by human cytochrome P450 (CYP450) inhibition is an important consideration in drug discovery. It is highly desirable to develop computational models that can predict the inhibitive effect of a compound against a specific CYP450 isoform. In this study, we developed a multitask model for concurrent inhibition prediction of five major CYP450 isoforms, namely, 1A2, 2C9, 2C19, 2D6, and 3A4. The model was built by training a multitask autoencoder deep neural network (DNN) on a large dataset containing more than 13 000 compounds, extracted from the PubChem BioAssay Database. We demonstrate that the multitask model gave better prediction results than that of single-task models, previous reported classifiers, and traditional machine learning methods on an average of five prediction tasks. Our multitask DNN model gave average prediction accuracies of 86.4% for the 10-fold cross-validation and 88.7% for the external test datasets. In addition, we built linear regression models to quantify how the other tasks contributed to the prediction difference of a given task between single-task and multitask models, and we explained under what conditions the multitask model will outperform the single-task model, which suggested how to use multitask DNN models more effectively. We applied sensitivity analysis to extract useful knowledge about CYP450 inhibition, which may shed light on the structural features of these isoforms and give hints about how to avoid side effects during drug development. Our models are freely available at http://repharma.pku.edu.cn/deepcyp/home.php or http://www.pkumdl.cn/deepcyp/home.php .

Extracting Compound Profiling Matrices from Screening Data.

Compound profiling matrices record assay results for compound libraries tested against panels of targets. In addition to their relevance for exploring structure–activity relationships, such matrices are of considerable interest for chemoinformatic and chemogenomic applications. For example, profiling matrices provide a valuable data resource for the development and evaluation of machine learning approaches for multitask activity prediction. However, experimental compound profiling matrices are rare in the public domain. Although they are generated in pharmaceutical settings, they are typically not disclosed. Herein, we present an algorithm for the generation of large profiling matrices, for example, containing more than 100 000 compounds exhaustively tested against 50 to 100 targets. The new methodology is a variant of biclustering algorithms originally introduced for large-scale analysis of genomics data. Our approach is applied here to assays from the PubChem BioAssay database and generates profiling matrices of increasing assay or compound coverage by iterative removal of entities that limit coverage. Weight settings control final matrix size by preferentially retaining assays or compounds. In addition, the methodology can also be applied to generate matrices enriched with active entries representing above-average assay hit rates.

Hit Dexter: A Machine-Learning Model for the Prediction of Frequent Hitters.

False-positive assay readouts caused by badly behaving compounds-frequent hitters, pan-assay interference compounds (PAINS), aggregators, and others-continue to pose a major challenge to experimental screening. There are only a few in silico methods that allow the prediction of such problematic compounds. We report the development of Hit Dexter, two extremely randomized trees classifiers for the prediction of compounds likely to trigger positive assay readouts either by true promiscuity or by assay interference. The models were trained on a well-prepared dataset extracted from the PubChem Bioassay database, consisting of approximately 311 000 compounds tested for activity on at least 50 proteins. Hit Dexter reached MCC and AUC values of up to 0.67 and 0.96 on an independent test set, respectively. The models are expected to be of high value, in particular to medicinal chemists and biochemists who can use Hit Dexter to identify compounds for which extra caution should be exercised with positive assay readouts. Hit Dexter is available as a free web service at http://hitdexter.zbh. uni-hamburg.de.

CAPi: Computational Model for Apicoplast Inhibitors Prediction Against Plasmodium Parasite

Discovery of apicoplast as a drug target offers a new direction in the development of novel anti-malarial compounds, especially against the drug-resistant strains. Drugs such as azithromycin were reported to block the apicoplast development that leads to unusual phenotypes affecting the parasite. This phenomenon suggests that identification of new apicoplast inhibitors will aid in the anti-malarial drug discovery. Therefore, in this study, we developed a computational model to predict apicoplast inhibitors by applying state-of-the-art machine learning techniques. We have used two high-throughput chemical screening data (AID-504850, AID-504848) from PubChem BioAssay database and applied machine learning techniques. The performance of the models were assessed on various types of binary fingerprints. In this study, we developed a robust computational algorithm for the prediction of apicoplast inhibition. We observed 73.7% sensitivity and 84% specificity along with 81.4% accuracy rate only on 41 PubChem fingerprints on 48 hrs dataset. Similarly, an accuracy rate of 75.8% was observed for 96 hrs dataset. Additionally, we observed that our model has ~70% positive prediction rate on the independent dataset obtained from ChEMBL-NTD database. Furthermore, the fingerprint analysis suggested that compounds with at least one heteroatom containing hexagonal ring would most likely belong to the antimalarial category as compared to simple aliphatic compounds. We also observed that aromatic compounds with oxygen and chlorine atoms were preferred in inhibitors class as compared to sulphur. Additionally, the compounds with average molecular weight >380Da and XlogP>4 were most likely to belong to the inhibitor category. This study highlighted the significance of simple interpretable molecular properties along with some preferred substructure in designing the novel anti-malarial compounds. In addition to that, robustness and accuracy of models developed in the present work could be utilized to screen a large chemical library. Based on this study, we developed freely available software at http://deepaklab. com/capi. This study would provide the best alternative for searching the novel apicoplast inhibitors against Plasmodium.

In silico prediction of multiple-category classification model for cytochrome P450 inhibitors and non-inhibitors using machine-learning method

The cytochrome P450 (CYP) enzyme superfamily is involved in phase I metabolism which chemically modifies a variety of substrates via oxidative reactions to make them more water-soluble and easier to eliminate. Inhibition of these enzymes leads to undesirable effects, including toxic drug accumulations and adverse drug–drug interactions. Hence, it is necessary to develop in silico models that can predict the inhibition potential of compounds for different CYP isoforms. This study focused on five major CYP isoforms, including CYP1A2, 2C9, 2C19, 2D6 and 3A4, that are responsible for more than 90% of the metabolism of clinical drugs. The main aim of this study is to develop a multiple-category classification model (MCM) for the major CYP isoforms using a Laplacian-modified naïve Bayesian method. The dataset composed of more than 4500 compounds was collected from the PubChem Bioassay database. VolSurf+ descriptors and FCFP_8 fingerprint were used as input features to build classification models. The results demonstrated that the developed MCM using Laplacian-modified naïve Bayesian method was successful in classifying inhibitors and non-inhibitors for each CYP isoform. Moreover, the accuracy, sensitivity and specificity values for both training and test sets were above 80% and also yielded satisfactory area under the receiver operating characteristic curve and Matthews correlation coefficient values.

PubChem BioAssay: A Decade’s Development toward Open High-Throughput Screening Data Sharing

High-throughput screening (HTS) is now routinely conducted for drug discovery by both pharmaceutical companies and screening centers at academic institutions and universities. Rapid advance in assay development, robot automation, and computer technology has led to the generation of terabytes of data in screening laboratories. Despite the technology development toward HTS productivity, fewer efforts were devoted to HTS data integration and sharing. As a result, the huge amount of HTS data was rarely made available to the public. To fill this gap, the PubChem BioAssay database (https://www.ncbi.nlm.nih.gov/pcassay/) was set up in 2004 to provide open access to the screening results tested on chemicals and RNAi reagents. With more than 10 years’ development and contributions from the community, PubChem has now become the largest public repository for chemical structures and biological data, which provides an information platform to worldwide researchers supporting drug development, medicinal chemistry study, and chemical biology research. This work presents a review of the HTS data content in the PubChem BioAssay database and the progress of data deposition to stimulate knowledge discovery and data sharing. It also provides a description of the database’s data standard and basic utilities facilitating information access and use for new users.

Abstract 3238: Design, synthesis and evaluation of a novel series of BLM helicase inhibitors

Abstract Helicases are enzymes that unwind DNA or RNA. As a result, they are described as ‘guardians of the genome’ and play roles in key processes such as DNA replication, RNA transcription, translation, DNA repair and DNA recombination. Certain helicases have also been implicated in the DNA damage response (DDR) and with an increasing interest growing in these enzymes, more helicases are likely to be implicated in DNA damage repair as understanding grows. Targeting proteins involved in DDR has recently gained promise as a selective chemotherapeutic strategy due to the high level of genomic instability in cancer cells. One of these helicases, Bloom syndrome protein (BLM), is particularly implicated in homologous recombination (HR), a key pathway in the repair of double strand breaks. The role of BLM in DNA repair and its complex interactions with key proteins in these networks suggest it may be a good chemotherapeutic target. The first BLM helicase inhibitor to be characterised was ML216. However, ML216 does not seem to be highly specific to BLM as it also inhibits DNA unwinding of the closely related WRN helicase. Furthermore, the poor solubility and permeability of this series which would prevent reliable in-vitro data from being generated. Potent and selective inhibitors targeting BLM helicase can increase understanding of its function in human cells and role and relevance in DNA damage repair. Furthermore, the tool can also be used in synthetic lethality screens to assess the potential of BLM inhibition as a chemotherapeutic option. As a result, the discovery of a novel series of potent and selective BLM inhibitors with improved physiochemical properties were sought. In this work, 672 active hits of the NIH Molecular Libraries Small Molecule Repository (MLSMR) qHTS screen on BLM helicase, which have been deposited in the PubChem BioAssay database [AID2386], were analysed and 10 series were selected for in-house testing on a BLM helicase unwinding assay. This led to the medicinal chemistry investigation of a single series that is relatively potent compared to inhibitors of other human helicases. Efforts have been undertaken to establish primary SAR and to improve the activity of the compound to <0.1 µM as well as further improve the physiochemical properties of this series. This series has shown positive binding data using microscale thermophoresis (MST). Very few inhibitors of human helicases have used biophysical techniques to validate binding and the use of MST for this purpose has not been reported. Further biological efforts are underway to validate the series through in vitro SCE assays and co-crystallisation methods. Citation Format: Yusuf Ali, Xiangrong Chen, Simon Ward, Frances Pearl. Design, synthesis and evaluation of a novel series of BLM helicase inhibitors [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 3238. doi:10.1158/1538-7445.AM2017-3238

Discovery and evaluation of inhibitors to the immunosuppressive enzyme indoleamine 2,3-dioxygenase 1 (IDO1): Probing the active site-inhibitor interactions

High expression of the immunosuppressive enzyme, indoleamine 2,3-dioxygenase 1 (IDO1) for a broad range of malignancies is associated with poor patient prognosis, and the enzyme is a validated target for cancer intervention. To identify novel IDO1 inhibitors suitable for drug development, 1597 compounds in the National Cancer Institute Diversity Set III library were tested for inhibitory activity against recombinant human IDO1. We retrieved 35 hits that inhibited IDO1 activity >50% at 20 μM. Five structural filters and the PubChem Bioassay database were used to guide the selection of five inhibitors with IC50 between 3 and 12 μM for subsequent experimental evaluation. A pyrimidinone scaffold emerged as being the most promising. It showed excellent cell penetration, negligible cytotoxicity and passed four out of the five structural filters applied. To evaluate the importance of Ser167 and Cys129 residues in the IDO1 active site for inhibitor binding, the entire NCI library was subsequently screened against alanine-replacement mutant enzymes of these two residues. The results established that Ser167 but not Cys129 is important for inhibitory activity of a broad range of IDO1 inhibitors. Structure-activity-relationship studies proposed substituents interacting with Ser167 on four investigated IDO1 inhibitors. Three of these four Ser167 interactions associated with an increased IDO1 inhibition and were correctly predicted by molecular docking supporting Ser167 as an important mediator of potency for IDO1 inhibitors.