A machine learning-based quantitative model (LogBB_Pred) to predict the blood-brain barrier permeability (logBB value) of drug compounds.

MOTIVATION: Efficient assessment of the blood-brain barrier (BBB) penetration ability of a drug compound is one of the major hurdles in central nervous system drug discovery since experimental methods are costly and time-consuming. To advance and elevate the success rate of neurotherapeutic drug discovery, it is essential to develop an accurate computational quantitative model to determine the absolute logBB value (a logarithmic ratio of the concentration of a drug in the brain to its concentration in the blood) of a drug candidate. RESULTS: Here, we developed a quantitative model (LogBB_Pred) capable of predicting a logBB value of a query compound. The model achieved an R2 of 0.61 on an independent test dataset and outperformed other publicly available quantitative models. When compared with the available qualitative (classification) models that only classified whether a compound is BBB-permeable or not, our model achieved the same accuracy (0.85) with the best qualitative model and far-outperformed other qualitative models (accuracies between 0.64 and 0.70). For further evaluation, our model, quantitative models, and the qualitative models were evaluated on a real-world central nervous system drug screening library. Our model showed an accuracy of 0.97 while the other models showed an accuracy in the range of 0.29-0.83. Consequently, our model can accurately classify BBB-permeable compounds as well as predict the absolute logBB values of drug candidates. AVAILABILITY AND IMPLEMENTATION: Web server is freely available on the web at http://ssbio.cau.ac.kr/software/logbb_pred/. The data used in this study are available to download at http://ssbio.cau.ac.kr/software/logbb_pred/dataset.zip.

A multi-layered network model identifies Akt1 as a common modulator of neurodegeneration.

The accumulation of misfolded and aggregated proteins is a hallmark of neurodegenerative proteinopathies. Although multiple genetic loci have been associated with specific neurodegenerative diseases (NDs), molecular mechanisms that may have a broader relevance for most or all proteinopathies remain poorly resolved. In this study, we developed a multi-layered network expansion (MLnet) model to predict protein modifiers that are common to a group of diseases and, therefore, may have broader pathophysiological relevance for that group. When applied to the four NDs Alzheimer's disease (AD), Huntington's disease, and spinocerebellar ataxia types 1 and 3, we predicted multiple members of the insulin pathway, including PDK1, Akt1, InR, and sgg (GSK-3ß), as common modifiers. We validated these modifiers with the help of four Drosophila ND models. Further evaluation of Akt1 in human cell-based ND models revealed that activation of Akt1 signaling by the small molecule SC79 increased cell viability in all models. Moreover, treatment of AD model mice with SC79 enhanced their long-term memory and ameliorated dysregulated anxiety levels, which are commonly affected in AD patients. These findings validate MLnet as a valuable tool to uncover molecular pathways and proteins involved in the pathophysiology of entire disease groups and identify potential therapeutic targets that have relevance across disease boundaries. MLnet can be used for any group of diseases and is available as a web tool at http://ssbio.cau.ac.kr/software/mlnet.

PredAOT: a computational framework for prediction of acute oral toxicity based on multiple random forest models.

BACKGROUND: Acute oral toxicity of drug candidates can lead to drug development failure; thus, predicting the acute oral toxicity of small compounds is important for successful drug development. However, evaluation of the acute oral toxicity of small compounds considered in the early stages of drug discovery is limited because of cost and time. Here, we developed a computational framework, PredAOT, that predicts the acute oral toxicity of small compounds in mice and rats. METHODS: PredAOT is based on multiple random forest models for the accurate prediction of acute oral toxicity. A total of 6226 and 6238 compounds evaluated in mice and rats, respectively, were used to train the models. RESULTS: PredAOT has the advantage of predicting acute oral toxicity in mice and rats simultaneously, and its prediction performance is similar to or better than that of existing tools. CONCLUSION: PredAOT will be a useful tool for the quick and accurate prediction of the acute oral toxicity of small compounds in mice and rats during drug development.

PredMS: a random forest model for predicting metabolic stability of drug candidates in human liver microsomes.

MOTIVATION: Poor metabolic stability leads to drug development failure. Therefore, it is essential to evaluate the metabolic stability of small compounds for successful drug discovery and development. However, evaluating metabolic stability in vitro and in vivo is expensive, time-consuming and laborious. In addition, only a few free software programs are available for metabolic stability data and prediction. Therefore, in this study, we aimed to develop a prediction model that predicts the metabolic stability of small compounds. RESULTS: We developed a computational model, PredMS, which predicts the metabolic stability of small compounds as stable or unstable in human liver microsomes. PredMS is based on a random forest model using an in-house database of metabolic stability data of 1917 compounds. To validate the prediction performance of PredMS, we generated external test data of 61 compounds. PredMS achieved an accuracy of 0.74, Matthew's correlation coefficient of 0.48, sensitivity of 0.70, specificity of 0.86, positive predictive value of 0.94 and negative predictive value of 0.46 on the external test dataset. PredMS will be a useful tool to predict the metabolic stability of small compounds in the early stages of drug discovery and development. AVAILABILITY AND IMPLEMENTATION: The source code for PredMS is available at https://bitbucket.org/krictai/predms, and the PredMS web server is available at https://predms.netlify.app. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Development of a Particle Filter-Based Path Tracking Algorithm of Autonomous Trucks with a Single Steering and Driving Module Using a Monocular Camera.

Recently, in various fields, research into the path tracking of autonomous vehicles and automated guided vehicles has been conducted to improve worker safety, convenience, and work efficiency. For path tracking of various systems applied to autonomous driving technology, it is necessary to recognize the surrounding environment, determine technology accordingly, and develop control methods. Various sensors and artificial-intelligence-based perception methods have limitations in that they must learn a large amount of data. Therefore, a particle-filter-based path tracking algorithm using a monocular camera was used for the recognition of target RGB. The path tracking errors were calculated and a linear-quadratic-regulator-based desired steering angle were derived. The autonomous trucks were steered and driven using a pulse-width-modulation-based steering and driving motor. Based on an autonomous truck with a single steering and driving module, it was verified that the path tracking could be used in three evaluation scenarios. To compare the LQR-based path tracking control performance proposed in this paper, an elliptical path tracking scenario using a conventional sliding mode control with robust control performance was performed. The results show that the RMS of the lateral preview error of the SMC was approximately 18% larger than that of the LQR-based method.

LightBBB: computational prediction model of blood-brain-barrier penetration based on LightGBM.

MOTIVATION: Identification of blood-brain barrier (BBB) permeability of a compound is a major challenge in neurotherapeutic drug discovery. Conventional approaches for BBB permeability measurement are expensive, time-consuming and labor-intensive. BBB permeability is associated with diverse chemical properties of compounds. However, BBB permeability prediction models have been developed using small datasets and limited features, which are usually not practical due to their low coverage of chemical diversity of compounds. Aim of this study is to develop a BBB permeability prediction model using a large dataset for practical applications. This model can be used for facilitated compound screening in the early stage of brain drug discovery. RESULTS: A dataset of 7162 compounds with BBB permeability (5453 BBB+ and 1709 BBB-) was compiled from the literature, where BBB+ and BBB- denote BBB-permeable and non-permeable compounds, respectively. We trained a machine learning model based on Light Gradient Boosting Machine (LightGBM) algorithm and achieved an overall accuracy of 89%, an area under the curve (AUC) of 0.93, specificity of 0.77 and sensitivity of 0.93, when 10-fold cross-validation was performed. The model was further evaluated using 74 central nerve system compounds (39 BBB+ and 35 BBB-) obtained from the literature and showed an accuracy of 90%, sensitivity of 0.85 and specificity of 0.94. Our model outperforms over existing BBB permeability prediction models. AVAILABILITYAND IMPLEMENTATION: The prediction server is available at http://ssbio.cau.ac.kr/software/bbb.

Development of a Sliding-Mode-Control-Based Path-Tracking Algorithm with Model-Free Adaptive Feedback Action for Autonomous Vehicles.

This paper presents a sliding mode control (SMC)-based path-tracking algorithm for autonomous vehicles by considering model-free adaptive feedback actions. In autonomous vehicles, safe path tracking requires adaptive and robust control algorithms because driving environment and vehicle conditions vary in real time. In this study, the SMC was adopted as a robust control method to adjust the switching gain, taking into account the sliding surface and unknown uncertainty to make the control error zero. The sliding surface can be designed mathematically, but it is difficult to express the unknown uncertainty mathematically. Information of priori bounded uncertainties is needed to obtain closed-loop stability of the control system, and the unknown uncertainty can vary with changes in internal and external factors. In the literature, ongoing efforts have been made to overcome the limitation of losing control stability due to unknown uncertainty. This study proposes an integrated method of adaptive feedback control (AFC) and SMC that can adjust a bounded uncertainty. Some illustrative and representative examples, such as autonomous driving scenarios, are also provided to show the main properties of the designed integrated controller. The examples show superior control performance, and it is expected that the integrated controller could be widely used for the path-tracking algorithms of autonomous vehicles.

Identification and New Indication of Melanin-Concentrating Hormone Receptor 1 (MCHR1) Antagonist Derived from Machine Learning and Transcriptome-Based Drug Repositioning Approaches.

Melanin-concentrating hormone receptor 1 (MCHR1) has been a target for appetite suppressants, which are helpful in treating obesity. However, it is challenging to develop an MCHR1 antagonist because its binding site is similar to that of the human Ether-à-go-go-Related Gene (hERG) channel, whose inhibition may cause cardiotoxicity. Most drugs developed as MCHR1 antagonists have failed in clinical development due to cardiotoxicity caused by hERG inhibition. Machine learning-based prediction models can overcome these difficulties and provide new opportunities for drug discovery. In this study, we identified KRX-104130 with potent MCHR1 antagonistic activity and no cardiotoxicity through virtual screening using two MCHR1 binding affinity prediction models and an hERG-induced cardiotoxicity prediction model. In addition, we explored other possibilities for expanding the new indications for KRX-104130 using a transcriptome-based drug repositioning approach. KRX-104130 increased the expression of low-density lipoprotein receptor (LDLR), which induced cholesterol reduction in the gene expression analysis. This was confirmed by comparison with gene expression in a nonalcoholic steatohepatitis (NASH) patient group. In a NASH mouse model, the administration of KRX-104130 showed a protective effect by reducing hepatic lipid accumulation, liver injury, and histopathological changes, indicating a promising prospect for the therapeutic effect of NASH as a new indication for MCHR1 antagonists.

DeepHIT: a deep learning framework for prediction of hERG-induced cardiotoxicity.

MOTIVATION: Blockade of the human ether-à-go-go-related gene (hERG) channel by small compounds causes a prolonged QT interval that can lead to severe cardiotoxicity and is a major cause of the many failures in drug development. Thus, evaluating the hERG-blocking activity of small compounds is important for successful drug development. To this end, various computational prediction tools have been developed, but their prediction performances in terms of sensitivity and negative predictive value (NPV) need to be improved to reduce false negative predictions. RESULTS: We propose a computational framework, DeepHIT, which predicts hERG blockers and non-blockers for input compounds. For the development of DeepHIT, we generated a large-scale gold-standard dataset, which includes 6632 hERG blockers and 7808 hERG non-blockers. DeepHIT is designed to contain three deep learning models to improve sensitivity and NPV, which, in turn, produce fewer false negative predictions. DeepHIT outperforms currently available tools in terms of accuracy (0.773), MCC (0.476), sensitivity (0.833) and NPV (0.643) on an external test dataset. We also developed an in silico chemical transformation module that generates virtual compounds from a seed compound, based on the known chemical transformation patterns. As a proof-of-concept study, we identified novel urotensin II receptor (UT) antagonists without hERG-blocking activity derived from a seed compound of a previously reported UT antagonist (KR-36676) with a strong hERG-blocking activity. In summary, DeepHIT will serve as a useful tool to predict hERG-induced cardiotoxicity of small compounds in the early stages of drug discovery and development. AVAILABILITY AND IMPLEMENTATION: https://bitbucket.org/krictai/deephit and https://bitbucket.org/krictai/chemtrans. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Development of a FOXM1-DBD Binding Assay for High-Throughput Screening Using TR-FRET Assay.

Electrophoretic mobility shift assay (EMSA) technology has been widely employed for the analysis of transcription factors such as Forkhead box protein M1 (FOXM1). However, the application of high-throughput screening (HTS) in performing, such analyses are limited as it uses time consuming electrophoresis procedure and radioisotopes. In this study, we developed a FOXM1-DNA binding domain (DBD) binding assay based on time-resolved fluorescence energy transfer (TR-FRET) that enables HTS for the inhibitors of FOXM1-DNA interaction. This assay was robust, highly reproducible and could be easily miniaturized into 384-well plate format. The signal-to-background (S/B) ratio and Z' factor were calculated as 7.46 and 0.74, respectively, via a series of optimization of the assay conditions. A pilot library screening of 1019 natural compounds was performed using the FOXM1-DBD binding assay. Five hit compounds, namely, AC1LXM, BRN5, gangaleoidin, leoidin, and roemerine were identified as the inhibitors of FOXM1. In a cell viability assay, it was demonstrated that cell proliferation of FOXM1 overexpressed cell lines was suppressed in cell lines such as MDA-MB-231 and MCF-7 by five hit compounds. These results indicate that developed FOXM1-DBD binding assay can be applied to highly efficiency HTS of compound libraries.

Computational determination of hERG-related cardiotoxicity of drug candidates.

BACKGROUND: Drug candidates often cause an unwanted blockage of the potassium ion channel of the human ether-a-go-go-related gene (hERG). The blockage leads to long QT syndrome (LQTS), which is a severe life-threatening cardiac side effect. Therefore, a virtual screening method to predict drug-induced hERG-related cardiotoxicity could facilitate drug discovery by filtering out toxic drug candidates. RESULT: In this study, we generated a reliable hERG-related cardiotoxicity dataset composed of 2130 compounds, which were carried out under constant conditions. Based on our dataset, we developed a computational hERG-related cardiotoxicity prediction model. The neural network model achieved an area under the receiver operating characteristic curve (AUC) of 0.764, with an accuracy of 90.1%, a Matthews correlation coefficient (MCC) of 0.368, a sensitivity of 0.321, and a specificity of 0.967, when ten-fold cross-validation was performed. The model was further evaluated using ten drug compounds tested on guinea pigs and showed an accuracy of 80.0%, an MCC of 0.655, a sensitivity of 0.600, and a specificity of 1.000, which were better than the performances of existing hERG-toxicity prediction models. CONCLUSION: The neural network model can predict hERG-related cardiotoxicity of chemical compounds with a high accuracy. Therefore, the model can be applied to virtual high-throughput screening for drug candidates that do not cause cardiotoxicity. The prediction tool is available as a web-tool at http://ssbio.cau.ac.kr/CardPred .

Synthesis and SAR of 5-aryl-furan-2-carboxamide derivatives as potent urotensin-II receptor antagonists.

The synthesis and biological evaluation as potential urotensin-II receptor antagonists of a series of 5-arylfuran-2-carboxamide derivatives 1, bearing a 4-(3-chloro-4-(piperidin-4-yloxy)benzyl)piperazin-1-yl group, are described. The results of a systematic SAR investigation of furan-2-carboxamides with C-5 aryl groups possessing a variety of aryl ring substituents led to identification of the 3,4-difluorophenyl analog 1y as a highly potent UT antagonist with an IC50 value of 6 nM. In addition, this substance was found to display high metabolic stability, and low hERG inhibition and cytotoxicity, and to have an acceptable PK profile.

Optimization study towards more potent thiazolidine-2,4-dione IKK-ß modulator: Synthesis, biological evaluation and in silico docking simulation.

Inhibition of IKK-ß (inhibitor of nuclear factor kappa-B kinase subunit beta) has been broadly documentedas a promising approach for treatment of acute and chronic inflammatory diseases, cancer, and autoimmune diseases. Recently, we have identified a novel class of thiazolidine-2,4-diones as structurally novel modulators for IKK-ß. Herein, we report a hit optimization study via analog synthesis strategy aiming to acquire more potent derivative(s), probe the structure activity relationship (SAR), and get reasonable explanations for the elicited IKK-ß inhibitory activities though an in silico docking simulation study. Accordingly, a new series of eighteen thiazolidine-2,4-dione derivatives was rationally designed, synthesized, identified with different spectroscopic techniques and biologically evaluated as noteworthy IKK-ß potential modulators. Successfully, new IKK-ß potent modulators were obtained, including the most potent analog up-to-date 7m with IC50 value of 260 nM. A detailed structure activity relationship (SAR) was discussed and a mechanistic study for 7m was carried out indicating its irreversible inhibition mode with IKK-ß (Kinact value = 0.01 (min-1). Furthermore, the conducted in silico simulation study provided new insights for the binding modes of this novel class of modulators with IKK-ß.

Development of a High-Throughput Assay for Inhibitors of the Polo-Box Domain of Polo-Like Kinase 1 Based on Time-Resolved Fluorescence Energy Transfer.

Although enzyme-linked immunosorbent assay (ELISA) technology has been widely accepted for binding assays against the polo-box domain (PBD) of polo-like kinase-1 (Plk1), these assays have a limitation-related heterogeneous procedure, such as multiple incubations and washing steps to apply high-throughput screenings (HTSs). In the present study, a Plk1-PBD binding assay based on time-resolved fluorescence energy transfer (TR-FRET) was developed for HTS of PBD-binding inhibitors. The TR-FRET-based Plk1-PBD binding assay is sensitive and robust and can be miniaturized into the 384-well plate-based format. Compared with the ELISA-based Plk1-PBD binding assay (Z' factor, 0.53; signal-to-background ratio, 4.19), the TR-FRET-based Plk1-PBD binding assay improved the Z' factor (0.72) and signal-to-background ratio (8.16). Using TR-FRET based Plk1-PBD binding assay, pilot library screening of 1019 natural compounds was conducted and five hit compounds such as haematoxylin, verbascoside, menadione, lithospermic acid and (1,3-dioxolo[4,5-g]isoquinolinium 5,6,7,8-tetrahydro-4-methoxy-6,6-dimethyl-5-[2-oxo-2-(2-pyridinyl)ethyl]-iodide) (DITMD) were identified as Plk1-PBD inhibitor. In a functional assay to validate the hit compounds, five hit compounds exhibited suppression of HeLa cells proliferation. These results suggest that TR-FRET-based Plk1-PBD binding assay can be applied for an efficient and less time-consuming HTS of compound libraries.

A novel role of G protein-coupled receptor kinase 5 in urotensin II-stimulated cellular hypertrophy in H9c2UT cells.

Urotensin II (UII) is a neural hormone that induces cardiac hypertrophy and may be involved in the pathogenesis of cardiac remodeling and heart failure. Hypertrophy has been linked to histone deacetylase 5 (HDAC5) phosphorylation and nuclear factor κB (NF-κB) translocation, both of which are predominantly mediated by G protein-coupled receptor kinase 5 (GRK5). In the present study, we found that UII rapidly and strongly stimulated nuclear export of HDAC5 and nuclear import of NF-κB in H9c2 cells overexpressing the urotensin II receptor (H9c2UT). Hence, we hypothesized that GRK5 and its signaling pathway may play a role in UII-mediated cellular hypertrophy. H9c2UT cells were transduced with a GRK5 small hairpin RNA interference recombinant lentivirus, resulting in the down-regulation of GRK5. Under UII stimulation, reduced levels of GRK5 in H9c2UT cells led to suppression of UII-mediated HDAC5 phosphorylation and activation of the NF-κB signaling pathway. In contrast, UII-mediated activations of ERK1/2 and GSK3α/ß were not affected by down-regulation of GRK5. In a cellular hypertrophy assay, down-regulation of GRK5 significantly suppressed UII-mediated hypertrophy of H9c2UT cells. Furthermore, UII-mediated cellular hypertrophy was inhibited by amlexanox, a selective GRK5 inhibitor, in H9c2UT cells and neonatal cardiomyocytes. Our results suggest that GRK5 may be involved in a UII-mediated hypertrophic response via activation of NF-κB and HDAC5 at least in part by ERK1/2 and GSK3α/ß-independent pathways.

Benzo[b]thiophene-2-carboxamide derivatives as potent urotensin-II receptor antagonists.

Members of a series of benzo[b]thiophene-2-carboxamide derivatives, possessing an N-(1-(3-bromo-4-(piperidin-4-yloxy)benzyl)piperidin-4-yl) group, were synthesized and evaluated as urotensin-II receptor antagonists. The results show that these substances have potent UT binding affinities. Observations made in a systematic SAR investigation of the effects of a variety of substituents (R(1) and R(2)) at the 5- and 6-positions in the benzo[b]thiophene-2-carboxamide moiety on UT binding affinities led to identification of the 5-cyano analog 7f as a highly potent UT antagonist with an IC50 value of 25nM. Despite having a good metabolic stability, 7f is a potent inhibitor of CYP isozyme and displays an unsuitable PK profile.

A Dual Readout Assay Based on Fluorescence Polarization and Time-Resolved Fluorescence Resonance Energy Transfer to Screen for RSK1 Inhibitors.

A dual readout assay based on fluorescence polarization (FP) and time-resolved fluorescence resonance energy transfer (TR-FRET) exhibits many advantages over single assay technology in terms of screening quality and efficiency. In this study, we developed a dual readout assay combining FP and TR-FRET to identify ribosomal S6 kinase 1 (RSK1) inhibitors. This dual readout assay can monitor both FP and TR-FRET signals from a single RSK1 kinase reaction by using the immobilized metal affinity for phosphochemical (IMAP)-based assay. The Z' value and signal to background (S/B) ratio were 0.85 and 4.0 using FP, and 0.79 and 10.6 using TR-FRET, which led to performance of a pilot library screening against the drug repositioning set consisting of 2320 compounds with a reasonable reproducibility. From this screening, we identified 16 compounds showing greater than 50% inhibition against RSK1 for both FP and TR-FRET; 6 compounds with greater than 50% inhibition only for FP; and 4 compounds with greater than 50% inhibition only for TR-FRET. In a cell-based functional assay to validate the hit compounds, 10 compounds identified only in a single assay had little effect on the RSK-mediated phosphorylation of liver kinase B1, whereas 5 compounds showing greater than 80% inhibition for both FP and TR-FRET reduced the phosphorylation of liver kinase B1. These results demonstrate that the dual readout assay can be used to identify hit compounds by subsequently monitoring both FP and TR-FRET signals from one RSK1 reaction.

Synthesis and SAR of thieno[3,2-b]pyridinyl urea derivatives as urotensin-II receptor antagonists.

The preparation and SAR profile of thieno[3,2-b]pyridinyl urea derivatives as novel and potent urotensin-II receptor antagonists are described. An activity optimization study, probing the effects of substituents on thieno[3,2-b]pyridinyl core and benzyl group of the piperidinyl moiety, led to the identification of p-fluorobenzyl substituted thieno[3,2-b]pyridinyl urea 6n as a highly potent UT antagonist with an IC50 value of 13nM. Although 6n displays good metabolic stability and low hERG binding activity, it has an unacceptable oral bioavailability.

4-Substituted quinazoline derivatives as novel EphA2 receptor tyrosine kinase inhibitors.

Erythropoietin-producing hepatocellular receptor tyrosine kinase subtype A2 (EphA2) is an attractive therapeutic target for suppressing tumor progression. In our efforts to discover novel small molecules to inhibit EphA2, a class of compound based on 4-substituted quinazoline containing 7-(morpholin-2-ylmethoxy) group was identified as a novel hit by high throughput screening campaign. Structural modification of parent quinazoline scaffolds by introducing substituents on aniline displayed potent inhibitory activities toward EphA2.

AnoChem: Prediction of chemical structural abnormalities based on machine learning models.

De novo drug design aims to rationally discover novel and potent compounds while reducing experimental costs during the drug development stage. Despite the numerous generative models that have been developed, few successful cases of drug design utilizing generative models have been reported. One of the most common challenges is designing compounds that are not synthesizable or realistic. Therefore, methods capable of accurately assessing the chemical structures proposed by generative models for drug design are needed. In this study, we present AnoChem, a computational framework based on deep learning designed to assess the likelihood of a generated molecule being real. AnoChem achieves an area under the receiver operating characteristic curve score of 0.900 for distinguishing between real and generated molecules. We utilized AnoChem to evaluate and compare the performances of several generative models, using other metrics, namely SAscore and Fréschet ChemNet distance (FCD). AnoChem demonstrates a strong correlation with these metrics, validating its effectiveness as a reliable tool for assessing generative models. The source code for AnoChem is available at https://github.com/CSB-L/AnoChem.