Use of Solvent Mapping for Characterizing the Binding Site and for Predicting the Inhibition of the Human Ether-á-Go-Go-Related K+ Channel.

Molecular dynamics was used to optimize the droperidol-hERG complex obtained from docking. To accommodate the inhibitor, residues T623, S624, V625, G648, Y652, and F656 did not move significantly during the simulation, while F627 moved significantly. Binding sites in cryo-EM structures and in structures obtained from molecular dynamics simulations were characterized using solvent mapping and Atlas ligands, which were negative images of the binding site, were generated. Atlas ligands were found to be useful for identifying human ether-á-go-go-related potassium channel (hERG) inhibitors by aligning compounds to them or by guiding the docking of compounds in the binding site. A molecular dynamics optimized structure of hERG led to improved predictions using either compound alignment to the Atlas ligand or docking. The structure was also found to be suitable to define a strategy for lowering inhibition based on the proposed binding mode of compounds in the channel.

Cardiovascular and renal safety of metformin in patients with diabetes and moderate or severe chronic kidney disease: Observations from the EXSCEL and SAVOR-TIMI 53 cardiovascular outcomes trials.

AIM: To provide evidence on the cardiovascular and renal safety of metformin in chronic kidney disease (CKD) stages 3 to 4. MATERIALS AND METHODS: This post hoc analysis compared participants with an estimated glomerular filtration rate (eGFR) of 15 to 59 mL/min/1.73m2 in the Exenatide Study of Cardiovascular Event Lowering (EXSCEL) and the Saxagliptin and Cardiovascular Outcomes in Patients With Type 2 Diabetes Mellitus (SAVOR-TIMI 53) trials taking metformin, with those not exposed to metformin during these trials, using a propensity-matching approach. Adjusted Cox proportional hazards models were used to assess risk of major adverse cardiovascular events (MACE) and all-cause mortality (ACM). Metformin effect on eGFR slope was calculated using a mixed-model repeated measures analysis, and the number of lactic acidosis events was tabulated. RESULTS: No strong trend for lower metformin doses with lower eGFR values was observed in either the EXSCEL or SAVOR-TIMI 53 trials. In the 1745 metformin-using participants matched to non-metformin users, metformin had neutral effects on MACE (hazard ratio [HR] 0.91, 95% confidence interval [CI] 0.76-1.08; P = 0.28) and ACM (HR 0.86, 95% CI 0.70-1.07; P = 0.18), with no interaction by CKD stage, or with use of exenatide or saxagliptin. An improvement in eGFR slope was observed with metformin in the CKD stage 3B cohort in SAVOR-TIMI 53, but not in other groups. CONCLUSIONS: This analysis of participants with CKD stages 3 to 4 from two cardiovascular outcomes trials supports the cardiorenal safety of metformin, but does not suggest a consistent benefit on MACE, ACM, or eGFR slope across this population.

Efficacy assessment of ticagrelor versus clopidogrel in Chinese patients with acute coronary syndrome undergoing percutaneous coronary intervention by data mining and machine-learning decision tree approaches.

WHAT IS KNOWN AND OBJECTIVE: Although ticagrelor has been well-known to improve clinical outcomes in patients undergoing percutaneous coronary intervention (PCI), and its effectiveness and safety have not been well evaluated in Chinese patients. This study aimed to evaluate the effectiveness and safety of ticagrelor in Chinese patients. In order to find potential effect modifiers on the drug effects, a decision tree method was performed to detect interactions between treatment and patient characteristics in an automatic and systematic manner. METHODS: This retrospective study included acute coronary syndrome (ACS) patients who underwent PCI and received either ticagrelor (N = 250) or clopidogrel (N = 291) while hospitalized between August 2014 and August 2015. After propensity score matching, Kaplan-Meier analysis was used to study the event-free survival against major adverse cardiovascular events (MACE, primary efficacy outcome, defined as the composite of cardiac death, non-fatal myocardial infarction [MI], stroke, restenosis and target vessel revascularization [TVR]), re-hospitalization, the need for urgent re-PCI (secondary efficacy outcome) and bleeding events (safety outcome) within 12 months of the PCI date. To search for effect modifiers of the two antiplatelet therapies, a machine-learning decision tree algorithm was conducted to predict re-hospitalization status. RESULTS: After propensity score matching (N = 442), ticagrelor and clopidogrel had no significant difference in MACE, re-hospitalization and bleeding. The decision tree analysis showed that the number of diseased vessels modulated the effect of ticagrelor and clopidogrel on re-hospitalization rates. In single-vessel disease (SVD) patients, ticagrelor was associated with lower hazards than clopidogrel for all efficacy outcomes: MACE (HR = 0.190, 95% CI: 0.042-0.866), re-hospitalization (HR = 0.296, 95% CI: 0.108-0.808), urgent re-PCI (HR = 0.249, 95% CI: 0.069-0.895), bleeding (HR = 1.006, 95% CI: 0.063-16.129). However, in multi-vessel disease (MVD) patients, the two treatments did not show significant difference. WHAT IS NEW AND CONCLUSION: In the general patient population, there was no significant difference between ticagrelor and clopidogrel on the hazard of MACE. However, ticagrelor achieved a better effectiveness than clopidogrel in patients with SVD. This pilot study provides scientific basis to call for a large-scale prospective study in this population.

Analysis of substance use and its outcomes by machine learning I. Childhood evaluation of liability to substance use disorder.

BACKGROUND: Substance use disorder (SUD) exacts enormous societal costs in the United States, and it is important to detect high-risk youths for prevention. Machine learning (ML) is the method to find patterns and make prediction from data. We hypothesized that ML identifies the health, psychological, psychiatric, and contextual features to predict SUD, and the identified features predict high-risk individuals to develop SUD. METHOD: Male (N = 494) and female (N = 206) participants and their informant parents were administered a battery of questionnaires across five waves of assessment conducted at 10-12, 12-14, 16, 19, and 22 years of age. Characteristics most strongly associated with SUD were identified using the random forest (RF)algorithm from approximately 1000 variables measured at each assessment. Next, the complement of features was validated, and the best models were selected for predicting SUD using seven ML algorithms. Lastly, area under the receiver operating characteristic curve (AUROC) evaluated accuracy of detecting individuals who develop SUD+/- up to thirty years of age. RESULTS: Approximately thirty variables strongly predict SUD. The predictors shift from psychological dysregulation and poor health behavior in late childhood to non-normative socialization in mid to late adolescence. In 10-12-year-old youths, the features predict SUD+/- with 74% accuracy, increasing to 86% at 22 years of age. The RF algorithm optimally detects individuals between 10-22 years of age who develop SUD compared to other ML algorithms. CONCLUSION: These findings inform the items required for inclusion in instruments to accurately identify high risk youths and young adults requiring SUD prevention.

Analysis of substance use and its outcomes by machine learning: II. Derivation and prediction of the trajectory of substance use severity.

BACKGROUND: This longitudinal study explored the utility of machine learning (ML) methodology in predicting the trajectory of severity of substance use from childhood to thirty years of age using a set of psychological and health characteristics. DESIGN: Boys (N = 494) and girls (N = 206) were recruited using a high-risk paradigm at 10-12 years of age and followed up at 12-14, 16, 19, 22, 25 and 30 years of age. MEASUREMENTS: At each visit, the subjects were administered a comprehensive battery to measure psychological makeup, health status, substance use and psychiatric disorder, and their overall harmfulness of substance consumption was quantified according to the multidimensional criteria (physical, dependence, and social) developed by Nutt et al. (2007). Next, high- and low- substance use severity trajectories were derived differentially associated with probability of segueing to substance use disorder (SUD). ML methodology was employed to predict trajectory membership. FINDINGS: The high-severity trajectory group had a higher probability of leading to SUD than the low-severity trajectory (89.0% vs 32.4%; odds ratio = 16.88, p < 0.0001). Thirty psychological and health status items at each of the six visits predict membership in the high- or low-severity trajectory, with 71% accuracy at 10-12 years of age, increasing to 93% at 22 years of age. CONCLUSION: These findings demonstrate the applicability of the machine learning methodology for detecting membership in a substance use trajectory with high probability of culminating in SUD, potentially informing primary and secondary prevention.

Prediction of Orthosteric and Allosteric Regulations on Cannabinoid Receptors Using Supervised Machine Learning Classifiers.

Designing highly selective compounds to protein subtypes and developing allosteric modulators targeting them are critical considerations to both drug discovery and mechanism studies for cannabinoid receptors. It is challenging but in demand to have classifiers to identify active ligands from inactive or random compounds and distinguish allosteric modulators from orthosteric ligands. In this study, supervised machine learning classifiers were built for two subtypes of cannabinoid receptors, CB1 and CB2. Three types of features, including molecular descriptors, MACCS fingerprints, and ECFP6 fingerprints, were calculated to evaluate the compound sets from diverse aspects. Deep neural networks, as well as conventional machine learning algorithms including support vector machine, naïve Bayes, logistic regression, and ensemble learning, were applied. Their performances on the classification with different types of features were compared and discussed. According to the receiver operating characteristic curves and the calculated metrics, the advantages and drawbacks of each algorithm were investigated. The feature ranking was followed to help extract useful knowledge about critical molecular properties, substructural keys, and circular fingerprints. The extracted features will then facilitate the research on cannabinoid receptors by providing guidance on preferred properties for compound modification and novel scaffold design. Besides using conventional molecular docking studies for compound virtual screening, machine-learning-based decision-making models provide alternative options. This study can be of value to the application of machine learning in the area of drug discovery and compound development.

DAKB-GPCRs: An Integrated Computational Platform for Drug Abuse Related GPCRs.

Drug abuse (DA) or drug addiction is a complicated brain disorder which is commonly considered as neurobiological impairments caused by both genetic factors and environmental effects. Among DA-related targets, G protein-coupled receptors (GPCRs) play an important role in DA therapy. However, only 52 GPCRs have been published with crystal structures in the recent two decades. In the effort to overcome the limitations of crystal structure and conformational diversity of GPCRs, we built homology models and performed conformational searches by molecular dynamics (MD) simulation. To accelerate and facilitate the drug abuse research, we construct a DA-related GPCR-specific chemogenomics knowledgebase (KB) (DAKB-GPCRs) for its research that can be implemented with our established and novel chemogenomics tools as well as algorithms for data analysis and visualization. Our established TargetHunter and HTDocking tools, as well as our novel tools that include target classification and Spider Plot, are compiled into the platform. Our DAKB-GPCRs provides the following results for a query compound: (1) blood-brain barrier (BBB) plot via our BBB predictor, (2) docking scores via HTDocking, (3) similarity score via TargetHunter, (4) target classification via machine learning methods that utilize both docking scores and similarity scores, and (5) a drug-target interaction network via Spider Plot.

Computational systems pharmacology analysis of cannabidiol: a combination of chemogenomics-knowledgebase network analysis and integrated in silico modeling and simulation.

With treatment benefits in both the central nervous system and the peripheral system, the medical use of cannabidiol (CBD) has gained increasing popularity. Given that the therapeutic mechanisms of CBD are still vague, the systematic identification of its potential targets, signaling pathways, and their associations with corresponding diseases is of great interest for researchers. In the present work, chemogenomics-knowledgebase systems pharmacology analysis was applied for systematic network studies to generate CBD-target, target-pathway, and target-disease networks by combining both the results from the in silico analysis and the reported experimental validations. Based on the network analysis, three human neuro-related rhodopsin-like GPCRs, i.e., 5-hydroxytryptamine receptor 1 A (5HT1A), delta-type opioid receptor (OPRD) and G protein-coupled receptor 55 (GPR55), were selected for close evaluation. Integrated computational methodologies, including homology modeling, molecular docking, and molecular dynamics simulation, were used to evaluate the protein-CBD binding modes. A CBD-preferred pocket consisting of a hydrophobic cavity and backbone hinges was proposed and tested for CBD-class A GPCR binding. Finally, the neurophysiological effects of CBD were illustrated at the molecular level, and dopamine receptor 3 (DRD3) was further predicted to be an active target for CBD.

Correction to: Deep Learning for Drug Design: an Artificial Intelligence Paradigm for Drug Discovery in the Big Data Era.

The name of the corresponding author should be 'Xiang-Qun Xie', rather than 'Xiang-Qun Sean Xie'.

Deep Learning for Drug Design: an Artificial Intelligence Paradigm for Drug Discovery in the Big Data Era.

Over the last decade, deep learning (DL) methods have been extremely successful and widely used to develop artificial intelligence (AI) in almost every domain, especially after it achieved its proud record on computational Go. Compared to traditional machine learning (ML) algorithms, DL methods still have a long way to go to achieve recognition in small molecular drug discovery and development. And there is still lots of work to do for the popularization and application of DL for research purpose, e.g., for small molecule drug research and development. In this review, we mainly discussed several most powerful and mainstream architectures, including the convolutional neural network (CNN), recurrent neural network (RNN), and deep auto-encoder networks (DAENs), for supervised learning and nonsupervised learning; summarized most of the representative applications in small molecule drug design; and briefly introduced how DL methods were used in those applications. The discussion for the pros and cons of DL methods as well as the main challenges we need to tackle were also emphasized.

In Silico Prediction of Volume of Distribution in Humans. Extensive Data Set and the Exploration of Linear and Nonlinear Methods Coupled with Molecular Interaction Fields Descriptors.

We present three in silico volume of distribution at steady state (VDss) models generated on a training set comprising 1096 compounds, which goes well beyond the conventional drug space delineated by the Rule of 5 or similar approaches. We have performed a careful selection of descriptors and kept a homogeneous Molecular Interaction Field-based descriptor set and linear (Partial Least Squares, PLS) and nonlinear (Random Forest, RF) models. We have tested the models, which we deem orthogonal in nature due to different descriptors and statistical approaches, with good results. In particular we tested the RF model, via a leave-class-out approach and by using a set of 34 additional compounds not used for training. We report comparable results against in vivo scaling approaches with geometric mean-fold error at or below 2 (for a set of 60 compounds with animal data available) and discuss the predictive performance based on the ionization states of the compounds. Lastly, we report the findings using a two-tier approach (classification followed by regression) based on VDss ranges, in an attempt to improve the prediction of compounds with very high VDss. We would recommend, overall, the RF model, with 33 descriptors, as the primary choice for VDss prediction in humans.

In silico prediction of hERG inhibition.

The voltage-gated potassium channel encoded by hERG carries a delayed rectifying potassium current (IKr) underlying repolarization of the cardiac action potential. Pharmacological blockade of the hERG channel results in slowed repolarization and therefore prolongation of action potential duration and an increase in the QT interval as measured on an electrocardiogram. Those are possible to cause sudden death, leading to the withdrawals of many drugs, which is the reason for hERG screening. Computational in silico prediction models provide a rapid, economic way to screen compounds during early drug discovery. In this review, hERG prediction models are classified as 2D and 3D quantitative structure-activity relationship models, pharmacophore models, classification models, and structure based models (using homology models of hERG).