Artificial intelligence and machine-learning approaches in structure and ligand-based discovery of drugs affecting central nervous system

CNS disorders are indications with a very high unmet medical needs, relatively smaller number of available drugs, and a subpar satisfaction level among patients and caregiver. Discovery of CNS drugs is extremely expensive affair with its own unique challenges leading to extremely high attrition rates and low efficiency. With explosion of data in information age, there is hardly any aspect of life that has not been touched by data driven technologies such as artificial intelligence (AI) and machine learning (ML). Drug discovery is no exception, emergence of big data via genomic, proteomic, biological, and chemical technologies has driven pharmaceutical giants to collaborate with AI oriented companies to revolutionise drug discovery, with the goal of increasing the efficiency of the process. In recent years many examples of innovative applications of AI and ML techniques in CNS drug discovery has been reported. Research on therapeutics for diseases such as schizophrenia, Alzheimer's and Parkinsonism has been provided with a new direction and thrust from these developments. AI and ML has been applied to both ligand-based and structure-based drug discovery and design of CNS therapeutics. In this review, we have summarised the general aspects of AI and ML from the perspective of drug discovery followed by a comprehensive coverage of the recent developments in the applications of AI/ML techniques in CNS drug discovery.

Artificial intelligence and machine learning-aided drug discovery in central nervous system diseases: State-of-the-arts and future directions

Neurological disorders significantly outnumber diseases in other therapeutic areas. However, developing drugs for central nervous system (CNS) disorders remains the most challenging area in drug discovery, accompanied with the long timelines and high attrition rates. With the rapid growth of biomedical data enabled by advanced experimental technologies, artificial intelligence (AI) and machine learning (ML) have emerged as an indispensable tool to draw meaningful insights and improve decision making in drug discovery. Thanks to the advancements in AI and ML algorithms, now the AI/ML-driven solutions have an unprecedented potential to accelerate the process of CNS drug discovery with better success rate. In this review, we comprehensively summarize AI/ML-powered pharmaceutical discovery efforts and their implementations in the CNS area. After introducing the AI/ML models as well as the conceptualization and data preparation, we outline the applications of AI/ML technologies to several key procedures in drug discovery, including target identification, compound screening, hit/lead generation and optimization, drug response and synergy prediction, de novo drug design, and drug repurposing. We review the current state-of-the-art of AI/ML-guided CNS drug discovery, focusing on blood-brain barrier permeability prediction and implementation into therapeutic discovery for neurological diseases. Finally, we discuss the major challenges and limitations of current approaches and possible future directions that may provide resolutions to these difficulties.

Bioactive Compounds from Plant-Based Functional Foods: A Promising Choice for the Prevention and Management of Hyperuricemia

Hyperuricemia is a common metabolic disease that is caused by high serum uric acid levels. It is considered to be closely associated with the development of many chronic diseases, such as obesity, hypertension, hyperlipemia, diabetes, and cardiovascular disorders. While pharmaceutical drugs have been shown to exhibit serious side effects, and bioactive compounds from plant-based functional foods have been demonstrated to be active in the treatment of hyperuricemia with only minimal side effects. Indeed, previous reports have revealed the significant impact of bioactive compounds from plant-based functional foods on hyperuricemia. This review focuses on plant-based functional foods that exhibit a hypouricemic function and discusses the different bioactive compounds and their pharmacological effects. More specifically, the bioactive compounds of plant-based functional foods are divided into six categories, namely flavonoids, phenolic acids, alkaloids, saponins, polysaccharides, and others. In addition, the mechanism by which these bioactive compounds exhibit a hypouricemic effect is summarized into three classes, namely the inhibition of uric acid production, improved renal uric acid elimination, and improved intestinal uric acid secretion. Overall, this current and comprehensive review examines the use of bioactive compounds from plant-based functional foods as natural remedies for the management of hyperuricemia.

Natural Products with Analgesic Effect from Herbs and Nutraceuticals Used in Traditional Chinese Medicines

BACKGROUND

Pain is one of the most common clinical symptoms . This review aims to describe research on herbs and their active ingredients in treating pain and provide a valuable reference for the development and utilization of analgesic traditional Chinese medicine (TCM).

MATERIAL AND METHODS

The literature search was performed from 1995 to October 2016, covering the relevant studies that concern the treatment of pain with TCM. Active ingredients extracted from TCM with analgesic activity are summarized and classified into six categories, including polysaccharides, saponins, alkaloids, flavonoids, terpenoids, and other constituents.

RESULTS

There are two pathways constituting the analgesic mechanisms of TCM: through the central nervous system and the peripheral nervous system. The former pathway includes increasing the content of endogenous analgesic substances like opiate peptide, cutting down the second messenger of neurotransmitter like nitric oxide (NO), reducing the content of prostaglandin E2 (PGE2) in brain tissues, blocking the central calcium channel, reducing excitatory amino acids in brain tissues, inhibiting their receptors and raising the content of the central 5-hydroxytryptamine (5-HT). The latter one usually involves the decrease in the secretion of peripheral algogenic substances, the induction of pain-sensitive substances, the accumulation of a local algogenic substance, the increase in the release of peripheral endogenous analgesia materials and the regulation of c-Fos gene (immediate early gene).

Concepts of Artificial Intelligence for Computer-Assisted Drug Discovery

Artificial intelligence (AI), and, in particular, deep learning as a subcategory of AI, provides opportunities for the discovery and development of innovative drugs. Various machine learning approaches have recently (re)emerged, some of which may be considered instances of domain-specific AI which have been successfully employed for drug discovery and design. This review provides a comprehensive portrayal of these machine learning techniques and of their applications in medicinal chemistry. After introducing the basic principles, alongside some application notes, of the various machine learning algorithms, the current state-of-the art of AI-assisted pharmaceutical discovery is discussed, including applications in structure- and ligand-based virtual screening, de novo drug design, physicochemical and pharmacokinetic property prediction, drug repurposing, and related aspects. Finally, several challenges and limitations of the current methods are summarized, with a view to potential future directions for AI-assisted drug discovery and design.

Neural Network Modeling of AChE Inhibition by New Carbazole-Bearing Oxazolones

Acetylcholine esterase (AChE) is one of the targeted enzymes in the therapy of important neurodegenerative diseases such as Alzheimer's disease. Many studies on carbazole- and oxazolone-based compounds have been conducted in the last decade due to the importance of these compounds. New carbazole-bearing oxazolones were synthesized from several carbazole aldehydes and p-nitrobenzoyl glycine as AChE inhibitors by the Erlenmeyer reaction in the present study. The inhibitory effects of three carbazole-bearing oxazolone derivatives on AChE were studied in vitro and the experimental results were modeled using artificial neural network (ANN). The developed ANN provided sufficient correlation between several dependent systems, including enzyme inhibition. The inhibition data for AChE were modeled by a two-layered ANN architecture. High correlation coefficients were observed between the experimental and predicted ANN results. Synthesized carbazole-bearing oxazolone derivatives inhibited AChE under in vitro conditions, and further research involving in vivo studies is recommended. An ANN may be a useful alternative modeling approach for enzyme inhibition.

Machine Learning and Data Mining Methods in Diabetes Research

The remarkable advances in biotechnology and health sciences have led to a significant production of data, such as high throughput genetic data and clinical information, generated from large Electronic Health Records (EHRs). To this end, application of machine learning and data mining methods in biosciences is presently, more than ever before, vital and indispensable in efforts to transform intelligently all available information into valuable knowledge. Diabetes mellitus (DM) is defined as a group of metabolic disorders exerting significant pressure on human health worldwide. Extensive research in all aspects of diabetes (diagnosis, etiopathophysiology, therapy, etc.) has led to the generation of huge amounts of data. The aim of the present study is to conduct a systematic review of the applications of machine learning, data mining techniques and tools in the field of diabetes research with respect to a) Prediction and Diagnosis, b) Diabetic Complications, c) Genetic Background and Environment, and e) Health Care and Management with the first category appearing to be the most popular. A wide range of machine learning algorithms were employed. In general, 85% of those used were characterized by supervised learning approaches and 15% by unsupervised ones, and more specifically, association rules. Support vector machines (SVM) arise as the most successful and widely used algorithm. Concerning the type of data, clinical datasets were mainly used. The title applications in the selected articles project the usefulness of extracting valuable knowledge leading to new hypotheses targeting deeper understanding and further investigation in DM.

Construction of explicit models to correlate the structure and the inhibitory activity of aldose reductase: Flavonoids and sulfonyl-pyridazinones as inhibitors

The correlation between binding energies and bioactivities is the core of structure-based computer-aided drug design. However, many models to address this correlation are still strongly system-dependent at current stage. We constructed two explicit models to correlate the binding energies with the inhibitory activities of flavonoids and sulfonyl-pyridazinones as inhibitors of aldose reductase. The introduction of multiple complex states comprised of protein, coenzyme, substrate, and inhibitor can remarkably improve the correlation coefficients, compared with that using single complex state. Recombination of energy terms from complex structures and molecular descriptors of inhibitors can further improve the correlation. The explicit models provide correlation coefficients of 0.90 and 0.92 for flavonoids and sulfonyl-pyridazinones, respectively. These models also steadily present the contribution from each energy term and the favorite of protein-inhibitor complex states. Meanwhile, we also observed that some inhibitors can accommodate alternative sites out of the conserved binding pocket at the presence/absence of coenzyme and substrate. It is responsible for the remarkable change in the binding energies and thus significantly influences the correlation between the structures and the inhibitory activities. Overall, this work presents a rational way to construct reliable explicit models through the combination of multiple physically accessible complex states, even though each of them only bears marginal information related to their activities.

Have artificial neural networks met expectations in drug discovery as implemented in QSAR framework?

INTRODUCTION

Artificial neural networks (ANNs) are highly adaptive nonlinear optimization algorithms that have been applied in many diverse scientific endeavors, ranging from economics, engineering, physics, and chemistry to medical science. Notably, in the past two decades, ANNs have been used widely in the process of drug discovery.

AREAS COVERED

In this review, the authors discuss advantages and disadvantages of ANNs in drug discovery as incorporated into the quantitative structure-activity relationships (QSAR) framework. Furthermore, the authors examine the recent studies, which span over a broad area with various diseases in drug discovery. In addition, the authors attempt to answer the question about the expectations of the ANNs in drug discovery and discuss the trends in this field.

EXPERT OPINION

The old pitfalls of overtraining and interpretability are still present with ANNs. However, despite these pitfalls, the authors believe that ANNs have likely met many of the expectations of researchers and are still considered as excellent tools for nonlinear data modeling in QSAR. It is likely that ANNs will continue to be used in drug development in the future.

QSAR study of some anti-hyperglycaemic sulphonylurea drugs

Sulphonylureas are widely used anti-hyperglycaemic drugs for the treatment of type 2 diabetes. The only published quantitative structure-activity relationship (QSAR) models for sulphonylurea drugs have been found to be questionable, for a number of reasons. We have re-analysed the human anti-hyperglycaemic potencies, acute mouse intraperitoneal toxicities (LD50) and plasma protein-binding abilities of the 15 drugs using multiple linear regression and obtained good QSAR models for each endpoint. The obtained QSARs all comply well with the Organisation for Economic Co-operation and Development (OECD) Guidelines for the Validation of (Q)SARs. We could not carry out external validation of our models for acute toxicity and plasma protein-binding because of the very small datasets available.

Natural Flavonoids as Potential Herbal Medication for the Treatment of Diabetes Mellitus and its Complications

Diabetes mellitus, together with its various complications, is becoming a serious threat to human health. Natural products are secondary metabolites widely distributed in plants, having a broad range of biological activities. The development of antidiabetic medication from natural products, especially those originating from plants with a traceable folk-usage history in treating diabetes, is receiving more attention. Many studies highlighted not only the benefits of natural flavonoids with hypoglycemic effects, but also their importance in the management of diabetic complications. This review describes selected natural flavonoids that have been validated for their hypoglycemic properties, together with their mechanisms of action. Also discussed are their activities in the treatment of diabetic complications demonstrated via laboratory diabetic animal models, in vitro and clinical trials using human subjects. Published papers from 2000 to date on flavonoids and diabetes were covered through accessing Web of Science and multiple databases for biomedical sciences. The major potential benefits of natural flavonoids discussed in this review clearly suggest that these substances are lead compounds with sufficient structural diversity of great importance in the antidiabetic drug developing process.

Quercetin in anti-diabetic research and strategies for improved quercetin bioavailability using polymer-based carriers – a review

With numerous pharmacological and biological functions bio-flavonoids gain appreciable attention in diabetes and other therapeutic research.

Ligand biological activity predictions using fingerprint-based artificial neural networks (FANN-QSAR)

This chapter focuses on the fingerprint-based artificial neural networks QSAR (FANN-QSAR) approach to predict biological activities of structurally diverse compounds. Three types of fingerprints, namely ECFP6, FP2, and MACCS, were used as inputs to train the FANN-QSAR models. The results were benchmarked against known 2D and 3D QSAR methods, and the derived models were used to predict cannabinoid (CB) ligand binding activities as a case study. In addition, the FANN-QSAR model was used as a virtual screening tool to search a large NCI compound database for lead cannabinoid compounds. We discovered several compounds with good CB2 binding affinities ranging from 6.70 nM to 3.75 μM. The studies proved that the FANN-QSAR method is a useful approach to predict bioactivities or properties of ligands and to find novel lead compounds for drug discovery research.

Inhibitory effect of polyphenolic-rich extract from Cola nitida (Kolanut) seed on key enzyme linked to type 2 diabetes and Fe(2+) induced lipid peroxidation in rat pancreas in vitro

OBJECTIVE

To investigate the inhibitory effect of phenolic-rich extracts from Cola nitida (C. nitida) seeds on key enzymes linked with type-2 diabetes and Fe(2+) induced oxidative stress in rat pancreas.

METHODS

The phenolic extract was prepared with 80% acetone (v/v). Subsequently, the antioxidant properties and inhibitory effect of the extract on α - amylase and α - glucosidase as well as on Fe(2+) induced lipid peroxidation in rat pancreas were determined in vitro.

RESULTS

The result revealed that C. nitida extract inhibited α-amylase (EC50=0.34 mg/mL) and α-glucosidase (EC50=0.32 mg/mL) activities as well as Fe(2+) induced lipid peroxidation in rat pancreas in a dose dependent manner. In addition, the extract had high DPPH radical scavenging ability (EC50=2.2 mg/mL) and reducing power (8.2 mg AAE/g). Characterization of the main phenolic compounds of the extract using gas chromatography analysis revealed catechin (6.6 mg/100 g), epicatechin (3.6 mg/100 g), apigenin (5.1 mg/100 g) and naringenin (3.6 mg/100 g) were the main compounds in the extract.

CONCLUSIONS

This antioxidant and enzyme inhibition could be some of the possible mechanism by which C. nitida is use in folklore for the management/treatment of type-2 diabetes. However, the enzyme inhibitory properties of the extract could be attributed to the presence of catechin, epicatechin, apigenin and naringenin.

Predictive QSAR modeling of aldose reductase inhibitors using Monte Carlo feature selection

This study explores the chemical space and quantitative structure-activity relationship (QSAR) of a set of 60 sulfonylpyridazinones with aldose reductase inhibitory activity. The physicochemical properties of the investigated compounds were described by a total of 3230 descriptors comprising of 6 quantum chemical descriptors and 3224 molecular descriptors. A subset of 5 descriptors was selected from the aforementioned pool by means of Monte Carlo (MC) feature selection coupled to multiple linear regression (MLR). Predictive QSAR models were then constructed by MLR, support vector machine and artificial neural network, which afforded good predictive performance as deduced from internal and external validation. The investigated models are capable of accounting for the origins of aldose reductase inhibitory activity and could be utilized in predicting this property in screening for novel and robust compounds.

Modeling in vitro inhibition of butyrylcholinesterase using molecular docking, multi-linear regression and artificial neural network approaches

Butyrylcholinesterase (BChE) has been an important protein used for development of anti-cocaine medication. Through computational design, BChE mutants with ∼2000-fold improved catalytic efficiency against cocaine have been discovered in our lab. To study drug-enzyme interaction it is important to build mathematical model to predict molecular inhibitory activity against BChE. This report presents a neural network (NN) QSAR study, compared with multi-linear regression (MLR) and molecular docking, on a set of 93 small molecules that act as inhibitors of BChE by use of the inhibitory activities (pIC₅₀ values) of the molecules as target values. The statistical results for the linear model built from docking generated energy descriptors were: r(2)=0.67, rmsd=0.87, q(2)=0.65 and loormsd=0.90; the statistical results for the ligand-based MLR model were: r(2)=0.89, rmsd=0.51, q(2)=0.85 and loormsd=0.58; the statistical results for the ligand-based NN model were the best: r(2)=0.95, rmsd=0.33, q(2)=0.90 and loormsd=0.48, demonstrating that the NN is powerful in analysis of a set of complicated data. As BChE is also an established drug target to develop new treatment for Alzheimer's disease (AD). The developed QSAR models provide tools for rationalizing identification of potential BChE inhibitors or selection of compounds for synthesis in the discovery of novel effective inhibitors of BChE in the future.

Classification of flavonoid compounds by using entropy of information theory

A total of 74 flavonoid compounds are classified into a periodic table by using an algorithm based on the entropy of information theory. Seven features in hierarchical order are used to classify structurally the flavonoids. From these features, the first three mark the group or column, while the last four are used to indicate the row or period in a table of periodic classification. Those flavonoids in the same group and period are suggested to show maximum similarity in properties. Furthermore, those with only the same group will present moderate similarity. In this report, the flavonoid compounds in the table, whose experimental data in bioactivity and antioxidant properties have been previously published, are related.

Molecular fingerprint-based artificial neural networks QSAR for ligand biological activity predictions

In this manuscript, we have reported a novel 2D fingerprint-based artificial neural network QSAR (FANN-QSAR) method in order to effectively predict biological activities of structurally diverse chemical ligands. Three different types of fingerprints, namely, ECFP6, FP2 and MACCS, were used in FANN-QSAR algorithm development, and FANN-QSAR models were compared to known 3D and 2D QSAR methods using five data sets previously reported. In addition, the derived models were used to predict GPCR cannabinoid ligand binding affinities using our manually curated cannabinoid ligand database containing 1699 structurally diverse compounds with reported cannabinoid receptor subtype CB(2) activities. To demonstrate its useful applications, the established FANN-QSAR algorithm was used as a virtual screening tool to search a large NCI compound database for lead cannabinoid compounds, and we have discovered several compounds with good CB(2) binding affinities ranging from 6.70 nM to 3.75 μM. To the best of our knowledge, this is the first report for a fingerprint-based neural network approach validated with a successful virtual screening application in identifying lead compounds. The studies proved that the FANN-QSAR method is a useful approach to predict bioactivities or properties of ligands and to find novel lead compounds for drug discovery research.

Active ingredients of traditional Chinese medicine in the treatment of diabetes and diabetic complications

INTRODUCTION

Diabetes mellitus (DM) is a chronic progressive systemic disease caused by metabolic disorder. In recent years, significant amounts of studies have shown that traditional Chinese medicine (TCM) and its active ingredients have obvious hypoglycemic effect.

AREAS COVERED

This paper summarizes single herbs and their active ingredients from TCM with the role of treating DM, and relevant literatures published in the past decades are reviewed. The active ingredients are divided into polysaccharides, saponins, alkaloids, flavonoids, terpenoids and others, which are described in this article from the aspects of active ingredients, sources, models, efficacy, and mechanisms.

EXPERT OPINION

Mechanisms of TCM in treating DM are concluded: i) to promote insulin secretion and increase serum insulin levels; ii) to increase the sensitivity of insulin and improve its resistance; iii) to inhibit glucose absorption; iv) to affect glucose metabolism of insulin receptor; and v) to scavenge radicals and prevent lipid peroxidation. The separation and extraction of effective monomer from TCM is an important direction of anti-diabetic drug discovery currently. Future research about hypoglycemic mechanism of TCM based on the clinical should combine with modern scientific methods and regulatory approach to strive for more meaningful discovery and innovation.

Integrated QSAR study for inhibitors of Hedgehog Signal Pathway against multiple cell lines:a collaborative filtering method

BACKGROUND

The Hedgehog Signaling Pathway is one of signaling pathways that are very important to embryonic development. The participation of inhibitors in the Hedgehog Signal Pathway can control cell growth and death, and searching novel inhibitors to the functioning of the pathway are in a great demand. As the matter of fact, effective inhibitors could provide efficient therapies for a wide range of malignancies, and targeting such pathway in cells represents a promising new paradigm for cell growth and death control. Current research mainly focuses on the syntheses of the inhibitors of cyclopamine derivatives, which bind specifically to the Smo protein, and can be used for cancer therapy. While quantitatively structure-activity relationship (QSAR) studies have been performed for these compounds among different cell lines, none of them have achieved acceptable results in the prediction of activity values of new compounds. In this study, we proposed a novel collaborative QSAR model for inhibitors of the Hedgehog Signaling Pathway by integration the information from multiple cell lines. Such a model is expected to substantially improve the QSAR ability from single cell lines, and provide useful clues in developing clinically effective inhibitors and modifications of parent lead compounds for target on the Hedgehog Signaling Pathway.

RESULTS

In this study, we have presented: (1) a collaborative QSAR model, which is used to integrate information among multiple cell lines to boost the QSAR results, rather than only a single cell line QSAR modeling. Our experiments have shown that the performance of our model is significantly better than single cell line QSAR methods; and (2) an efficient feature selection strategy under such collaborative environment, which can derive the commonly important features related to the entire given cell lines, while simultaneously showing their specific contributions to a specific cell-line. Based on feature selection results, we have proposed several possible chemical modifications to improve the inhibitor affinity towards multiple targets in the Hedgehog Signaling Pathway.

CONCLUSIONS

Our model with the feature selection strategy presented here is efficient, robust, and flexible, and can be easily extended to model large-scale multiple cell line/QSAR data. The data and scripts for collaborative QSAR modeling are available in the Additional file 1.