Computer-aided design of novel antibacterial 3-hydroxypyridine-4-ones: application of QSAR methods based on the MOLMAP approach

Advances in machine intelligence-driven virtual screening approaches for big-data

Virtual screening (VS) is an integral and ever-evolving domain of drug discovery framework. The VS is traditionally classified into ligand-based (LB) and structure-based (SB) approaches. Machine intelligence or artificial intelligence has wide applications in the drug discovery domain to reduce time and resource consumption. In combination with machine intelligence algorithms, VS has emerged into revolutionarily progressive technology that learns within robust decision orders for data curation and hit molecule screening from large VS libraries in minutes or hours. The exponential growth of chemical and biological data has evolved as "big-data" in the public domain demands modern and advanced machine intelligence-driven VS approaches to screen hit molecules from ultra-large VS libraries. VS has evolved from an individual approach (LB and SB) to integrated LB and SB techniques to explore various ligand and target protein aspects for the enhanced rate of appropriate hit molecule prediction. Current trends demand advanced and intelligent solutions to handle enormous data in drug discovery domain for screening and optimizing hits or lead with fewer or no false positive hits. Following the big-data drift and tremendous growth in computational architecture, we presented this review. Here, the article categorized and emphasized individual VS techniques, detailed literature presented for machine learning implementation, modern machine intelligence approaches, and limitations and deliberated the future prospects.

Ligand-Based Design of Novel Quinoline Derivatives as Potential Anticancer Agents: An In-Silico Virtual Screening Approach

In this study, using the Comparative Molecular Field Analysis (CoMFA) approach, the structure-activity relationship of 33 small quinoline-based compounds with biological anti-gastric cancer activity in vitro was analyzed in 3D space. Once the 3D geometric and energy structure of the target chemical library has been optimized and their steric and electrostatic molecular field descriptions computed, the ideal 3D-QSAR model is generated and matched using the Partial Least Squares regression (PLS) algorithm. The accuracy, statistical precision, and predictive power of the developed 3D-QSAR model were confirmed by a range of internal and external validations, which were interpreted by robust correlation coefficients (RTrain2=0.931; Qcv2=0.625; RTest2=0.875). After carefully analyzing the contour maps produced by the trained 3D-QSAR model, it was discovered that certain structural characteristics are beneficial for enhancing the anti-gastric cancer properties of Quinoline derivatives. Based on this information, a total of five new quinoline compounds were developed, with their biological activity improved and their drug-like bioavailability measured using POM calculations. To further explore the potential of these compounds, molecular docking and molecular dynamics simulations were performed in an aqueous environment for 100 nanoseconds, specifically targeting serine/threonine protein kinase. Overall, the new findings of this study can serve as a starting point for further experiments with a view to the identification and design of a potential next-generation drug for target therapy against cancer.

Synthesis of 3-hydroxypyridin-4-one derivatives bearing benzyl hydrazide substitutions towards anti-tyrosinase and free radical scavenging activities

Tyrosinase is a vital enzyme in the biosynthesis of melanin, which has a significant role in skin protection. Due to the importance of the tyrosinase enzyme in the cosmetics and health industries, studies to design new tyrosinase inhibitors have been expanded. In this study, the design and synthesis of 3-dihydroxypyridine-4-one derivatives containing benzo hydrazide groups with different substitutions were carried out, and their antioxidant and anti-tyrosinase activities were also evaluated. The proposed compounds showed tyrosinase inhibitory effects (IC50) in the 25.29 to 64.13 μM range. Among all compounds, 6i showed potent anti-tyrosinase activity with an IC50 = 25.29 μM. Also, the antioxidant activity of derivatives by using DPPH radical scavenging indicates an EC50 value between 0.039 and 0.389 mM. Molecular docking studies were performed to reveal the position and interactions of 6i as the most potent inhibitor within the tyrosinase active site. The results showed that 6i binds well to the proposed binding site and forms a stable complex with the target protein. Furthermore, the physicochemical profiles of the tested compounds indicated drug-like and bioavailability properties. The kinetic assay revealed that 6i acts as a competitive inhibitor. Also, for the estimation of the reactivity of the best compound (6i), the density functional theory (DFT) was performed at the B3LYP/6-31+G**.

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.

Using computers to ESKAPE the antibiotic resistance crisis

Since the discovery of penicillin, the development and use of antibiotics have promoted safe and effective control of bacterial infections. However, the number of antibiotic-resistance cases has been ever increasing over time. Thus, the drug discovery process demands fast, efficient and cost-effective alternative approaches for developing lead candidates with outstanding performance. Computational approaches are appealing techniques to develop lead candidates in an in silico fashion. In this review, we provide an overview of the implementation of current in silico state-of-the-art techniques, including machine learning (ML) and deep learning (DL), in drug discovery. We also discuss the development of quantum computing and its potential benefits for antibiotics research and current bottlenecks that limit computational drug discovery advancement.

Quinazoline analogues as cytotoxic agents; QSAR, docking, and in silico studies

Background and purpose:

Synthesis and investigation of pharmacological activity of novel compounds are time and money-consuming. However, computational techniques, docking, and in silico studies have facilitated drug discovery research to design pharmacologically effective compounds.

Experimental approach:

In this study, a series of quinazoline derivatives were applied to quantitative structure-activity relationship (QSAR) analysis. A collection of chemometric methods were conducted to provide relations between structural features and cytotoxic activity of a variety of quinazoline derivatives against breast cancer cell line. An in silico-screening was accomplished and new impressive lead compounds were designed to target the epidermal growth factor receptor (EGFR)-active site based on a new structural pattern. Molecular docking was performed to delve into the interactions, free binding energy, and molecular binding mode of the compounds against the EGFR target.

Findings/Results:

A comparison between different methods significantly indicated that genetic algorithm-partial least-squares were selected as the best model for quinazoline derivatives. In the current study, constitutional, functional, chemical, resource description framework, 2D autocorrelation, and charge descriptors were considered as significant parameters for the prediction of anticancer activity of quinazoline derivatives. In silico screening was employed to discover new compounds with good potential as anticancer agents and suggested to be synthesized. Also, the binding energy of docking simulation showed desired correlation with QSAR and experimental data.

Conclusion and implications:

The results showed good accordance between binding energy and QSAR results. Compounds Q₁-Q₃₀ are desired to be synthesized and applied to in vitro evaluation.

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.

Hydroxypyridinone-Based Iron Chelators with Broad-Ranging Biological Activities

Iron plays an essential role in all living cells because of its unique chemical properties. It is also the most abundant trace element in mammals. However, when iron is present in excess or inappropriately located, it becomes toxic. Excess iron can become involved in free radical formation, resulting in oxidative stress and cellular damage. Iron chelators are used to treat serious pathological disorders associated with systemic iron overload. Hydroxypyridinones stand out for their outstanding chelation properties, including high selectivity for Fe³⁺ in the biological environment, ease of derivatization, and good biocompatibility. Herein, we overview the potential for multifunctional hydroxypyridinone-based chelators to be used as therapeutic agents against a wide range of diseases associated either with systemic or local elevated iron levels.

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.

Virtual Screening Meets Deep Learning

BACKGROUND

Automated compound testing is currently the de facto standard method for drug screening, but it has not brought the great increase in the number of new drugs that was expected. Computer- aided compounds search, known as Virtual Screening, has shown the benefits to this field as a complement or even alternative to the robotic drug discovery. There are different methods and approaches to address this problem and most of them are often included in one of the main screening strategies. Machine learning, however, has established itself as a virtual screening methodology in its own right and it may grow in popularity with the new trends on artificial intelligence.

OBJECTIVE

This paper will attempt to provide a comprehensive and structured review that collects the most important proposals made so far in this area of research. Particular attention is given to some recent developments carried out in the machine learning field: the deep learning approach, which is pointed out as a future key player in the virtual screening landscape.

Machine-learning techniques applied to antibacterial drug discovery

The emergence of drug-resistant bacteria threatens to revert humanity back to the preantibiotic era. Even now, multidrug-resistant bacterial infections annually result in millions of hospital days, billions in healthcare costs, and, most importantly, tens of thousands of lives lost. As many pharmaceutical companies have abandoned antibiotic development in search of more lucrative therapeutics, academic researchers are uniquely positioned to fill the pipeline. Traditional high-throughput screens and lead-optimization efforts are expensive and labor intensive. Computer-aided drug-discovery techniques, which are cheaper and faster, can accelerate the identification of novel antibiotics, leading to improved hit rates and faster transitions to preclinical and clinical testing. The current review describes two machine-learning techniques, neural networks and decision trees, that have been used to identify experimentally validated antibiotics. We conclude by describing the future directions of this exciting field.

A general ANN-based multitasking model for the discovery of potent and safer antibacterial agents

Bacteria have been one of the world's most dangerous and deadliest pathogens for mankind, nowadays giving rise to significant public health concerns. Given the prevalence of these microbial pathogens and their increasing resistance to existing antibiotics, there is a pressing need for new antibacterial drugs. However, development of a successful drug is a complex, costly, and time-consuming process. Quantitative Structure-Activity Relationships (QSAR)-based approaches are valuable tools for shortening the time of lead compound identification but also for focusing and limiting time-costly synthetic activities and in vitro/vivo evaluations. QSAR-based approaches, supported by powerful statistical techniques such as artificial neural networks (ANNs), have evolved to the point of integrating dissimilar types of chemical and biological data. This chapter reports an overview of the current research and potential applications of QSAR modeling tools toward the rational design of more efficient antibacterial agents. Particular emphasis is given to the setup of multitasking models along with ANNs aimed at jointly predicting different antibacterial activities and safety profiles of drugs/chemicals under diverse experimental conditions.

Ground-state and excited-state multiple proton transfer via a hydrogen-bonded water wire for 3-hydroxypyridine

The multiple proton transfer reactions of 3-hydroxypyridine-(H₂O)₃ have been demonstrated, and a perfect proton transfer cycle has been revealed in the ground and excited states.