Clinical development of orally active peptide drugs has been restricted by the unfavorable physicochemical properties of these molecules limiting intestinal mucosal permeation and the lack of ...stability of peptides against enzymatic degradation. Successful oral delivery of peptides will depend, therefore, on strategies designed to alter the physicochemical characteristics of these potential drugs, without changing their biological activity, in order to increase the permeation across intestinal cells. This manuscript will focus on the biological barrier properties that limit oral peptide bioavailability and on prodrug strategies designed to overcome these barriers.
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•Artificial Intelligence (AI) has revolutionized many aspects of the pharmaceuticals.•AI assistance to pharma industries helps to improve overall life cycle of product.•AI can be implemented in ...pharma ranging from drug discovery to product management.•Future challenges related to AI and their respective solutions have been expounded.
Artificial Intelligence (AI) has recently started to gear-up its application in various sectors of the society with the pharmaceutical industry as a front-runner beneficiary. This review highlights the impactful use of AI in diverse areas of the pharmaceutical sectors viz., drug discovery and development, drug repurposing, improving pharmaceutical productivity, clinical trials, etc. to name a few, thus reducing the human workload as well as achieving targets in a short period. Crosstalk on the tools and techniques utilized in enforcing AI, ongoing challenges, and ways to overcome them, along with the future of AI in the pharmaceutical industry, is also discussed.
Artificial intelligence-integrated drug discovery and development has accelerated the growth of the pharmaceutical sector, leading to a revolutionary change in the pharma industry. Here, we discuss areas of integration, tools, and techniques utilized in enforcing AI, ongoing challenges, and ways to overcome them.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
The rise of deep learning in drug discovery Chen, Hongming; Engkvist, Ola; Wang, Yinhai ...
Drug discovery today,
June 2018, 2018-06-00, 20180601, Volume:
23, Issue:
6
Journal Article
Peer reviewed
Open access
•Deep learning technology has gained remarkable success.•We highlight the recent applications of deep learning in drug discovery research.•Some popular deep learning architectures are introduced in ...the current study.•Future development of deep learning in drug discovery is discussed.
Over the past decade, deep learning has achieved remarkable success in various artificial intelligence research areas. Evolved from the previous research on artificial neural networks, this technology has shown superior performance to other machine learning algorithms in areas such as image and voice recognition, natural language processing, among others. The first wave of applications of deep learning in pharmaceutical research has emerged in recent years, and its utility has gone beyond bioactivity predictions and has shown promise in addressing diverse problems in drug discovery. Examples will be discussed covering bioactivity prediction, de novo molecular design, synthesis prediction and biological image analysis.
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•Chemical graph theory and descriptors in drug discovery.•Chemical fingerprint and similarity analysis.•Machine learning models for virtual screening.•Future challenges and direction in ...machine-learning-based drug discovery.
Chemoinformatics is an established discipline focusing on extracting, processing and extrapolating meaningful data from chemical structures. With the rapid explosion of chemical ‘big’ data from HTS and combinatorial synthesis, machine learning has become an indispensable tool for drug designers to mine chemical information from large compound databases to design drugs with important biological properties. To process the chemical data, we first reviewed multiple processing layers in the chemoinformatics pipeline followed by the introduction of commonly used machine learning models in drug discovery and QSAR analysis. Here, we present basic principles and recent case studies to demonstrate the utility of machine learning techniques in chemoinformatics analyses; and we discuss limitations and future directions to guide further development in this evolving field.
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Role of oxidative stress in depression Bhatt, Shvetank; Nagappa, Anantha Naik; Patil, Chandragouda R.
Drug discovery today,
07/2020, Volume:
25, Issue:
7
Journal Article
Peer reviewed
•The Reactive Oxygen Species have an utmost importance in progression of Depression.•Depression is associated with lowered concentration of anti-oxidants in plasma.•The imbalance in ROS leads to ...abnormality of brain functions and neuronal signalling.•Increased oxidative stress triggers multiple proinflammatory mediators.•This review highlights the involvement of ROS in depressive disorders.
Reactive oxygen species (ROS) have vital roles in cellular signaling and in defence against invasive microorganisms. Excessive ROS generation and exhaustion of antioxidative defences trigger proinflammatory signaling, damaging vital macromolecules and inducing cellular apoptosis. The failure of cells to maintain redox homeostasis and resultant generation of proinflammatory mediators leads to cell necrosis. The brain is more vulnerable to oxidative stress (OS) because of its higher oxygen consumption, higher lipid content, and weaker antioxidative defence. OS is a main cause of neurodegeneration and its involvement in the pathogenesis of major depressive disorder (MDD) is unequivocally established. OS and proinflammatory signaling have emerged as mainstays in the pathogenesis of MDD. Targeting these changes with suitable antioxidants could be an effective strategy to treat MDD.
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Human coronaviruses (CoVs) are enveloped viruses with a positive-sense single-stranded RNA genome. Currently, six human CoVs have been reported including human coronavirus 229E (HCoV-229E), OC43 ...(HCoV-OC43), NL63 (HCoV-NL63), HKU1 (HCoV-HKU1), severe acute respiratory syndrome (SARS) coronavirus (SARS-CoV), and MiddleEast respiratory syndrome (MERS) coronavirus (MERS-CoV). They cause moderate to severe respiratory and intestinal infections in humans. In this review, we focus on recent advances in the research and development of small-molecule anti-human coronavirus therapies targeting different stages of the CoV life cycle.
Recent advances in the research and development of small-molecule anti-human coronavirus therapies.
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•ADMET modeling plays a pivotal part in drug discovery.•Chemoinformatics has evolved into robust machine learning approaches.•Comprehensive web-based platforms for ADMET prediction have been ...developed.•Tree-based methods and support vector machines have predominated in recent studies.
In silico prediction of ADMET is an important component of pharmaceutical R&D. Last year, the FDA approved 59 new molecular entities, with small molecules comprising 64% of the therapies approved in 2018. Estimation of pharmacokinetic properties in the early phases of drug discovery has been central to guiding hit-to-lead and lead-optimization efforts. Given the outstanding complexity of the current R&D model, drug discovery players have intensely pursued molecular modeling strategies to identify patterns in ADMET data and convert them into knowledge. The field has advanced alongside the progress of chemoinformatics, which has evolved from traditional chemometrics to advanced machine learning methods.
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•Triazoles mimic different functional groups, resulting optimal bioisosteres for the synthesis of new active molecules.•Triazoles present a marked stability under hydrolytic, oxidative and reductive ...conditions.•New highly regioselective synthetic methodologies for triazoles synthesis have been recently developed.•Among the plethora of the selected examples of bioisosterism, the amide bond replacement is clearly a predominant approach.
1,2,3-Triazole is a well-known scaffold that has a widespread occurrence in different compounds characterized by several bioactivities, such as antimicrobial, antiviral, and antitumor effects. Moreover, the structural features of 1,2,3-triazole enable it to mimic different functional groups, justifying its wide use as a bioisostere for the synthesis of new active molecules. Here, we provide an overview of the 1,2,3-triazole ring as a bioisostere for the design of drug analogs, highlighting relevant recent examples.
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