Drug designing and development is an important area of research for pharmaceutical companies and chemical scientists. However, low efficacy, off-target delivery, time consumption, and high cost impose a hurdle and challenges that impact drug design and discovery. Further, complex and big data from genomics, proteomics, microarray data, and clinical trials also impose an obstacle in the drug discovery pipeline. Artificial intelligence and machine learning technology play a crucial role in drug discovery and development. In other words, artificial neural networks and deep learning algorithms have modernized the area. Machine learning and deep learning algorithms have been implemented in several drug discovery processes such as peptide synthesis, structure-based virtual screening, ligand-based virtual screening, toxicity prediction, drug monitoring and release, pharmacophore modeling, quantitative structure-activity relationship, drug repositioning, polypharmacology, and physiochemical activity. Evidence from the past strengthens the implementation of artificial intelligence and deep learning in this field. Moreover, novel data mining, curation, and management techniques provided critical support to recently developed modeling algorithms. In summary, artificial intelligence and deep learning advancements provide an excellent opportunity for rational drug design and discovery process, which will eventually impact mankind. The primary concern associated with drug design and development is time consumption and production cost. Further, inefficiency, inaccurate target delivery, and inappropriate dosage are other hurdles that inhibit the process of drug delivery and development. With advancements in technology, computer-aided drug design integrating artificial intelligence algorithms can eliminate the challenges and hurdles of traditional drug design and development. Artificial intelligence is referred to as superset comprising machine learning, whereas machine learning comprises supervised learning, unsupervised learning, and reinforcement learning. Further, deep learning, a subset of machine learning, has been extensively implemented in drug design and development. The artificial neural network, deep neural network, support vector machines, classification and regression, generative adversarial networks, symbolic learning, and meta-learning are examples of the algorithms applied to the drug design and discovery process. Artificial intelligence has been applied to different areas of drug design and development process, such as from peptide synthesis to molecule design, virtual screening to molecular docking, quantitative structure-activity relationship to drug repositioning, protein misfolding to protein-protein interactions, and molecular pathway identification to polypharmacology. Artificial intelligence principles have been applied to the classification of active and inactive, monitoring drug release, pre-clinical and clinical development, primary and secondary drug screening, biomarker development, pharmaceutical manufacturing, bioactivity identification and physiochemical properties, prediction of toxicity, and identification of mode of action.

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Artificial intelligence to deep learning: machine intelligence approach for drug discovery.

Cyclin dependent kinase-5 (Cdk5), a family member of the cyclin-dependent kinases, plays a pivotal role in the central nervous system. During embryogenesis, Cdk5 is indispensable for brain development and, in the adult brain, it is essential for numerous neuronal processes, including higher cognitive functions such as learning and memory formation. However, Cdk5 activity becomes deregulated in several neurological disorders, such as Alzheimer's disease, Parkinson's disease and Huntington's disease, which leads to neurotoxicity. Therefore, precise control over Cdk5 activity is essential for its physiological functions. This Commentary covers the various mechanisms of Cdk5 regulation, including several recently identified protein activators and inhibitors of Cdk5 that control its activity in normal and diseased brains. We also discuss the autoregulatory activity of Cdk5 and its regulation at the transcriptional, post-transcriptional and post-translational levels. We finally highlight physiological and pathological roles of Cdk5 in the brain. Specific modulation of these protein regulators is expected to provide alternative strategies for the development of effective therapeutic interventions that are triggered by deregulation of Cdk5.

Cdk5 activity in the brain - multiple paths of regulation

Here, we report a β-galactosidase (β-Gal)-responsive photochromic fluorescent probe, NpG, that was designed to prebind to human serum albumin (HSA) to form the probe/protein hybrid, NpG@HSA. The formation of NpG@HSA led to an increase in fluorescence emission (520 nm) corresponding to the binding of the fluorescent naphthalimide unit with HSA. In addition, this enabled visualization of the spiropyran fluorescence emission in aqueous media. Our probe/protein hybrid approach afforded a unique imaging platform with enhanced cell permeability and solubility that was capable of visualizing the cellular uptake of NpG@HSA before its activation by β-Gal. The β-Gal-mediated cleavage of the galactose unit within the NpG@HSA hybrid resulted in the formation of NpM@HSA and an increase in red fluorescence emission (620 nm). The resultant merocyanine unit was then able to undergo photoisomerization (merocyanine ↔ spiropyran) to facilitate STORM (i.e., stochastic optical reconstruction microscopy) imaging with minimal phototoxicity and excellent photostability/reversibility. Using STORM, NpG@HSA was able to determine the subcellular distribution of β-Gal activity between cell lines with nanoscale precision. We believe that this system represents a versatile imaging platform for the design of photochromic fluorescent probes suitable for illuminating the precise location of disease-specific biomarkers in various cellular processes.

Photochromic Fluorescent Probe Strategy for the Super-resolution Imaging of Biologically Important Biomarkers

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.

https://onlinelibrary.wiley.com/doi/pdf/10.1002/med.21764

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

BackgroundOptic nerve damage initiates a series of early atrophic events in retinal ganglion cells (RGCs) that precede the BAX-dependent committed step of the intrinsic apoptotic program. Nuclear atrophy, including global histone deacetylation, heterochromatin formation, shrinkage and collapse of nuclear structure, and the silencing of normal gene expression, comprise an important obstacle to overcome in therapeutic approaches to preserve neuronal function. Several studies have implicated histone deacetylases (HDACs) in the early stages of neuronal cell death, including RGCs. Importantly, these neurons exhibit nuclear translocation of HDAC3 shortly after optic nerve damage. Additionally, HDAC3 activity has been reported to be selectively toxic to neurons.ResultsRGC-specific conditional knockout of Hdac3 was achieved by transducing the RGCs of Hdac3fl/fl mice with an adeno-associated virus serotype 2 carrying CRE recombinase and GFP (AAV2-Cre/GFP). Controls included similar viral transduction of Rosa26fl/fl reporter mice. Optic nerve crush (ONC) was then performed on eyes. The ablation of Hdac3 in RGCs resulted in significant amelioration of characteristics of ONC-induced nuclear atrophy such as H4 deacetylation, heterochromatin formation, and the loss of nuclear structure. RGC death was also significantly reduced. Interestingly, loss of Hdac3 expression did not lead to protection against RGC-specific gene silencing after ONC, although this effect was achieved using the broad spectrum inhibitor, Trichostatin A.ConclusionAlthough other HDACs may be responsible for gene expression changes in RGCs, our results indicate a critical role for HDAC3 in nuclear atrophy in RGC apoptosis following axonal injury. This study provides a framework for studying the roles of other prevalent retinal HDACs in neuronal death as a result of axonal injury.

Histone Deacetylase 3 (HDAC3) plays an important role in retinal ganglion cell death after acute optic nerve injury

Pharmacological intervention of histone deacetylase enzymes in the neurodegenerative disorders.

Diabetes and colon cancer are the leading cause of mortality worldwide. According to World Health Organization, the number of patients with diabetes and cancer is going to be elevated by 50% in 2020. However, several flavonoids have been known to be useful in reducing the chance of cancer/diabetes but the hunt of a single biomolecule that can act as therapeutic and preventive molecules for future epidemic continues. In this review, we aim to perform an illustration of all researches done that target molecular signaling using luteolin in cancer/diabetes and predicted target protein using PharmMapper. The search confirms that luteolin can be a remedial molecule for both cancer and diabetes via acting on variety of signaling pathway. Furthermore, we also intend to illustrate/compare the predicted and verified molecular modes of action of luteolin. Fluorescence in situ hybridization analysis confirms the expression of CCND1 in colon cancer while immunofluorescence analysis confirms the CDK4 in diabetes. Finally, an effort has been made to map docking of marker protein-luteolin at a particular site using docking software. This review gives a holistic overview about luteolin as a therapeutic molecule for cancer/diabetes via acting on multiple signaling cascade such as p53, Wnt, eNOS, iNOS, SOD and MMP9, with especial emphasis on the cyclin-CDK pathway. Altogether, the review concludes that luteolin can be a molecule for the therapy of both cancer and diabetes by acting on broad signaling pathway.

Can luteolin be a therapeutic molecule for both colon cancer and diabetes

Post-translational modifications: Regulators of neurodegenerative proteinopathies.

Autophagy and apoptosis are two crucial self-destructive processes that maintain cellular homeostasis, which are characterized by their morphology and regulated through signal transduction mechanisms. These pathways determine the fate of cellular organelle and protein involved in human health and disease such as neurodegeneration, cancer, and cardiovascular disease. Cell death pathways share common molecular mechanisms, such as mitochondrial dysfunction, oxidative stress, calcium ion concentration, reactive oxygen species, and endoplasmic reticulum stress. Some key signaling molecules such as p53 and VEGF mediated angiogenic pathway exhibit cellular and molecular responses resulting in the triggering of apoptotic and autophagic pathways. Herein, based on previous studies, we describe the intricate relation between cell death pathways through their common genes and the role of various stress-causing agents. Further, extensive research on autophagy and apoptotic machinery excavates the implementation of selective biomarkers, for instance, mTOR, Bcl-2, BH3 family members, caspases, AMPK, PI3K/Akt/GSK3β, and p38/JNK/MAPK, in the pathogenesis and progression of neurodegenerative diseases. This molecular phenomenon will lead to the discovery of possible therapeutic biomolecules as a pharmacological intervention that are involved in the modulation of apoptosis and autophagy pathways. Moreover, we describe the potential role of micro-RNAs, long non-coding RNAs, and biomolecules as therapeutic agents that regulate cell death machinery to treat neurodegenerative diseases. Mounting evidence demonstrated that under stress conditions, such as calcium efflux, endoplasmic reticulum stress, the ubiquitin-proteasome system, and oxidative stress intermediate molecules, namely p53 and VEGF, activate and cause cell death. Further, activation of p53 and VEGF cause alteration in gene expression and dysregulated signaling pathways through the involvement of signaling molecules, namely mTOR, Bcl-2, BH3, AMPK, MAPK, JNK, and PI3K/Akt, and caspases. Alteration in gene expression and signaling cascades cause neurotoxicity and misfolded protein aggregates, which are characteristics features of neurodegenerative diseases. Excessive neurotoxicity and misfolded protein aggregates lead to neuronal cell death by activating death pathways like autophagy and apoptosis. However, autophagy has a dual role in the apoptosis pathways, i.e., activation and inhibition of the apoptosis signaling. Further, micro-RNAs and LncRNAs act as pharmacological regulators of autophagy and apoptosis cascade, whereas, natural compounds and chemical compounds act as pharmacological inhibitors that rescue neuronal cell death through inhibition of apoptosis and autophagic cell death.

Rohan Gupta

Papers

Artificial intelligence to deep learning: machine intelligence approach for drug discovery.

Pharmacological intervention of histone deacetylase enzymes in the neurodegenerative disorders.

Can luteolin be a therapeutic molecule for both colon cancer and diabetes

Post-translational modifications: Regulators of neurodegenerative proteinopathies.

Autophagy and apoptosis cascade: which is more prominent in neuronal death?