How Do Retinoids Affect Alzheimer's Disease and Can They Be Novel Drug Candidates?

Alzheimer's disease is a chronic, neurological condition that faces many challenges in its management and therapy nowadays highlighting the importance and urgent need of researching new ways of approaching this disease. Retinoic acid and its derivatives, collectively known as the retinoids, are considered promising agents that have disease-modifying properties in affecting Alzheimer's disease. This thesis aims to address the research questions of what the role of retinoids is in Alzheimer's disease, and whether they can be used as a novel drug candidate for treating this condition. Retinoids' properties and agonistic actions on the nuclear receptors retinoic acid receptor (RAR) and retinoic X receptor (RXR) affect various pathways as well as their underlying genetic factors that compose important pathophysiological hallmarks causing the progression of Alzheimer's disease as amyloid ß (Aß) production and deposition, neurofibrillary tangle (NFT) formation and phosphorylation, and inflammatory and autoimmune responses. Retinoic acid inhibits the amplification of these pathways and modifies the disease progression in animal models, proposing a solid basis for human trials. Hence, investigating retinoids as pharmacological agents in human trials has been conducted, and several synthetic analogues have been developed to address issues concerning retinoic acid's instability and short half-life, as well as adverse drug reactions. The most prominent of these analogues is tamibarotene, a stable retinoic acid derivative with a higher half-life, higher specificity to target receptors, and fewer adverse reactions. A number of criteria that explain what a novel drug candidate should have when managing Alzheimer's disease have been formulated, and which also explain why most novel drug candidates other than retinoic acid have failed in achieving clinical results. Most of these candidates share one common trait which is a single-target approach in targeting disease pathways. This means that when administering these agents, their actions are to target a single disease-causing pathway at a time but do not affect other pathways. On the other hand, tamibarotene is a novel drug candidate that targets a range of pathways at once and provides a more comprehensive approach in its pharmacological actions.

1,3,4-Oxadiazole Scaffold in Antidiabetic Drug Discovery: An Overview.

Diabetes mellitus is one of the biggest challenges for the scientific community in the 21st century. With the increasing number of cases of diabetes and drug-resistant diabetes, there is an urgent need to develop new potent molecules capable of combating this cruel disease. Medicinal chemistry concerns the discovery, development, identification, and interpretation of the mode of action of biologically active compounds at the molecular level. Oxadiazole-based derivatives have come up as a potential option for antidiabetic drug research. Oxadiazole is a five-membered heterocyclic organic compound containing two nitrogen atoms and one oxygen atom in its ring. Oxadiazole hybrids have shown the ability to improve glucose tolerance, enhance insulin sensitivity, and reduce fasting blood glucose levels. The mechanisms underlying the antidiabetic effects of oxadiazole involve the modulation of molecular targets such as peroxisome proliferator-activated receptor gamma (PPARγ), α-glucosidase, α-amylase and GSK-3ß which regulate glucose metabolism and insulin secretion. The present review article describes the chemical structure and properties of oxadiazoles and highlights the antidiabetic activity through action on different targets. The SAR for the oxadiazole hybrids has been discussed in this article, which will pave the way for the design and development of new 1,3,4-oxadiazole derivatives as promising antidiabetic agents in the future. We expect that this article will provide comprehensive knowledge and current innovation on oxadiazole derivatives with antidiabetic potential and will fulfil the needs of the scientific community in designing and developing efficacious antidiabetic agents.

FaissMolLib: An efficient and easy deployable tool for ligand-based virtual screening.

Virtual screening-based molecular similarity and fingerprint are crucial in drug design, target prediction, and ADMET prediction, aiding in identifying potential hits and optimizing lead compounds. However, challenges such as lack of comprehensive open-source molecular fingerprint databases and efficient search methods for virtual screening are prevalent. To address these issues, we introduce FaissMolLib, an open-source virtual screening tool that integrates 2.8 million compounds from ChEMBL and ZINC databases. Notably, FaissMolLib employs the highly efficient Faiss search algorithm, outperforming the Tanimoto algorithm in identifying similar molecules with its tighter clustering in scatter plots and lower mean, standard deviation, and variance in key molecular properties. This feature enables FaissMolLib to screen 2.8 million compounds in just 0.05 seconds, offering researchers an efficient, easily deployable solution for virtual screening on laptops and building unique compound databases. This significant advancement holds great potential for accelerating drug discovery efforts and enhancing chemical data analysis. FaissMolLib is freely available at http://liuhaihan.gnway.cc:80. The code and dataset of FaissMolLib are freely available at https://github.com/Superhaihan/FiassMolLib.

DockingGA: enhancing targeted molecule generation using transformer neural network and genetic algorithm with docking simulation.

Generative molecular models generate novel molecules with desired properties by searching chemical space. Traditional combinatorial optimization methods, such as genetic algorithms, have demonstrated superior performance in various molecular optimization tasks. However, these methods do not utilize docking simulation to inform the design process, and heavy dependence on the quality and quantity of available data, as well as require additional structural optimization to become candidate drugs. To address this limitation, we propose a novel model named DockingGA that combines Transformer neural networks and genetic algorithms to generate molecules with better binding affinity for specific targets. In order to generate high quality molecules, we chose the Self-referencing Chemical Structure Strings to represent the molecule and optimize the binding affinity of the molecules to different targets. Compared to other baseline models, DockingGA proves to be the optimal model in all docking results for the top 1, 10 and 100 molecules, while maintaining 100% novelty. Furthermore, the distribution of physicochemical properties demonstrates the ability of DockingGA to generate molecules with favorable and appropriate properties. This innovation creates new opportunities for the application of generative models in practical drug discovery.

Discovery of low-molecular-weight phenylalanine derivatives as novel HIV capsid modulators with improved antiretroviral activity and metabolic stability.

The HIV capsid (CA) protein is a promising target for anti-AIDS treatment due to its critical involvement in viral replication. Herein, we utilized the well-documented CA inhibitor PF74 as our lead compound and designed a series of low-molecular-weight phenylalanine derivatives. Among them, compound 7t exhibited remarkable antiviral activity with a high selection index (EC50 = 0.040 µM, SI = 2815), surpassing that of PF74 (EC50 = 0.50 µM, SI = 258). Furthermore, when evaluated against the HIV-2 strain, 7t (EC50 = 0.13 µM) demonstrated approximately 14-fold higher potency than that of PF74 (EC50 = 1.76 µM). Insights obtained from surface plasmon resonance (SPR) revealed that 7t exhibited stronger target affinity to the CA hexamer and monomer in comparison to PF74. The potential interactions between 7t and the HIV-1 CA were further elucidated using molecular docking and molecular dynamics simulations, providing a plausible explanation for the enhanced target affinity with 7t over PF74. Moreover, the metabolic stability assay demonstrated that 7t (T1/2 = 77.0 min) significantly outperforms PF74 (T1/2 = 0.7 min) in human liver microsome, exhibiting an improvement factor of 110-fold. In conclusion, 7t emerges as a promising drug candidate warranting further investigation.

GLDM: hit molecule generation with constrained graph latent diffusion model.

Discovering hit molecules with desired biological activity in a directed manner is a promising but profound task in computer-aided drug discovery. Inspired by recent generative AI approaches, particularly Diffusion Models (DM), we propose Graph Latent Diffusion Model (GLDM)-a latent DM that preserves both the effectiveness of autoencoders of compressing complex chemical data and the DM's capabilities of generating novel molecules. Specifically, we first develop an autoencoder to encode the molecular data into low-dimensional latent representations and then train the DM on the latent space to generate molecules inducing targeted biological activity defined by gene expression profiles. Manipulating DM in the latent space rather than the input space avoids complicated operations to map molecule decomposition and reconstruction to diffusion processes, and thus improves training efficiency. Experiments show that GLDM not only achieves outstanding performances on molecular generation benchmarks, but also generates samples with optimal chemical properties and potentials to induce desired biological activity.

A Review of FDA-Approved Anti-HIV-1 Drugs, Anti-Gag Compounds, and Potential Strategies for HIV-1 Eradication.

Acquired immunodeficiency syndrome (AIDS) is an enormous global health threat stemming from human immunodeficiency virus (HIV-1) infection. Up to now, the tremendous advances in combination antiretroviral therapy (cART) have shifted HIV-1 infection from a fatal illness into a manageable chronic disorder. However, the presence of latent reservoirs, the multifaceted nature of HIV-1, drug resistance, severe off-target effects, poor adherence, and high cost restrict the efficacy of current cART targeting the distinct stages of the virus life cycle. Therefore, there is an unmet need for the discovery of new therapeutics that not only bypass the limitations of the current therapy but also protect the body's health at the same time. The main goal for complete HIV-1 eradication is purging latently infected cells from patients' bodies. A potential strategy called "lock-in and apoptosis" targets the budding phase of the life cycle of the virus and leads to susceptibility to apoptosis of HIV-1 infected cells for the elimination of HIV-1 reservoirs and, ultimately, for complete eradication. The current work intends to present the main advantages and disadvantages of United States Food and Drug Administration (FDA)-approved anti-HIV-1 drugs as well as plausible strategies for the design and development of more anti-HIV-1 compounds with better potency, favorable pharmacokinetic profiles, and improved safety issues.

Optimizing Neural Networks for Chemical Reaction Prediction: Insights from Methylene Blue Reduction Reactions.

This paper offers a thorough investigation of hyperparameter tuning for neural network architectures using datasets encompassing various combinations of Methylene Blue (MB) Reduction by Ascorbic Acid (AA) reactions with different solvents and concentrations. The aim is to predict coefficients of decay plots for MB absorbance, shedding light on the complex dynamics of chemical reactions. Our findings reveal that the optimal model, determined through our investigation, consists of five hidden layers, each with sixteen neurons and employing the Swish activation function. This model yields an NMSE of 0.05, 0.03, and 0.04 for predicting the coefficients A, B, and C, respectively, in the exponential decay equation A + B · e-x/C. These findings contribute to the realm of drug design based on machine learning, providing valuable insights into optimizing chemical reaction predictions.

Antibiotic class with potent in vivo activity targeting lipopolysaccharide synthesis in Gram-negative bacteria.

Here, we describe the identification of an antibiotic class acting via LpxH, a clinically unexploited target in lipopolysaccharide synthesis. The lipopolysaccharide synthesis pathway is essential in most Gram-negative bacteria and there is no analogous pathway in humans. Based on a series of phenotypic screens, we identified a hit targeting this pathway that had activity on efflux-defective strains of Escherichia coli. We recognized common structural elements between this hit and a previously published inhibitor, also with activity against efflux-deficient bacteria. With the help of X-ray structures, this information was used to design inhibitors with activity on efflux-proficient, wild-type strains. Optimization of properties such as solubility, metabolic stability and serum protein binding resulted in compounds having potent in vivo efficacy against bloodstream infections caused by the critical Gram-negative pathogens E. coli and Klebsiella pneumoniae. Other favorable properties of the series include a lack of pre-existing resistance in clinical isolates, and no loss of activity against strains expressing extended-spectrum-ß-lactamase, metallo-ß-lactamase, or carbapenemase-resistance genes. Further development of this class of antibiotics could make an important contribution to the ongoing struggle against antibiotic resistance.

Structural simulation and selective inhibitor discovery study for histone demethylases KDM4E/6B from a computational perspective.

The methylation and demethylation of lysine and arginine side chains are fundamental processes in gene regulation and disease development. Histone lysine methylation, controlled by histone lysine methyltransferases (KMTs) and histone lysine demethylases (KDMs), plays a vital role in maintaining cellular homeostasis and has been implicated in diseases such as cancer and aging. This study focuses on two members of the lysine demethylase (KDM) family, KDM4E and KDM6B, which are significant in gene regulation and disease pathogenesis. KDM4E demonstrates selectivity for gene regulation, particularly concerning cancer, while KDM6B is implicated in inflammation and cancer. The study utilizes specific inhibitors, DA-24905 and GSK-J1, showcasing their exceptional selectivity for KDM4E and KDM6B, respectively. Employing an array of computational simulations, including sequence alignment, molecular docking, dynamics simulations, and free energy calculations, we conclude that although the binding cavities of KDM4E and KDM6B has high similarity, there are still some different crucial amino acid residues, indicating diverse binding forms between protein and ligands. Various interaction predominates when proteins are bound to different ligands, which also has significant effect on selective inhibition. These findings provide insights into potential therapeutic strategies for diseases by selectively targeting these KDM members.

Non-peptidic inhibitors targeting SARS-CoV-2 main protease: A review.

The COVID-19 pandemic continues to pose a threat to global health, and sounds the alarm for research & development of effective anti-coronavirus drugs, which are crucial for the patients and urgently needed for the current epidemic and future crisis. The main protease (Mpro) stands as an essential enzyme in the maturation process of SARS-CoV-2, playing an irreplaceable role in regulating viral RNA replication and transcription. It has emerged as an ideal target for developing antiviral agents against SARS-CoV-2 due to its high conservation and the absence of homologous proteases in the human body. Among the SARS-CoV-2 Mpro inhibitors, non-peptidic compounds hold promising prospects owing to their excellent antiviral activity and improved metabolic stability. In this review, we offer an overview of research progress concerning non-peptidic SARS-CoV-2 Mpro inhibitors since 2020. The efforts delved into molecular structures, structure-activity relationships (SARs), biological activity, and binding modes of these inhibitors with Mpro. This review aims to provide valuable clues and insights for the development of anti-SARS-CoV-2 agents as well as broad-spectrum coronavirus Mpro inhibitors.

Time-series analysis of rhenium(I) organometallic covalent binding to a model protein for drug development.

Metal-based complexes with their unique chemical properties, including multiple oxidation states, radio-nuclear capabilities and various coordination geometries yield value as potential pharmaceuticals. Understanding the interactions between metals and biological systems will prove key for site-specific coordination of new metal-based lead compounds. This study merges the concepts of target coordination with fragment-based drug methodologies, supported by varying the anomalous scattering of rhenium along with infrared spectroscopy, and has identified rhenium metal sites bound covalently with two amino acid types within the model protein. A time-based series of lysozyme-rhenium-imidazole (HEWL-Re-Imi) crystals was analysed systematically over a span of 38 weeks. The main rhenium covalent coordination is observed at His15, Asp101 and Asp119. Weak (i.e. noncovalent) interactions are observed at other aspartic, asparagine, proline, tyrosine and tryptophan side chains. Detailed bond distance comparisons, including precision estimates, are reported, utilizing the diffraction precision index supplemented with small-molecule data from the Cambridge Structural Database. Key findings include changes in the protein structure induced at the rhenium metal binding site, not observed in similar metal-free structures. The binding sites are typically found along the solvent-channel-accessible protein surface. The three primary covalent metal binding sites are consistent throughout the time series, whereas binding to neighbouring amino acid residues changes through the time series. Co-crystallization was used, consistently yielding crystals four days after setup. After crystal formation, soaking of the compound into the crystal over 38 weeks is continued and explains these structural adjustments. It is the covalent bond stability at the three sites, their proximity to the solvent channel and the movement of residues to accommodate the metal that are important, and may prove useful for future radiopharmaceutical development including target modification.

Heterobifunctional small molecules to modulate RNA function.

RNA has diverse cellular functionality, including regulating gene expression, protein translation, and cellular response to stimuli, due to its intricate structures. Over the past decade, small molecules have been discovered that target functional structures within cellular RNAs and modulate their function. Simple binding, however, is often insufficient, resulting in low or even no biological activity. To overcome this challenge, heterobifunctional compounds have been developed that can covalently bind to the RNA target, alter RNA sequence, or induce its cleavage. Herein, we review the recent progress in the field of RNA-targeted heterobifunctional compounds using representative case studies. We identify critical gaps and limitations and propose a strategic pathway for future developments of RNA-targeted molecules with augmented functionalities.

Chemists Invent Drugs and Drugs Save Lives.

Drug molecules are the centrepiece of modern medical therapies, providing relief from pain, combatting infections and providing a myriad of other therapeutic effects. The quest for new and improved drug molecules drives medical research, and the introduction of a new drug frequently becomes a newsworthy event capturing the attention of the press and general public. And yet, misconceptions abound. Often, the general public thinks that drug molecules are designed, created, and invented by physicians rather than chemists - a misunderstanding that is merely one aspect of a widespread general underappreciation of the role of chemistry in the health and socioeconomic well-being of humankind. Chemistry as a discipline needs to change this narrative. Our journals, conferences, societies, mass media presence and social media postings need to better inform the general public about the societal value of chemistry. Though it is an arduous and time-demanding process, chemists, both in academia and industry, invent the drugs that are advancing medical care. We chemists need to do a better job educating policy makers, politicians, opinion leaders and fundraisers about the valuable contributions of chemistry. We need to have people know what we do, and why we became chemists; we need to engage the general public.

Chlordiazepoxide against signalling, receptor and regulatory proteins of breast cancer: a structure-based in-silico approach.

Among the most prevalent forms of cancer are breast, lung, colon-rectum, and prostate cancers, and breast cancer is a major global health challenge, contributing to 2.26 million cases with approximately 685,000 deaths worldwide in 2020 alone, typically beginning in the milk ducts or lobules that produce and transport milk during lactation and it is becoming challenging to treat as the tissues are developing resistance, which makes urgent calls for new multitargeted drugs. The multitargeted drug design provides a better solution, simultaneously targeting multiple pathways, even when the drug resists one, it remains effective for others. In this study, we included four crucial proteins that perform signalling, receptor, and regulatory action, namely- NUDIX Hydrolases, Dihydrofolate Reductase, HER2/neu Kinase and EGFR and performed multitargeted molecular docking studies against human-approved drugs using HTVS, SP and extra precise algorithms and filtered the poses with MM\GBSA, suggested a benzodiazepine derivative chlordiazepoxide, used as an anxiolytic agent, can be a multitargeted inhibitor with docking and MM\GBSA score ranging from - 4.628 to - 7.877 and - 18.59 to - 135.86 kcal/mol, respectively, and the most interacted residues were 6ARG, 6GLU, 3TRP, and 3VAL. The QikProp-based ADMET and DFT computations showed the suitability and stability of the drug candidate followed by 100 ns MD simulation in water and MMGBSA on trajectories, resulting in stable performance and many intermolecular interactions to make the complexes stable, which favours that chlordiazepoxide can be a multitargeted breast cancer inhibitor. However, experimental validation is needed before its use.

What doesn't fit is made to fit: Pim-1 kinase adapts to the configuration of stilbene-based inhibitors.

Recently, we have developed novel Pim-1 kinase inhibitors starting from a dihydrobenzofuran core structure using a computational approach. Here, we report the design and synthesis of stilbene-based Pim-1 kinase inhibitors obtained by formal elimination of the dihydrofuran ring. These inhibitors of the first design cycle, which were obtained as inseparable cis/trans mixtures, showed affinities in the low single-digit micromolar range. To be able to further optimize these compounds in a structure-based fashion, we determined the X-ray structures of the protein-ligand-complexes. Surprisingly, only the cis-isomer binds upon crystallization of the cis/trans-mixture of the ligands with Pim-1 kinase and the substrate PIMTIDE, the binding mode being largely consistent with that predicted by docking. After crystallization of the exclusively trans-configured derivatives, a markedly different binding mode for the inhibitor and a concomitant rearrangement of the glycine-rich loop is observed, resulting in the ligand being deeply buried in the binding pocket.

Quantum-assisted fragment-based automated structure generator (QFASG) for small molecule design: an in vitro study.

Introduction: The significance of automated drug design using virtual generative models has steadily grown in recent years. While deep learning-driven solutions have received growing attention, only a few modern AI-assisted generative chemistry platforms have demonstrated the ability to produce valuable structures. At the same time, virtual fragment-based drug design, which was previously less popular due to the high computational costs, has become more attractive with the development of new chemoinformatic techniques and powerful computing technologies. Methods: We developed Quantum-assisted Fragment-based Automated Structure Generator (QFASG), a fully automated algorithm designed to construct ligands for a target protein using a library of molecular fragments. QFASG was applied to generating new structures of CAMKK2 and ATM inhibitors. Results: New low-micromolar inhibitors of CAMKK2 and ATM were designed using the algorithm. Discussion: These findings highlight the algorithm's potential in designing primary hits for further optimization and showcase the capabilities of QFASG as an effective tool in this field.

De novo drug design as GPT language modeling: large chemistry models with supervised and reinforcement learning.

In recent years, generative machine learning algorithms have been successful in designing innovative drug-like molecules. SMILES is a sequence-like language used in most effective drug design models. Due to data's sequential structure, models such as recurrent neural networks and transformers can design pharmacological compounds with optimized efficacy. Large language models have advanced recently, but their implications on drug design have not yet been explored. Although one study successfully pre-trained a large chemistry model (LCM), its application to specific tasks in drug discovery is unknown. In this study, the drug design task is modeled as a causal language modeling problem. Thus, the procedure of reward modeling, supervised fine-tuning, and proximal policy optimization was used to transfer the LCM to drug design, similar to Open AI's ChatGPT and InstructGPT procedures. By combining the SMILES sequence with chemical descriptors, the novel efficacy evaluation model exceeded its performance compared to previous studies. After proximal policy optimization, the drug design model generated molecules with 99.2% having efficacy pIC50 > 7 towards the amyloid precursor protein, with 100% of the generated molecules being valid and novel. This demonstrated the applicability of LCMs in drug discovery, with benefits including less data consumption while fine-tuning. The applicability of LCMs to drug discovery opens the door for larger studies involving reinforcement-learning with human feedback, where chemists provide feedback to LCMs and generate higher-quality molecules. LCMs' ability to design similar molecules from datasets paves the way for more accessible, non-patented alternatives to drug molecules.

Pyrano[2,3-c]pyrazole fused spirooxindole-linked 1,2,3-triazoles as antioxidant agents: Exploring their utility in the development of antidiabetic drugs via inhibition of α-amylase and DPP4 activity.

Environment-benign, multicomponent synthetic methodologies are vital in modern pharmaceutical research and facilitates multi-targeted drug development via synergistic approach. Herein, we reported green and efficient synthesis of pyrano[2,3-c]pyrazole fused spirooxindole linked 1,2,3-triazoles using a tea waste supported copper catalyst (TWCu). The synthetic approach involves a one-pot, five-component reaction using N-propargylated isatin, hydrazine hydrate, ethyl acetoacetate, malononitrile/ethyl cyanoacetate and aryl azides as model substrates. Mechanistically, the reaction was found to proceed via in situ pyrazolone formation followed by Knoevenagel condensation, azide alkyne cycloaddition and Michael's addition reactions. The molecules were developed using structure-based drug design. The primary goal is to identifying anti-oxidant molecules with potential ability to modulate α-amylase and DPP4 (dipeptidyl-peptidase 4) activity. The anti-oxidant analysis, as determined via DPPH, suggested that the synthesized compounds, A6 and A10 possessed excellent anti-oxidant potential compared to butylated hydroxytoluene (BHT). In contrast, compounds A3, A5, A8, A9, A13, A15, and A18 were found to possess comparable anti-oxidant potential. Among these, A3 and A13 possessed potential α-amylase inhibitory activity compared to the acarbose, and A3 further emerged as dual inhibitors of both DPP4 and α-amylase with anti-oxidant potential. The relationship of functionalities on their anti-oxidant and enzymatic inhibition was explored in context to their SAR that was further corroborated using in silico techniques and enzyme kinetics.