Drug Design Approach Research Articles

Generative artificial intelligence in drug discovery: basic framework, recent advances, challenges, and opportunities.

There are two main ways to discover or design small drug molecules. The first involves fine-tuning existing molecules or commercially successful drugs through quantitative structure-activity relationships and virtual screening. The second approach involves generating new molecules through de novo drug design or inverse quantitative structure-activity relationship. Both methods aim to get a drug molecule with the best pharmacokinetic and pharmacodynamic profiles. However, bringing a new drug to market is an expensive and time-consuming endeavor, with the average cost being estimated at around $2.5 billion. One of the biggest challenges is screening the vast number of potential drug candidates to find one that is both safe and effective. The development of artificial intelligence in recent years has been phenomenal, ushering in a revolution in many fields. The field of pharmaceutical sciences has also significantly benefited from multiple applications of artificial intelligence, especially drug discovery projects. Artificial intelligence models are finding use in molecular property prediction, molecule generation, virtual screening, synthesis planning, repurposing, among others. Lately, generative artificial intelligence has gained popularity across domains for its ability to generate entirely new data, such as images, sentences, audios, videos, novel chemical molecules, etc. Generative artificial intelligence has also delivered promising results in drug discovery and development. This review article delves into the fundamentals and framework of various generative artificial intelligence models in the context of drug discovery via de novo drug design approach. Various basic and advanced models have been discussed, along with their recent applications. The review also explores recent examples and advances in the generative artificial intelligence approach, as well as the challenges and ongoing efforts to fully harness the potential of generative artificial intelligence in generating novel drug molecules in a faster and more affordable manner. Some clinical-level assets generated form generative artificial intelligence have also been discussed in this review to show the ever-increasing application of artificial intelligence in drug discovery through commercial partnerships.

In silico Structure-based Screening of Potential Anticancer Bioactive Natural Constituents from African Natural Products

Introduction: Inhibitors of topoisomerases, essential regulators of cancer development, are promising as cancer treatments. These enzymes regulate DNA topology and eliminate topological constraints during various biological processes, including replication, transcription, and recombination. Nature has continually offered scientists pathways to explore the development of new drugs. Indeed, since ancient times, various plant extracts have been utilized in treating multiple pathologies. Objective: It’s intriguing to diversify the therapeutic classes of natural topoisomerase 1 inhibitors. We aimed to explore the relationship between the toxicity of certain medicinal plants in North Africa and their anti-topoisomerase 1 enzyme activity. This investigation aims to discover potentially valuable compounds for fighting cancer by inhibiting the Topo1 enzyme, enriching the anticancer therapeutic class. Methods: This study has conducted a virtual screening of the African Natural Products Database to identify new scaffolds as topoisomerase 1 inhibitors. Molecular docking as a structure-based drug design approach was selected as one of the best approaches, and the complex code ID: 1K4T was used for this purpose. Results and Discussion: The molecular docking of more than 5790 natural products extracted from this database was docked into the binding site of the above-cited complex using the Modlock optimizer and Moldock score as search and scoring function algorithms, respectively. The top-ranked compounds have been assessed, analyzed, and compared to Topotecan and Irinotecan as reference ligands and drugs. Conclusion: Consequently, the seven natural products have shown a strong affinity to topoisomerase 1 and DNA. They establish a clear link between topoisomerase 1 inhibition and the anticancer activity of their corresponding plant extracts. Therefore, these hits are promising and serve as a base for further development of new topoisomerase 1 inhibitors.

Targeting with Structural Analogs of Natural Products the Purine Salvage Pathway in Leishmania (Leishmania) infantum by Computer-Aided Drug-Design Approaches.

Visceral Leishmaniasis (VL) has a high death rate, with 500,000 new cases and 50,000 deaths occurring annually. Despite the development of novel strategies and technologies, there is no adequate treatment for the disease. Therefore, the purpose of this study is to find structural analogs of natural products as potential novel drugs to treat VL. We selected structural analogs from natural products that have shown antileishmanial activities, and that may impede the purine salvage pathway using computer-aided drug-design (CADD) approaches. For these, we started with the vastly studied target in the pathway, the adenine phosphoribosyl transferase (APRT) protein, which alone is non-essential for the survival of the parasite. Keeping this in mind, we search for a substance that can bind to multiple targets throughout the pathway. Computational techniques were used to study the purine salvage pathway from Leishmania infantum, and molecular dynamic simulations were used to gather information on the interactions between ligands and proteins. Because of its low homology to human proteins and its essential role in the purine salvage pathway proteins network interaction, the findings further highlight the significance of adenylosuccinate lyase protein (ADL) as a therapeutic target. An analog of the alkaloid Skimmianine, N,N-diethyl-4-methoxy-1-benzofuran-6-carboxamide, demonstrated a good binding affinity to APRT and ADL targets, no expected toxicity, and potential for oral route administration. This study indicates that the compound may have antileishmanial activity, which was granted in vitro and in vivo experiments to settle this finding in the future.

De Novo Drug Design Using Transformer-Based Machine Translation and Reinforcement Learning of an Adaptive Monte Carlo Tree Search.

The discovery of novel therapeutic compounds through de novo drug design represents a critical challenge in the field of pharmaceutical research. Traditional drug discovery approaches are often resource intensive and time consuming, leading researchers to explore innovative methods that harness the power of deep learning and reinforcement learning techniques. Here, we introduce a novel drug design approach called drugAI that leverages the Encoder-Decoder Transformer architecture in tandem with Reinforcement Learning via a Monte Carlo Tree Search (RL-MCTS) to expedite the process of drug discovery while ensuring the production of valid small molecules with drug-like characteristics and strong binding affinities towards their targets. We successfully integrated the Encoder-Decoder Transformer architecture, which generates molecular structures (drugs) from scratch with the RL-MCTS, serving as a reinforcement learning framework. The RL-MCTS combines the exploitation and exploration capabilities of a Monte Carlo Tree Search with the machine translation of a transformer-based Encoder-Decoder model. This dynamic approach allows the model to iteratively refine its drug candidate generation process, ensuring that the generated molecules adhere to essential physicochemical and biological constraints and effectively bind to their targets. The results from drugAI showcase the effectiveness of the proposed approach across various benchmark datasets, demonstrating a significant improvement in both the validity and drug-likeness of the generated compounds, compared to two existing benchmark methods. Moreover, drugAI ensures that the generated molecules exhibit strong binding affinities to their respective targets. In summary, this research highlights the real-world applications of drugAI in drug discovery pipelines, potentially accelerating the identification of promising drug candidates for a wide range of diseases.

Computational fragment-based drug design of potential Glo-I inhibitors

In this study, a fragment-based drug design approach, particularly de novo drug design, was implemented utilising three different crystal structures in order to discover new privileged scaffolds against glyoxalase-I enzyme as anticancer agents. The fragments were evoluted to indicate potential inhibitors with high receptor affinities. The resulting compounds were served as a benchmark for choosing similar compounds from the ASINEX® database by applying different computational ligand-based drug design techniques. Afterwards, the selection of potential hits was further aided by various structure-based approaches. Then, 14 compounds were purchased, and tested in vitro against Glo-I enzyme. Of the tested 14 hits, the biological screening results showed humble activities where the percentage of Glo-I inhibition ranged from 0–18.70 %. Compound 19 and compound 28, whose percentage of inhibitions are 18.70 and 15.80%, respectively, can be considered as hits that need further optimisation in order to be converted into lead-like compounds.

Computer Aided Structure-Based Drug Design of Novel SARS-CoV-2 Main Protease Inhibitors: Molecular Docking and Molecular Dynamics Study

Severe acute respiratory syndrome Coronavirus 2 (SARS-CoV-2) virus syndrome caused the recent outbreak of COVID-19 disease, the most significant challenge to public health for decades. Despite the successful development of vaccines and promising therapies, the development of novel drugs is still in the interests of scientific society. SARS-CoV-2 main protease Mpro is one of the key proteins for the lifecycle of the virus and is considered an intriguing target. We used a structure-based drug design approach as a part of the search of new inhibitors for SARS-CoV-2 Mpro and hence new potential drugs for treating COVID-19. Four structures of potential inhibitors of (4S)-2-(2-(1H-imidazol-5-yl)ethyl)-4-amino-2-(1,3-dihydroxypropyl)-3-hydroxy-5-(1H-imidazol-5-yl)pentanal (L1), (2R,4S)-2-((1H-imidazol-4-yl)methyl)-4-chloro-8-hydroxy-7-(hydroxymethyl)octanoic acid (L2), 1,9-dihydroxy-6-(hydroxymethyl)-6-(((1S)-1,7,7-trimethylbicyclo [2.2.1]heptan-2-yl)amino)nonan-4-one (L3), and 2,4,6-tris((4H-1,2,4-triazol-3-yl)amino)benzonitrile (L4) were modeled. Three-dimensional structures of ligand–protein complexes were modeled and their potential binding efficiency proved. Docking and molecular dynamic simulations were performed for these compounds. Detailed trajectory analysis of the ligands’ binding conformation was carried out. Binding free energies were estimated by the MM/PBSA approach. Results suggest a high potential efficiency of the studied inhibitors.

Rational design for novel heterocyclic based Donepezil analogs for Alzheimer’s disease: an in silico approach

Alzheimer’s disease (AD) is a progressive neurodegenerative disease and has devastating impacts on the elderly population. During the last two decades, there has been a significant focus on developing effective and safe treatments for AD. Acetylcholinesterase (AChE) has been identified as one of the primary therapeutic targets for developing drug candidates for AD. However, there is still a need for more efficient therapies. In this study, our aim is to design a new series of heterocyclic-based AChE inhibitors inspired by a standard drug. Here, we carried out molecular docking, drug-likeliness characteristics, and molecular dynamics (MD) to predict important pharmacophore features and understand the inhibitory mechanism of the designed inhibitors towards the AChE. We have designed 112 new derivatives by replacing the piperidine moiety of Donepezil with the different five and six-membered heterocyclic rings and selected 15 compounds that show higher or comparable docking scores as compared to standard Donepezil and pose no risk for carcinogenicity. Furthermore, MD results imply the structural stability of the selected docked complexes and seven exhibit a stronger binding affinity towards the AChE than Donepezil. Thus, heterocyclic-based derivatives based on oxazole, pyrazole, and tetrahydropyran may be potential therapeutic candidates for AD. Our structure-based drug design approach allows us to identify and gain insight into the structural stability of the inhibitor-protein complex and the inhibition mechanism of the newly designed inhibitors. The present finding might be an initial selection for developing a new inhibitor for AD and provide a direction for further experiments on its biological activities. Communicated by Ramaswamy H. Sarma

Elucidating anti-sclerostin mechanism of baicalein using LRP6-Sclersotin complex of canonical Wnt/β-catenin signaling pathway

Flavonoids are polyphenolic compounds produced by plants as secondary metabolites that are known to exhibit wide range of pharmaceutical properties. Flavonoids from different medicinal plants have been used in traditional medicine to treat several musculoskeletal disorders for centuries. Of the numerous flavonoids, baicalein from Oroxylum indicum has a well-documented protective effect in skeletal health. However, studies into its influence on the canonical Wnt/β-catenin signaling pathway for musculoskeletal disorders remain limited. With the results of our previous study, the current research investigated the molecular mechanism of baicalein to inhibit the interaction between LRP6 and sclerostin to activate the canonical Wnt/β-catenin signaling pathway. Molecular docking revealed that baicalein docks between LRP6 and sclerostin with a binding energy of −8.4 kcal/mol and interacts with key binding residues of both the proteins. The molecular dynamics simulations predicted the stability of baicalein through 100 ns with more conformational changes observed in sclerostin than LRP6 especially in and around the PNAIG motif of loop-2 region, hinting at a possible inhibitory effect of baicalein over sclerostin. The findings of this research could pave the way for novel drug design approaches while promoting the use of natural flavonoids as potential therapeutics for musculoskeletal disorders. Communicated by Ramaswamy H. Sarma

Discovery of the First-in-Class RORγ Covalent Inhibitors for Treatment of Castration-Resistant Prostate Cancer.

Nuclear receptor receptor-related orphan receptor γ (RORγ) is a ligand-dependent transcription factor and has been established as a key player in castration-resistant prostate cancers (CRPC) by driving androgen receptor (AR) overexpression, representing a potential therapeutical target for advanced prostate cancers. Here, we report the identification of the first-in-class RORγ covalent inhibitor 29 via the structure-based drug design approach following structure-activity relationship (SAR) exploration. Mass spectrometry assay validated its covalent inhibition mechanism. Compound 29 significantly inhibited RORγ transcriptional activity and remarkably suppressed the expression levels of AR and AR-targeted genes. Compound 29 also exhibited much superior activity in inhibiting the proliferation and colony formation and inducing apoptosis of the CRPC cell lines relative to the positive control 2 and noncovalent control 33. Importantly, it markedly suppressed the tumor growth in a 22Rv1 mouse tumor xenograft model with good safety. These results clearly demonstrate that 29 is a highly potent and selective RORγ covalent inhibitor.

HTLV-1 reverse transcriptase homology model provides structural basis for sensitivity to existing nucleoside/nucleotide reverse transcriptase inhibitors

The human T-lymphotropic virus type 1 (HTLV-1) infects millions of people globally and is endemic to various resource-limited regions. Infections persist for life and are associated with increased susceptibility to opportunistic infections and severe diseases including adult T cell leukemia/lymphoma and HTLV-1-associated myelopathy-tropical spastic paraparesis. No HTLV-1-specific anti-retrovirals have been developed and it is unclear whether existing anti-retrovirals developed for treatment of human immunodeficiency virus (HIV) have efficacy against HTLV-1. To understand the structural basis for therapeutic binding, homology modelling and machine learning were used to develop a structural model of the HTLV-1 reverse transcriptase. With this, molecular docking experiments using a panel of FDA-approved inhibitors of viral reverse transcriptases to assess their capacity for binding, and in turn, inhibition. Importantly, nucleoside/nucleotide reverse transcriptase inhibitor but not non-nucleoside reverse transcriptase inhibitors were predicted to bind the HTLV-1 reverse transcriptase, with similar affinity to HIV-1 reverse transcriptase. By strengthening the rationale for clinical testing of therapies such as tenofovir alafenamide, zidovudine, lamivudine, and azvudine for treatment of HTLV-1, this study has demonstrated the power of in silico structural biology approaches in drug design and therapeutic testing.

Ligand coupling mechanism of the human serotonin transporter differentiates substrates from inhibitors

The presynaptic serotonin transporter (SERT) clears extracellular serotonin following vesicular release to ensure temporal and spatial regulation of serotonergic signalling and neurotransmitter homeostasis. Prescription drugs used to treat neurobehavioral disorders, including depression, anxiety, and obsessive-compulsive disorder, trap SERT by blocking the transport cycle. In contrast, illicit drugs of abuse like amphetamines reverse SERT directionality, causing serotonin efflux. Both processes result in increased extracellular serotonin levels. By combining molecular dynamics simulations with biochemical experiments and using a homologous series of serotonin analogues, we uncovered the coupling mechanism between the substrate and the transporter, which triggers the uptake of serotonin. Free energy analysis showed that only scaffold-bound substrates could initiate SERT occlusion through attractive long-range electrostatic interactions acting on the bundle domain. The associated spatial requirements define substrate and inhibitor properties, enabling additional possibilities for rational drug design approaches.

Design of new small molecules derived from indolin-2-one as potent TRKs inhibitors using a computer-aided drug design approach

Tropomyosin receptor kinase (TRKs) enzymes are responsible for cancers associated with the neurotrophic tyrosine kinase receptor gene fusion and are identified as effective targets for anticancer drug discovery. A series of small-molecule indolin-2-one derivatives showed remarkable biological activity against TRKs enzymatic activity. These small molecules could have an excellent profile for pharmaceutical application in the treatment of cancers caused by TRKs activity. The aim of this study is to modify the structure of these molecules to obtain new molecules with improved TRK inhibitory activity and pharmacokinetic properties favorable to the design of new drugs. Based on these series, we carried out a 3D-QSAR study. As a result, robust and reliable CoMFA and CoMSIA models are developed and applied to the design of 11 new molecules. These new molecules have a biological activity superior to the most active molecule in the starting series. The eleven designed molecules are screened using drug-likeness, ADMET proprieties, molecular docking, and MM-GBSA filters. The results of this screening identified the T1, T3, and T4 molecules as the best candidates for strong inhibition of TRKs enzymatic activity. In addition, molecular dynamics simulations are performed for TRK free and complexed with ligands T1, T3, and T4 to evaluate the stability of ligand-protein complexes over the simulation time. On the other hand, we proposed experimental synthesis routes for these newly designed molecules. Finally, the designed molecules T1, T2, and T3 have great potential to become reliable candidates for the conception of new drug inhibitors of TRKs. Communicated by Ramaswamy H. Sarma

Rational design and in vitro testing of new urease inhibitors to prevent urinary catheter blockage.

Catheter associated urinary tract infections (CAUTI) caused by urease-positive organisms can lead to catheter blockage: urease metabolizes urea in urine to ammonia causing an increase in pH and hence precipitation of struvite and apatite salts into the catheter lumen and bladder leading to blockage. Acetohydroxamic acid (AHA) is the only urease inhibitor currently approved for patient use, however, it is rarely used owing to its side effects. Here, we report the identification and development of new urease inhibitors discovered using a rational in silico drug design approach. A series of compounds were designed, the compounds were screened and filtered to identify three compounds which were tested in in vitro urease activity assays. N,N'-Bis(3-pyridinylmethyl)thiourea (Bis-TU) outperformed AHA in activity assays and was tested in an in vitro bladder model, where it significantly extended the lifetime of the catheter compared to AHA. Bis-TU was delivered via a diffusible balloon catheter directly to the site of activity, thus demonstrating localized drug delivery. This cost-effective drug design approach allowed the identification of a potent urease inhibitor, which could be improved through iterative repeats of the method, and the process of design could be utilized to target other diseases.

Targeting Leishmania infantum Mannosyl-oligosaccharide glucosidase with natural products: potential pH-dependent inhibition explored through computer-aided drug design.

Visceral Leishmaniasis (VL) is a serious public health issue, documented in more than ninety countries, where an estimated 500,000 new cases emerge each year. Regardless of novel methodologies, advancements, and experimental interventions, therapeutic limitations, and drug resistance are still challenging. For this reason, based on previous research, we screened natural products (NP) from Nuclei of Bioassays, Ecophysiology, and Biosynthesis of Natural Products Database (NuBBEDB), Mexican Compound Database of Natural Products (BIOFACQUIM), and Peruvian Natural Products Database (PeruNPDB) databases, in addition to structural analogs of Miglitol and Acarbose, which have been suggested as treatments for VL and have shown encouraging action against parasite's N-glycan biosynthesis. Using computer-aided drug design (CADD) approaches, the potential inhibitory effect of these NP candidates was evaluated by inhibiting the Mannosyl-oligosaccharide Glucosidase Protein (MOGS) from Leishmania infantum, an enzyme essential for the protein glycosylation process, at various pH to mimic the parasite's changing environment. Also, computational analysis was used to evaluate the Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) profile, while molecular dynamic simulations were used to gather information on the interactions between these ligands and the protein target. Our findings indicated that Ocotillone and Subsessiline have potential antileishmanial effects at pH 5 and 7, respectively, due to their high binding affinity to MOGS and interactions in the active center. Furthermore, these compounds were non-toxic and had the potential to be administered orally. This research indicates the promising anti-leishmanial activity of Ocotillone and Subsessiline, suggesting further validation through in vitro and in vivo experiments.

Identification of SARS-CoV-2 Mpro inhibitors through deep reinforcement learning for de novo drug design and computational chemistry approaches.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused a global pandemic of coronavirus disease (COVID-19) since its emergence in December 2019. As of January 2024, there has been over 774 million reported cases and 7 million deaths worldwide. While vaccination efforts have been successful in reducing the severity of the disease and decreasing the transmission rate, the development of effective therapeutics against SARS-CoV-2 remains a critical need. The main protease (Mpro) of SARS-CoV-2 is an essential enzyme required for viral replication and has been identified as a promising target for drug development. In this study, we report the identification of novel Mpro inhibitors, using a combination of deep reinforcement learning for de novo drug design with 3D pharmacophore/shape-based alignment and privileged fragment match count scoring components followed by hit expansions and molecular docking approaches. Our experimentally validated results show that 3 novel series exhibit potent inhibitory activity against SARS-CoV-2 Mpro, with IC50 values ranging from 1.3 μM to 2.3 μM and a high degree of selectivity. These findings represent promising starting points for the development of new antiviral therapies against COVID-19.

Pillar[5]arene/albumin biosupramolecular systems for simultaneous native protein preservation and encapsulation of a water-soluble substrate.

The growing resistance of pathogens, bacteria, viruses, and fungi to a number of drugs has encouraged researchers to use natural and synthetic biomimetic systems to overcome this challenge. Multicomponent systems are an attractive approach for drug design and multitarget therapy. In this study, we report the assembly of a three-component (pillar[5]arene, bovine serum albumin, and methyl orange) biosupramolecular system as a potential drug delivery system. We estimated the cytotoxic activity and transfection ability of pillar[5]arene derivatives and investigated the effect of the nature of macrocycle functions (L-phenylalanine, glycine, L-alanine) on the native conformation of serum albumin in a three-component system. NMR, UV-vis, fluorescence, CD spectroscopy, DLS, and molecular docking studies were performed in order to confirm the structure and possible pillar[5]arene/bovine serum albumin/methyl orange interactions occurring during the association process. Results indicate that pillar[5]arene with L-phenylalanine fragments retains the native form of BSA to the maximum extent and forms more stable associates.

Feasibility of the inhibitor development for cancer: A systematic approach for drug design.

The traditional Chinese medicine (TCM) bupleurum-ginger-licorice formula presents significant anti-cancer effects, but its active ingredients and inhibitory mechanism remain unclear. In this work, the core effective ingredient quercetin and its signal transducer and activator of transcription 3 (Stat3) receptor both were identified by network pharmacology. Quercetin is a low-toxicity, non-carcinogenic flavonoid with antioxidant, anti-inflammatory and anticancer activities, which is widely distributed in edible plants. Stat3 can bind to specific DNA response elements and serves as a transcription factor to promote the translation of some invasion/migration-related target genes, considered as a potential anticancer target. Here, molecular docking and molecular dynamics (MD) simulation both were used to explore molecular recognition of quercetin with Stat3. The results show that quercetin impairs DNA transcription efficiency by hindering Stat3 dimerization, partially destroying DNA conformation. Specifically, when the ligand occupies the SH2 cavity of the enzyme, spatial rejection is not conductive to phosphokinase binding. It indirectly prevents the phosphorylation of Y705 and the formation of Stat3 dimer. When the inhibitor binds to the DT1005 position, it obviously shortens the distance between DNA and DBD, enhances their binding capacity, and thereby reduces the degree of freedom required for transcription. This work not only provides the binding modes between Stat3 and quercetin, but also contributes to the optimization and design of such anti-cancer inhibitors.

Antiviral Activity, Pharmacoinformatics, Molecular Docking, and Dynamics Studies of Azadirachta indica Against Nipah Virus by Targeting Envelope Glycoprotein: Emerging Strategies for Developing Antiviral Treatment.

The Nipah virus (NiV) belongs to the Henipavirus genus is a serious public health concern causing numerous outbreaks with higher fatality rate. Unfortunately, there is no effective medication available for NiV. To investigate possible inhibitors of NiV infection, we used in silico techniques to discover treatment candidates in this work. As there are not any approved treatments for NiV infection, the NiV-enveloped attachment glycoprotein was set as target for our study, which is responsible for binding to and entering host cells. Our in silico drug design approach included molecular docking, post-docking molecular mechanism generalised born surface area (MM-GBSA), absorption, distribution, metabolism, excretion/toxicity (ADME/T), and molecular dynamics (MD) simulations. We retrieved 418 phytochemicals associated with the neem plant (Azadirachta indica) from the IMPPAT database, and molecular docking was used to ascertain the compounds' binding strength. The top 3 phytochemicals with binding affinities of -7.118, -7.074, and -6.894 kcal/mol for CIDs 5280343, 9064, and 5280863, respectively, were selected for additional study based on molecular docking. The post-docking MM-GBSA of those 3 compounds was -47.56, -47.3, and -43.15 kcal/mol, respectively. As evidence of their efficacy and safety, all the chosen drugs had favorable toxicological and pharmacokinetic (Pk) qualities. We also performed MD simulations to confirm the stability of the ligand-protein complex structures and determine whether the selected compounds are stable at the protein binding site. All 3 phytochemicals, Quercetin (CID: 5280343), Cianidanol (CID: 9064), and Kaempferol (CID: 5280863), appeared to have outstanding binding stability to the target protein than control ribavirin, according to the molecular docking, MM-GBSA, and MD simulation outcomes. Overall, this work offers a viable approach to developing novel medications for treating NiV infection.

Implementation of multi-criteria decision making for the ranking of drugs used to treat bone-cancer

<abstract> <p>The concept of "topological index" refers to a numerical value determined by the structure of a chemical network. It serves to determine the physicochemical and biological properties of diverse medications, offering a more precise depiction of the theoretical properties of organic materials, this is achieved through the utilization of degree-based topological indices. Because of the development of resistance to existing treatments and the unpleasant side effects associated with some current drugs, the hunt for new drugs remains a priority. In drug discovery, QSPR approaches are widely used to predict, from a chemical structure, the biological activity of potential novel drugs. Researchers can prioritize compounds for synthesis and optimize them to improve potency, preference, and other desired attributes by establishing a correlation between chemical features and biological activity. Rational drug design approaches incorporate research methodologies such as quantitative structure-activity relationships (QSAR) and quantitative structure-property relationships (QSPR), along with decision-making strategies. The goal of these strategies is to improve the biological activity and physicochemical qualities of existing leads. This research includes mathematical modeling of drug mechanisms utilizing multiple-criteria decision analysis and QSPR analysis. Furthermore, using decision-making techniques, I can determine the order of production for various drugs used to treat bone cancer based on their examination using QSPR analysis and topological indices.</p> </abstract>

Insilico exploration C. koseri ATP synthase inhibitors by pharmacophore-based virtual screening, molecular docking and MD simulation.

Citrobacter koseri is a gram-negative rod that causes infections in people who have significant comorbidities and are immunocompromised. Antibiotic-resistant strains are becoming more common, which complicates infection treatment and highlights the need for innovative, effective drugs to fight these resistant strains. The enzyme complex ATP synthase participates in the adenosine triphosphate (ATP) synthesis, the fundamental energy currency of cells. This study used Computer-Aided Drug Design approaches to identify potential inhibitors of C. koseri ATP synthase. SWISS-MODEL was used to predict the 3D structure of C. koseri ATP synthase. A ligand-based pharmacophore model was developed using chemical features of ampicillin. Following ligand-based virtual screening across nine databases, the 2043 screened hits were docked to the ATP synthase active site using the standard precision mode of the glide tool. Based on their binding affinities, the top ten compounds were selected for additional investigation. The binding affinities of the chosen compounds ranged from -10.021 to -8.452 kcal/mol. The top four compounds (PubChem-25230613, PubChem-74936833, CHEMBL263035, PubChem-44208924) with the best ADMET characteristics and binding modes were chosen. Thus, the feasible binding mechanisms of the selected compounds were subjected to stability analysis using the MD Simulation study, which revealed the compounds' stability as potent inhibitors within the protein binding pocket. This computational approach provides important insights into the rational design of novel therapeutics and emphasizes the importance of targeting essential metabolic pathways when combating antibiotic-resistant pathogens. Future experimental validation and optimization of the identified inhibitors is required to determine their efficacy and safety profiles for clinical use.