Computer-aided Drug Design Research Articles

Hierarchical Virtual Screening of SARS-CoV-2 Main Protease Potential Inhibitors: Similarity Search, Pharmacophore Modeling, and Molecular Docking Study

Background: The outbreak of COVID-19 caused by severe acute respiratory syndrome coronavirus2 (SARS-CoV-2) resulted in a widespread pandemic. Various approaches involved the repositioning of antiviral remedies and other medications. Several therapies, including oral antiviral treatments, represent some approaches to adapting to the long existence of the COVID-19 pandemic. In silico studies provide valuable insights throughout drug discovery and development in compliance with global efforts to overcome the pandemic. The main protease is an essential target in the viral cycle. Computer-aided drug design accelerates the identification of potential treatments, including oral therapy. Aims: This work aims to identify potential SARS-CoV-2 main protease inhibitors using different aspects of in silico approaches. Methods: In this work, we conducted a hierarchical virtual screening of SARS-CoV-2 main protease inhibitors. A similarity search was conducted to screen molecules similar to the inhibitor PF-07321332. Concurrently, structure-based pharmacophores, besides ligand-based pharmacophores, were derived. A drug-likeness filter filtered the compounds retrieved from similarity search and pharmacophore modeling before being subjected to molecular docking. The candidate molecules that showed higher affinity to the main protease than the reference inhibitor were further filtered by absorption, distribution, metabolism, and excretion (ADME) parameters. Results: According to binding affinity and ADME analysis, four molecules (CHEMBL218022, PubChem163362029, PubChem166149100, and PubChem 162396459) were prioritized as promising hits. The compounds above were not reported before; no previous experimental studies and bioactive assays are available. Conclusion: Our time-saving approach represents a strategy for discovering novel SARS-CoV- 2 main protease inhibitors. The ultimate hits may be nominated as leads in discovering novel SARS-CoV-2 main protease inhibitors.

Recent Advances in Nanocarrier-based Approaches to Atopic Dermatitis and Emerging Trends in Drug Development and Design.

Atopic dermatitis (AD), commonly known as Eczema, is a non-communicable skin condition that tends to become chronic. The deteriorating immunological abnormalities are marked by mild to severe erythema, severe itching, and recurrent eczematous lesions. Different pharmacological approaches are used to treat AD. The problem with commercial topical preparations lies in the limitation of skin atrophy, systemic side effects, and burning sensation that decreases patient compliance. The carrier-based system promises to eliminate these shortcomings; thus, a novel approach to treating AD is required. Liposomes, microemulsions, solid lipid nanoparticles (SLNs), nanoemulsions, etc., have been developed recently to address this ailment. Despite extensive research in the development method and various techniques, it has been challenging to demonstrate the commercial feasibility of these carrier- based systems, which illustrates a gap among the different research areas. Further, different soft wares and other tools have proliferated among biochemists as part of a cooperative approach to drug discovery. It is crucial in designing, developing, and analyzing processes in the pharmaceutical industry and is widely used to reduce costs, accelerate the development of biologically innovative active ingredients, and shorten the development time. This review sheds light on the compilation of extensive efforts to combat this disease, the product development processes, commercial products along with patents in this regard, numerous options for each step of computer-aided drug design, including in silico pharmacokinetics, pharmacodynamics, and toxicity screening or predictions that are important in finding the drug-like compounds.

Revolutionizing Drug Discovery: A Comprehensive Review of Computer-Aided Drug Design Approaches

Abstract: Computer-Aided Drug Design (CADD) has significantly advanced the drug discovery process, offering tools to enhance efficiency and reduce costs. This review explores essential CADD methodologies, including molecular docking, virtual screening, ADMET profiling, homology modeling, and Quantitative Structure-Activity Relationship (QSAR) models. Molecular docking predicts interactions between drugs and targets, while virtual screening evaluates large compound libraries to identify promising candidates. ADMET profiling assesses pharmacokinetic and toxicological properties early in development. Homology modeling constructs three-dimensional protein models to aid target identification, and QSAR models predict biological activities based on chemical structures. These integrated approaches streamline drug development, providing a robust framework for modern pharmaceutical research.

Computational approach for drug discovery against Gardnerella vaginalis in quest for safer and effective treatments for bacterial vaginosis

Bacterial vaginosis (BV), primarily attributed to Gardnerella vaginalis, poses significant challenges due to antibiotic resistance and suboptimal treatment outcomes. This study presents an integrated approach to identify potential drug targets and screen compounds against this bacterium by leveraging a computational methodology. Subtractive proteomics of the reference strain ASM286196v1/UMB0386 (assembly accession: GCA_002861965.1) facilitated the prioritization of proteins with essential bacterial functions and pathways as potential drug targets. We selected 3-deoxy-7-phosphoheptulonate synthase (aroG gene product; also known as DAHP synthase) for downstream analysis. Molecular docking was employed in PyRx (AutoDock Vina) to predict binding affinities between aroG inhibitors from the ZINC database and 3-deoxy-7-phosphoheptulonate synthase. Molecular dynamics simulations of 100 ns, using GROMACS, validated the stability of drug-target interactions. Additionally, ADMET profiling aided in the selection of compounds with favorable pharmacokinetic properties and safety profile for human hosts. PBPK profiling showed that ZINC98088375 had the highest bioavailability and efficient systemic circulation. Conversely, ZINC5113880 demonstrated the lowest absorption rate (39.661%). Moreover, cirrhosis, steatosis, and renal impairment appeared to influence blood concentration of the drug, impacting bioavailability. The integrative –omics approach utilized in this study underscores the potential of computer-aided drug design and offers a rational strategy for targeted inhibitor discovery against G. vaginalis. The strategy is an attempt to address the limitations of current BV treatments, including antibiotic resistance, and pave way for the development of safer and more effective therapeutics.

Quantitative structure-activity relationships modelling in antimicrobial peptides design

Antimicrobial peptides are antimicrobial agents with high bioactivity and low bacterial resistance, which inspire scientists to recognize AMPs as appealing candidates for microbial infection treatments. QSAR modeling is one of the most important techniques in computer-aided drug design that is typically beneficial to promoting the design of AMPs. This paper presents an overview about breakthroughs and achievements in AMPs designed by QSAR modeling in recent 10 years, and primarily talks about 6 successful AMPs discoveries covering different QSAR models and solving various antimicrobial problems such as biological activity, cytotoxicity and bacterial resistance, which is responsible for future developments in new generation of AMPs and optimization in QSAR modeling.

In silico drug repurposing using molecular docking and dynamics to target the protein interaction between the SARS-CoV-2 S-glycoprotein and the ACE2 receptor.

Background: The protein interaction between the viral surface S-glycoprotein and the host angiotensin converting enzyme-2 receptor (ACE2) is key to the virulent nature of SARS-CoV-2. The potential role that effective drug repurposing strategies may have to help stem the impact of future outbreaks has been brought to light in the recent COVID-19 pandemic. This study outlines a comprehensive approach towards in-silico drug discovery which aims to identify hit agents that can be suitably translated into a clinical setting. Methods: We use two different computational platforms to analyze the viral S-glycoprotein in its bound conformational state to the ACE2 receptor. We employed a comprehensive screening approach to shortlist compounds capable of binding to the viral target interface and corroborated these findings using both Schrödinger's Glide and AutoDock Vina. Molecular dynamic simulation studies further verified the stability of the interaction at the viral-host protein interface. Results: Lymecycline, pentagalloylglucose, polydatin, and hexoprenaline were identified as prime candidates for further studies given the robust and stable nature of their interaction at the viral-host interface and relevance for clinical testing. These agents were shown in a 100-nanosecond simulation trajectory to favorably disrupt key binding interactions at the viral-host interface and may potentially inhibit viral entry into host cells. In all hit molecules it was observed that inhibiting the interaction with the following key viral binding residues: Lys17, Gly496, Tyr 505, and key host residues: His34, Asp38, Lys353, played a critical role toward the inhibition of the viral-host protein interaction. Conclusions: Our study is unique in its comprehensive approach to identify agents that can bind to the S-glycoprotein-ACE2 interface using multiple computational platforms. Among the hit compounds shortlisted in this study, both lymecycline and hexoprenaline may be considered as candidates for preliminarily clinical studies to assess their therapeutic potential in the management of COVID-19 infections.

4-Hydroxyphenylpyruvate Dioxygenase Inhibitors: From Molecular Design to Synthesis.

Weed resistance is a critical issue in crop production. Among the known herbicides, 4-hydroxyphenylpyruvate dioxygenase (HPPD) inhibitors are crucial for addressing weed resistance. HPPD inhibitors constitute a pivotal aspect of contemporary crop protection strategies. The advantages of these herbicides are their broad weed spectrum, flexible application, and excellent compatibility with other herbicides. They also exhibit satisfactory crop selectivity and low toxicity and are environmentally friendly. An increasing number of new HPPD inhibitors have been designed by combining computer-aided drug design with conventional design approaches. Herein, the molecular design and structural features of innovative HPPD inhibitors are reviewed to guide the development of new HPPD inhibitors possessing an enhanced biological efficacy.

Enhancing SILCS-MC via GPU Acceleration and Ligand Conformational Optimization with Genetic and Parallel Tempering Algorithms.

In the domain of computer-aided drug design, achieving precise and accurate estimates of ligand-protein binding is paramount in the context of screening extensive drug libraries and performing ligand optimization. A fundamental aspect of the SILCS (site identification by ligand competitive saturation) methodology lies in the generation of comprehensive 3D free-energy functional group affinity maps (FragMaps), encompassing the entirety of the target molecule structure. These FragMaps offer an intricate landscape of functional group affinities across the protein, bilayer, or RNA, acting as the basis for subsequent SILCS-Monte Carlo (MC) simulations wherein ligands are docked to the target molecule. To augment the efficiency and breadth of ligand sampling capabilities, we implemented an improved SILCS-MC methodology. By harnessing the parallel computing capability of GPUs, our approach facilitates concurrent calculations over multiple ligands and binding sites, markedly enhancing the computational efficiency. Moreover, the integration of a genetic algorithm (GA) with MC allows us to employ an evolutionary approach to perform ligand sampling, assuring enhanced convergence characteristics. In addition, the potential utility of parallel tempering (PT) to improve sampling was investigated. Implementation of SILCS-MC on GPU architecture is shown to accelerate the speed of SILCS-MC calculations by over 2-orders of magnitude. Use of GA and PT yield improvements over Markov-chain MC, increasing the precision of the resultant docked orientations and binding free energies, though the extent of improvements is relatively small. Accordingly, significant improvements in speed are obtained through the GPU implementation with minor improvements in the precision of the docking obtained via the tested GA and PT algorithms.

Rational In Silico Design of Selective TMPRSS6 Peptidomimetic Inhibitors via Exploitation of the S2 Subpocket.

TMPRSS6 is a potential therapeutic target for the treatment of iron overload due to its role in regulating levels of hepcidin. Although potent TMPRSS6 inhibitors have been previously developed, their lack of specificity requires optimization to avoid potential side effects before pursuing preclinical development with in vivo models. Here, using computer-aided drug design based on a TMPRSS6 homology model, we reveal that the S2 position of TMPRSS6 offers a potential avenue to achieve selectivity against other members of the TTSP family. Accordingly, we synthesized novel peptidomimetic molecules containing lipophilic amino acids at the P2 position to exploit this unexplored pocket. This enabled us to identify TMPRSS6-selective small molecules with low nanomolar affinity. Finally, pharmacokinetic parameters were determined, and a compound was found to be potent in cellulo toward its primary target while retaining TTSP-subtype selectivity and showing no signs of alteration in in vitro TEER experiments.

How to Sample Dozens of Substitutions per Site with λ Dynamics.

Alchemical free energy methods are useful in computer-aided drug design and computational protein design because they provide rigorous statistical mechanics-based estimates of free energy differences from molecular dynamics simulations. λ dynamics is a free energy method with the ability to characterize combinatorial chemical spaces spanning thousands of related systems within a single simulation, which gives it a distinct advantage over other alchemical free energy methods that are mostly limited to pairwise comparisons. Recently developed methods have improved the scalability of λ dynamics to perturbations at many sites; however, the size of chemical space that can be explored at each individual site has previously been limited to fewer than ten substituents. As the number of substituents increases, the volume of alchemical space corresponding to nonphysical alchemical intermediates grows exponentially relative to the size corresponding to the physical states of interest. Beyond nine substituents, λ dynamics simulations become lost in an alchemical morass of intermediate states. In this work, we introduce new biasing potentials that circumvent excessive sampling of intermediate states by favoring sampling of physical end points relative to alchemical intermediates. Additionally, we present a more scalable adaptive landscape flattening algorithm for these larger alchemical spaces. Finally, we show that this potential enables more efficient sampling in both protein and drug design test systems with up to 24 substituents per site, enabling, for the first time, simultaneous simulation of all 20 amino acids.

Computer-aided drug design supporting sunscreen research: a showcase study using previously synthesized hybrid UV filter-antioxidant compounds.

Although quantum mechanical calculations have proven effective in accurately predicting UV absorption and assessing the antioxidant potential of compounds, the utilization of computer-aided drug design (CADD) to support sustainable synthesis research of new sunscreen active ingredients remains an area with limited exploration. Furthermore, there are ongoing concerns about the safety and effectiveness of existing sunscreens. Therefore, it remains crucial to investigate photoprotection mechanisms and develop enhanced strategies for mitigating the harmful effects of UVR exposure, improving both the safety and efficacy of sunscreen products. A previous study conducted synthesis research on eight novel hybrid compounds (I-VIII) for use in sunscreen products by molecular hybridization of trans-resveratrol (RESV), avobenzone (AVO), and octinoxate (OMC). Herein, time-dependent density functional theory (TD-DFT) calculations performed in the gas phase on the isolated hybrid compounds (I-VIII) proved to reproduce the experimental UV absorption. Resveratrol-avobenzone structure-based hybrids (I-IV) present absorption maxima in the UVB range with slight differences between them, while resveratrol-OMC structure-based hybrids (V-VIII) showed main absorption in the UVA range. Among RESV-OMC hybrids, compounds V and VI exhibited higher UV absorption intensity, and compound VIII stood out for its broad-spectrum coverage in our simulations. Furthermore, both in silico and in vitro analyses revealed that compounds VII and VIII exhibited the highest antioxidant activity, with compound I emerging as the most reactive antioxidant within RESV-AVO hybrids. The study suggests a preference for the hydrogen atom transfer (HAT) mechanism over single-electron transfer followed by proton transfer (SET-PT) in the gas phase. With a strong focus on sustainability, this approach reduces costs and minimizes effluent production in synthesis research, promoting the eco-friendly development of new sunscreen active ingredients. The SPARTAN'20 program was utilized for the geometry optimization and energy calculations of all compounds. Conformer distribution analysis was performed using the Merck molecular force field 94 (MMFF94), and geometry optimization was carried out using the parametric method 6 (PM6) followed by density functional theory (DFT/B3LYP/6-31G(d)). The antioxidant behavior of the hybrid compounds (I-VIII) was determined using the highest occupied molecular orbital (εHOMO) and the lowest unoccupied molecular orbital (εLUMO) energies, as well as the bond dissociation enthalpy (BDE), ionization potential (IP), and proton dissociation enthalpy (PDE) values, all calculated at the same level of structural optimization. TD-DFT study is carried out to calculate the excitation energy using the B3LYP functional with the 6-31G(d) basis set. The calculated transitions were convoluted with a Gaussian profile using the Gabedit program.

Ligand Binding Affinity Prediction for Membrane Proteins with Alchemical Free Energy Calculation Methods.

Alchemical relative binding free energy (ΔΔG) calculations have shown high accuracy in predicting ligand binding affinity and have been used as important tools in computer-aided drug discovery and design. However, there has been limited research on the application of ΔΔG methods to membrane proteins despite the fact that these proteins represent a significant proportion of drug targets, play crucial roles in biological processes, and are implicated in numerous diseases. In this study, to predict the binding affinity of ligands to G protein-coupled receptors (GPCRs), we employed two ΔΔG calculation methods: thermodynamic integration (TI) with AMBER and the alchemical transfer method (AToM) with OpenMM. We calculated ΔΔG values for 53 transformations involving four class A GPCRs and evaluated the performance of AMBER-TI and AToM-OpenMM. In addition, we conducted tests using different numbers of windows and varying simulation times to achieve reliable ΔΔG results and to optimize resource utilization. Overall, both AMBER-TI and AToM-OpenMM show good agreement with the experimental data. Our results validate the applicability of AMBER-TI and AToM-OpenMM for optimization of lead compounds targeting membrane proteins.

Chemical Probes to Investigate Central Nervous System Disorders: Design, Synthesis and Mechanism of Action of a Potent Human Serine Racemase Inhibitor.

The intricate signaling network within the central nervous system (CNS) involving N-methyl-d-aspartate receptors (NMDARs) has been recognized as a key player in severe neurodegenerative diseases. The indirect modulation of NMDAR-mediated neurotransmission through inhibition of serine racemase (SR)-the enzyme responsible for the synthesis of the NMDAR coagonist d-serine-has been suggested as a therapeutic strategy to treat these conditions. Despite the inherent challenges posed by SR conformational flexibility, a ligand-based drug design strategy has successfully produced a series of potent covalent inhibitors structurally related to amino acid analogues. Among these inhibitors, O-(2-([1,1'-biphenyl]-4-yl)-1-carboxyethyl)hydroxylammonium chloride (28) has emerged as a valuable candidate with a K d of about 5 μM, which makes it one of the most potent hSR inhibitors reported to date. This molecule is expected to inspire the identification of selective hSR inhibitors that might find applications as tools in the study and treatment of several CNS pathologies.

TRPA1 Inhibition Effects by 3-Phenylcoumarin Derivatives.

Transient receptor potential ankyrin 1 (TRPA1) protein plays an important role in the inflammatory response, and it has been associated with different pain conditions and pain-related diseases, making TRPA1 a valid target for painkillers. In this study, we identified potential TRPA1 inhibitors and located their binding sites utilizing computer-aided drug design (CADD) techniques. The designed 3-phenylcoumarin-based TRPA1 inhibitors were successfully synthesized using a microwave assisted synthetic strategy. 3-(3-Bromophenyl)-7-acetoxycoumarin (5), 7-hydroxy-3-(3-hydroxyphenyl)coumarin (12) and 3-(3-hydroxyphenyl)coumarin (23) all showed inhibitory activity toward TRPA1 in vitro. Compound 5 also decreased the size and formation of breast cancer cells. Hence, targeting TRPA1 may represent a promising alternative for the treatment of pain and inflammation.

Novel 1,3,4-oxadiazole derivatives of naproxen targeting EGFR: Synthesis, molecular docking studies, and cytotoxic evaluation.

The close association between inflammation and cancer inspired the synthesis of a series of 1,3,4-oxadiazole derivatives (compounds H4-A-F) of 6-methoxynaphtalene. The chemical structures of the new compounds were validated utilizing Fourier-transform infrared, proton nuclear magnetic resonance, and carbon-13 nuclear magnetic resonance spectroscopic techniques and CHN analysis. Computer-aided drug design methods were used to predict the compounds biological target, ADMET properties, toxicity, and to evaluate the molecular similarities between the design compounds and erlotinib, a standard epidermal growth factor receptor (EGFR) inhibitor. The antiproliferative effects of the new compounds were evaluated by the 3-(4,5-dimethyl-2-thiazolyl)-2,5-diphenyl-2H-tetrazolium bromide assay, cell cycle analysis, apoptosis detection by microscopy, quantitative reverse transcription-polymerase chain reaction, and immunoblotting, and EGFR enzyme inhibition assay. In silico analysis of the new oxadiazole derivatives indicated that these compounds target EGFR, and that compounds H4-A, H4-B, H4-C, and H4-E show similar molecular properties to erlotinib. Additionally, the results indicated that none of the synthesized compounds are carcinogenic, and that compounds H4-A, H4-C, and H4-F are nontoxic. Compound H4-A showed the best-fit score against EGFR pharmacophore model, however, the in vitro studies indicated that compound H4-C was the most cytotoxic. Compound H4-C caused cytotoxicity in HCT-116 colorectal cancer cells by inducing both apoptosis and necrosis. Furthermore, compounds H4-D, H4-C, and H4-B had potent inhibitory effect on EGFR tyrosine kinase that was comparable to erlotinib. The findings of this inquiry offer a basis for further investigation into the differences between the synthesized compounds and erlotinib. However, additional testing will be needed to assess all of these differences and to identify the most promising compound for further research.

Advances in Computational Methods for Drug Design: A Revolution in Pharmaceutical Development

Computational methods of drug design involve the use of various software and algorithms to predict the properties of drug molecules, screen for potential targets, and optimize drug candidates for therapeutic efficacy, it lowering the cost of drug research and development time. The discovery and development of a novel medicine is a lengthy, complex, expensive, and high-risk process that has no commercial counterpart. CADD has previously been used to uncover drugs that have gone through clinical trials and become innovative medicines for many ailments. CADD approaches are widely divided into two categories: structure-based drug design (SBDD) and ligand-based drug design (LBDD). SBDD is used when the three-dimensional structures of target proteins are available, while LBDD design is used in the absence of receptor 3D information and relies on knowledge of molecules that bind to the biological target of interest. Some applications in CADD are Lead Optimization, Virtual screening (VS), ADMET prediction, and toxicity prediction. Medicinal chemists use a variety of computational approaches to modify the chemical structure of a compound to maximize its in vitro activity, drug discovery is driven by the idea that a ligand with higher binding affinity to a target should be more efficacious than that with lower binding affinity to the same target. Target flexibility is one of the key issues that still need to be resolved in drug discovery. The majority of molecular docking tools give the ligand high flexibility, but they fix or give the protein's residues close to or inside the active site only limited flexibility. It is very difficult to provide complete molecular flexibility to the protein as this increases the space and time complexity of the computation. In conclusion, computational methods have revolutionized the field of drug design by enabling faster, more cost-effective, and more efficient drug discovery

Polypharmacology-Driven Discovery and Design of Highly Selective, Dual and Multitargeting Inhibitors of Mycobacterium tuberculosis - A Review

: The increasing demand for novel antitubercular agents has been the main 'force' of many TB research efforts due to the uncontrolled growing number of drug-resistant strains of M. tuberculosis in the clinical setting. Many strategies have been employed to address the drug-resistant issue, including a trend that is gaining attention, which is the design and discovery of Mtb inhibitors that are either dual- or multitargeting. The multiple-target design concept is not new in medicinal chemistry. With a growing number of newly discovered Mtb proteins, numerous targets are now available for developing new biochemical/cell-based assays and computer-aided drug design (CADD) protocols. To describe the achievements and overarching picture of this field in anti- infective drug discovery, we provide in this review small molecules that exhibit profound inhibitory activity against the tubercle bacilli and are identified to trace two or more Mtb targets. This review also presents emerging design methodologies for developing new anti-TB agents, particularly tailored to structure-based CADD. Dedicated on the special occasion of the 70th birthday of Prof. Dr. Ma. Alicia Aguinaldo, whose scientific efforts elevated antituberculosis drug discovery in the Philippines.

In-silico Binding, Stability, Pharmacokinetics, and Toxicity Studies on Natural (-)-ambrox Metabolites as Binding Ligands to Luminal B and Triple-negative/basal-like Proteins for Breast Cancer Therapy

Background:: Breast cancer is the most prevalent malignant tumour in women of all races and is the second largest cause of cancer-related death in the majority of races. Based on the pattern of gene expression, five intrinsic or molecular classifications for breast tumours are frequently used. Our research, which is presently being utilized to treat breast cancer and has the potential to significantly change the course of the illness, is focused on two of them: luminal B breast cancer and triplenegative/ basal-like breast cancer. Methods:: Screening a database containing millions of drug molecules or phytochemicals has become rapid and simple due to computer-aided drug design (CADD) techniques. In the current work, nine natural compounds were screened for ambrox from a sperm whale using docking research. Results:: Following docking studies, nine substances were discovered to interact with basal-like and luminal B breast cancer proteins. All nine metabolites, however, adhered to Lipinski's rule of five and had sufficient oral bioavailability. The greatest binding affinities were demonstrated by 13,14,15,16-tetranorlabdane-3-oxo-8,12-diol, 6-β-hydroxy ambrox, 1-α-hydroxy-3-oxoambrox, and 2-α-3-β-dihydroxy ambrox. Conclusion:: Therefore, it can be concluded that research on molecular docking and pharmacological mimics may hasten the discovery of new medications. The use of ambrox metabolites in the treatment of breast cancer also requires future perspectives on their therapeutic use.

Computer-aided rational design and synthesis of new potential antihypertensive agents among 1,2,3-triazole-containing nifedipine analogs

1,2,3-Triazole-containing Nifedipine analogues offer the opportunity to increase biostability, bioavailability, efficacy and binding selectivity to target receptors. Here, we applied a computer-aided rational design for identifying new Nifedipine analogues containing a 1,2,3-triazole moiety. First, a new chemical library of 796 derivatives combining the DHP fragment and 1,2,3-triazole moiety was generated. Second, to reduce the library size, the library was pre-filtered using two 3D-pharmacophore models with different complexity, which allowed us to gradually reduce the chemical space, ending up with 26 hit candidates. Molecular docking calculations against the rCav1.1 receptor allowed the identification of eight derivatives 5a-h, characterized by the binding affinity towards the rCav1.1 receptor of the same level as approved Nifedipine-like drugs. Next, our molecular docking results were used to guide and optimize the retrosynthetic approaches for new analogues of Nifedipine as promising antihypertensive agents. So, a retrosynthetic approach for Nifedipine analogues with a 1,2,3-triazole ring in position 4 was proposed. Finally, eight analogues 5a-h determined by molecular docking calculations were synthesized using the suggested retrosynthetic approach. The aim of this study is to identify new Nifedipine analogues using a computer-aided drug design and a retrosynthetic approach. Materials and Methods. The organic synthesis of new Nifedipine analogues containing a 1,2,3-triazole moiety. Computer-aided drug design of new DHP derivatives using pharmacophore screening and molecular docking calculations. Results. Molecular docking of new Nifedipine analogues made it possible to estimate the binding affinity of new Nifedipine derivatives to the rCav1.1 receptor. Pharmacophore screening of a chemical library of analogues, consisting of 796 derivatives, allowed gradually reducing the chemical space and obtaining 26 candidates with high affinity to the rCav1.1 receptor. Using the method of molecular docking, eight hits 5a-h were identified, and the synthesis of the recommended compounds was proposed and performed. Conclusions. The results of molecular docking showed that Nifedipine analogues are characterized by binding affinity to the rCav1.1 receptor at the same level as approved Nifedipine-like drugs. Pharmacophore screening and molecular docking calculations indicate key features of the ligand-receptor interaction that can guide and optimize the synthesis of new Nifedipine analogues as promising new antihypertensive agents. A retrosynthetic approach was proposed, and the recommended compounds were synthesized

Integrating Computational Methods into Antibacterial Drug Discovery and Development from Natural Products

Natural products have long been pivotal in sustaining civilizations due to their medicinal properties and have served as a rich source of bioactive compounds for drug discovery. With the rise of antibiotic-resistant bacteria, there is an urgent need for new antibacterial agents. Natural products, with their diverse chemical structures, provide a promising reservoir for identifying new lead compounds. Historically, the discovery of bioactive natural products often relied on serendipity, but modern advancements in computational approaches, particularly computer-aided drug design (CADD), have revolutionized this process. In the modern era, vast data from advanced technologies can provide valuable insights through computational methods. These approaches enable the structural analysis of molecules and the prediction of their biological activities, accelerating drug discovery. Advances in molecular sciences have identified more antibacterial targets, further facilitating this process. Integrating computational drug design with the study of natural products allows for the efficient identification and optimization of potential antibacterial agents. Moreover, many medicinal plants harbor mechanisms of action that remain unexplored by conventional methods. By employing computational techniques, researchers can uncover these mechanisms and develop novel antibacterial drugs. This combined approach not only enhances our understanding of natural products but also addresses the critical need for new treatments in the fight against bacterial infections.