Applications In Drug Design Research Articles

Application of computer-aided drug design in drug discovery and development: Updating knowledge

Coronavirus (CoV) diseases are widespread throughout the world and have caused considerable socio-economic disruptions. For this reason, efforts have been made to develop a direct or indirect antiviral drugs against these diseases. However, no specific antiviral drug has yet been approved by the Food and Drug Administration (FDA) for CoV infections. Thus, the challenge in discovering therapeutic molecules against these infections remains pertinent. Computer-aided drug design (CADD) is one of the modern techniques for drug discovery and development. It accelerates the process, minimizes costs, and reduces research time. In this article, we present the three CADD approaches, namely structure-based drug discovery (SBDD), ligand-based drug discovery (LBDD) and high-throughput virtual screening (HTVS). The different methods used in these three approaches CADD, such as molecular modelling, target structure analysis, molecular docking, molecular dynamics simulation, pharmacophore modelling, quantitative structure-activity relationship (QSAR), ligand-based virtual screening (LBVS) and structure-based virtual screening (SBVS) are detailed. In addition, the bioinformatics tools and databases commonly used in these different CADD techniques are also described.

Synthesis, Crystal Structure, Intermolecular Interactions, HOMO-LUMO, MEP, NLO Properties, and DFT/TD-DFT Investigation of (Z)-5-(4-Nitrobenzylidene)-3-N(3-Chlorophenyl)-2-Thioxothiazolidin-4-One

A novel heterocyclic compound named (Z)-5-(4-nitrobenzylidene)-3-N(3-chlorophenyl)-2-thioxothiazolidin-4-one (NCTh) was synthesized and analyzed using various techniques, including single crystal X-ray diffraction, FT-IR, UV-Vis, NMR spectroscopy, and mass spectral data methods. NCTh was observed to crystallize in the triclinic crystal system P-1 space group. To validate the experimental findings, density functional theory (DFT) calculations were performed using the B3LYP functional with the 6-311 G(d,p) basis set. The results indicated good agreement in geometrical parameters between the experimental and theoretical outcomes. The study also calculated HOMO-LUMO energies, molecular electrostatic potential (MEP), and global chemical reactivity descriptors to evaluate the compound’s reactivity. Additionally, the study conducted reduced density gradient and Hirshfeld surface analyses to reveal attractive, repulsive, and van der Waals strong and weak interactions. The calculation of thermodynamic parameters was also included. The study focused on investigating the nonlinear optical (NLO) properties of NCTh, which were characterized through key parameters such as the electric dipole moment (µ), polarizability (α), first-order hyperpolarizability (β), and second-order hyperpolarizability (γ). These properties provide insights into the material’s potential applications in optical and photonic technologies. Additionally, a molecular docking study was performed to further explore the structure-activity relationship, particularly in a biological context. The docking results revealed a strong ligand-protein interaction, with a binding energy of −10.3 kcal/mol, indicating significant affinity. Moreover, the inhibition constant (Ki) was calculated to be 0.0281 μM, suggesting a highly potent interaction, which could be relevant for drug design or biochemical applications.

OpenDock: a pytorch-based open-source framework for protein-ligand docking and modelling.

Molecular docking is an invaluable computational tool with broad applications in computer-aided drug design and enzyme engineering. However, current molecular docking tools are typically implemented in languages such as C++ for calculation speed, which lack flexibility and user-friendliness for further development. Moreover, validating the effectiveness of external scoring functions for molecular docking and screening within these frameworks is challenging, and implementing more efficient sampling strategies is not straightforward. To address these limitations, we have developed an open-source molecular docking framework, OpenDock, based on Python and PyTorch. This framework supports the integration of multiple scoring functions; some can be utilized during molecular docking and pose optimization, while others can be used for post-processing scoring. In terms of sampling, the current version of this framework supports simulated annealing and Monte Carlo optimization. Additionally, it can be extended to include methods such as genetic algorithms and particle swarm optimization for sampling docking poses and protein side chain orientations. Distance constraints are also implemented to enable covalent docking, restricted docking or distance map constraints guided pose sampling. Overall, this framework serves as a valuable tool in drug design and enzyme engineering, offering significant flexibility for most protein-ligand modelling tasks. OpenDock is publicly available at: https://github.com/guyuehuo/opendock.

A review of 3D printing technology in drug design applications

A review of 3D printing technology in drug design applications

The potential of flavonoids to mitigate cellular senescence in cardiovascular disease.

Aging is one of the most significant factors affecting cardiovascular health, with cellular senescence being a central hallmark. Senescent cells (SCs) secrete a specific set of signaling molecules known as the senescence-associated secretory phenotype (SASP). The SASP has a remarkable impact on age-associated diseases, particularly cardiovascular diseases (CVD). Targeting SCs through anti-aging therapies represents a novel strategy to effectively retard senescence and attenuate disease progression. Accumulating evidence demonstrates that the flavonoids, widely presented in fruits and vegetables worldwide, can delay or treat CVD via selectively eliminating SCs (senolytics) and modulating SASPs (senomorphics). Nevertheless, only sporadic research has illustrated the application of flavonoids in targeting SCs for CVD, which requires further exploration. This review recapitulates the hallmarks and key molecular mechanisms involved in cellular senescence, then summarizes senescence of different types of cardiac cells and describes the mechanisms by which cellular senescence affects CVD development. The discussion culminates with the potential use of flavonoids via exerting their biological effects on cellular senescence to reduce CVD incidence. This summary will provide valuable insights for cardiovascular drug design, development and clinical applications leveraging flavonoids.

From Nature to Drug: Overview and CADD Approach of Anacardic Acid to Propose their Biological Potential

Anacardic acids are natural compounds found in various plant families, such as Anacardiaceae, Geraniaceae, Ginkgoaceae, and Myristicaceae, among others. Several activities have been reported regarding these compounds, including antibacterial, antioxidant, anticancer, anti-inflammatory, and antiviral activities, showing the potential therapeutic applicability of these compounds. From a chemical point of view, they are structurally made up of salicylic acids substituted by an alkyl chain containing unsaturated bonds, which can vary in number and position, determining their bioactivity and differentiating them from the various existing forms. Our work aimed to explore the potential of anacardic acids, based on studies that address the bioactivity of these compounds, as well as to establish a greater understanding of the structure-activity relationship of these compounds through in silico methods, with a focus on the elucidation of a possible drug target through the application of computer-aided drug design, CADD.

Evaluating the therapeutic potential for cancer treatment and revealing the mechanism of telisotuzumab by atomic force microscopy

Telisotuzumab is a monoclonal antibody and a novel anti-cancer drug that specifically binds to the hepatocyte growth factor receptor (HGFR). In this study, we utilized atomic force microscopy (AFM) to examine the mechanical properties of telisotuzumab-treated cancer cells and the biomechanical interplay between telisotuzumab and HGFR. Our observations showed that the binding force of telisotuzumab and hepatocyte growth factor (HGF) with HGFR was nearly identical; however, compared to HGF, telisotuzumab exhibited greater stability and ease of binding. Additionally, telisotuzumab treatment increased the stiffness of A549 lung cancer cells, thus limiting their migration and proliferation. Furthermore, telisotuzumab had a shorter interaction duration, faster bond-formation rate, smaller dissociation rate constant, and required higher activation-free energy for dissociation, which may explain its dominance in the competitive sites. Our study provides valuable insights into the molecular mechanisms underlying the efficacy of telisotuzumab and demonstrates its potential application in further anti-cancer drug design.

Enthalpic Classification of Water Molecules in Target-Ligand Binding.

Water molecules play various roles in target-ligand binding. For example, they can be replaced by the ligand and leave the surface of the binding pocket or stay conserved in the interface and form bridges with the target. While experimental techniques supply target-ligand complex structures at an increasing rate, they often have limitations in the measurement of a detailed water structure. Moreover, measurements of binding thermodynamics cannot distinguish between the different roles of individual water molecules. However, such a distinction and classification of the role of individual water molecules would be key to their application in drug design at atomic resolution. In this study, we investigate a quantitative approach for the description of the role of water molecules during ligand binding. Starting from complete hydration structures of the free and ligand-bound target molecules, binding enthalpy scores are calculated for each water molecule using quantum mechanical calculations. A statistical evaluation showed that the scores can distinguish between conserved and displaced classes of water molecules. The classification system was calibrated and tested on more than 1000 individual water positions. The practical tests of the enthalpic classification included important cases of antiviral drug research on HIV-1 protease inhibitors and the Influenza A ion channel. The methodology of classification is based on open source program packages, Gromacs, Mopac, and MobyWat, freely available to the scientific community.

HydraScreen: A Generalizable Structure-Based Deep Learning Approach to Drug Discovery.

We propose HydraScreen, a deep-learning framework for safe and robust accelerated drug discovery. HydraScreen utilizes a state-of-the-art 3D convolutional neural network designed for the effective representation of molecular structures and interactions in protein-ligand binding. We designed an end-to-end pipeline for high-throughput screening and lead optimization, targeting applications in structure-based drug design. We assessed our approach using established public benchmarks based on the CASF-2016 core set, achieving top-tier results in affinity and pose prediction (Pearson's r = 0.86, RMSE = 1.15, Top-1 = 0.95). We introduced a novel approach for interaction profiling, aimed at detecting potential biases within both the model and data sets. This approach not only enhanced interpretability but also reinforced the impartiality of our methodology. Finally, we demonstrated HydraScreen's ability to generalize effectively across novel proteins and ligands through a temporal split. We also provide insights into potential avenues for future development aimed at enhancing the robustness of machine learning scoring functions. HydraScreen (accessible at http://hydrascreen.ro5.ai/paper) provides a user-friendly GUI and a public API, facilitating the easy-access assessment of protein-ligand complexes.

Bionic nanopore recognition receptors for single-molecule enantioselectivity studies of chiral drugs

BackgroundEnantiodiscrimination of chiral drugs is critical for understanding physiological phenomena and ensuring medical safety. Although enantiomers of these drugs share identical physicochemical properties, they exhibit significant differences in pharmacodynamic, pharmacokinetic, and toxicological properties due to the differences in their three-dimensional shapes. Therefore, the development of effective methods for chiral recognition is of great significance and has been a hot topic in chemo/biological studies. ResultsIn this study, we designed a recognition receptor comprising a α-hemolysin (α-HL) nanopore and sulfobutyl ether-β-cyclodextrin (SBEβCD) for identifying the enantiomers of the antidepressant duloxetine at the single-molecule level. Chiral molecules were discriminated based on the different current blockages within the recognition receptor. The results indicated a strong interaction between R-duloxetine and the recognition receptor. By combining the experimental data and molecular docking results, we explored the recognition mechanism of the designed nanopore recognition receptor for chiral drug molecules. It was found that hydrophobic and electrostatic interactions play key roles in chiral recognition. Additionally, by comparing the binding kinetics of enantiomers to cyclodextrins in confined nanospace and bulk solution, we found that enantiomeric identification was better facilitated in the confined nanospace. Finally, the enantiomeric excess (ee) of the enantiomeric duloxetine mixture was measured using this recognized receptor. SignificanceThis strategy has the advantages of low cost, high sensitivity, and no need for additional derivative modifications, providing a new perspective on the development of chiral recognition sensors with excellent enantioselectivity in drug design, pharmaceuticals, and biological applications.

MolScore: a scoring, evaluation and benchmarking framework for generative models in de novo drug design

Generative models are undergoing rapid research and application to de novo drug design. To facilitate their application and evaluation, we present MolScore. MolScore already contains many drug-design-relevant scoring functions commonly used in benchmarks such as, molecular similarity, molecular docking, predictive models, synthesizability, and more. In addition, providing performance metrics to evaluate generative model performance based on the chemistry generated. With this unification of functionality, MolScore re-implements commonly used benchmarks in the field (such as GuacaMol, MOSES, and MolOpt). Moreover, new benchmarks can be created trivially. We demonstrate this by testing a chemical language model with reinforcement learning on three new tasks of increasing complexity related to the design of 5-HT2a ligands that utilise either molecular descriptors, 266 pre-trained QSAR models, or dual molecular docking. Lastly, MolScore can be integrated into an existing Python script with just three lines of code. This framework is a step towards unifying generative model application and evaluation as applied to drug design for both practitioners and researchers. The framework can be found on GitHub and downloaded directly from the Python Package Index.Scientific ContributionMolScore is an open-source platform to facilitate generative molecular design and evaluation thereof for application in drug design. This platform takes important steps towards unifying existing benchmarks, providing a platform to share new benchmarks, and improves customisation, flexibility and usability for practitioners over existing solutions.Graphical

In-silico analysis of ribosome inactivating protein (RIP) of the Cucurbitaceae family.

Ribosome-inactivating proteins (RIPs) are highly active N-glycosidases that depurinate both bacterial and eukaryotic rRNAs, halting protein synthesis during translation. Found in a diverse spectrum of plant species and tissues, RIPs possess antifungal, antibacterial, antiviral, and insecticidal properties linked to plant defense. In this study, we investigated the physiochemical properties of RIP peptides from the Cucurbitaceae family through bioinformatics approaches. Molecular weight, isoelectric point, aliphatic index, extinction coefficient, and secondary structures were analyzed, revealing their hydrophobic nature. The novelty of this work lies in the comprehensive examination of RIPs from the Cucurbitaceae family and their potential therapeutic applications. The study also elucidated the binding interactions of Cucurbitaceae RIPs with key biological targets, including Interleukin-6 (IL-6). Strong hydrogen bond interactions between RIPs and these targets suggest potential for innovative insilico drug design and therapeutic applications, particularly in cancer treatment. Comprehensive analysis of bond lengths using Ligpolt + software provides insights for optimizing molecular interactions, offering a valuable tool for drug design and structural biology studies.

Prediction of 3D RNA Structures from Sequence Using Energy Landscapes of RNA Dimers: Application to RNA Tetraloops.

Access to the three-dimensional structure of RNA enables an ability to gain a more profound understanding of its biological mechanisms, as well as the ability to design RNA-targeting drugs, which can take advantage of the unique chemical environment imposed by a folded RNA structure. Due to the dynamic and structurally complex properties of RNA, both experimental and traditional computational methods have difficulty in determining RNA's 3D structure. Herein, we introduce TAPERSS (Theoretical Analyses, Prediction, and Evaluation of RNA Structures from Sequence), a physics-based fragment assembly method for predicting 3D RNA structures from sequence. Using a fragment library created using discrete path sampling calculations of RNA dinucleoside monophosphates, TAPERSS can sample the physics-based energy landscapes of any RNA sequence with relatively low computational complexity. We have benchmarked TAPERSS on 21 RNA tetraloops, using a combinatorial algorithm as a proof-of-concept. We show that TAPERSS was successfully able to predict the apo-state structures of all 21 RNA hairpins, with 16 of those structures also having low predicted energies as well. We demonstrate that TAPERSS performs most accurately on GNRA-like tetraloops with mostly stacked loop-nucleotides, while having limited success with more dynamic UNCG and CUYG tetraloops, most likely due to the influence of the RNA force field used to create the fragment library. Moreover, we show that TAPERSS can successfully predict the majority of the experimental non-apo states, highlighting its potential in anticipating biologically significant yet unobserved states. This holds great promise for future applications in drug design and related studies. With discussed improvements and implementation of more efficient sampling algorithms, we believe TAPERSS may serve as a useful tool for a physics-based conformational sampling of large RNA structures.

Display Technologies for Expanding the Pharmaceutical Applications of Cyclotides

Display Technologies for Expanding the Pharmaceutical Applications of Cyclotides

Modern machine‐learning for binding affinity estimation of protein–ligand complexes: Progress, opportunities, and challenges

AbstractStructure‐based drug design is a widely applied approach in the discovery of new lead compounds for known therapeutic targets. In most structure‐based drug design applications, the docking procedure is considered the crucial step. Here, a potential ligand is fitted into the binding site, and a scoring function assesses its binding capability. With the rise of modern machine‐learning in drug discovery, novel scoring functions using machine‐learning techniques achieved significant performance gains in virtual screening and ligand optimization tasks on retrospective data. However, real‐world applications of these methods are still limited. Missing success stories in prospective applications are one reason for this. Additionally, the fast‐evolving nature of the field makes it challenging to assess the advantages of each individual method. This review will highlight recent strides toward improved real world applicability of machine‐learning based scoring, enabling a better understanding of the potential benefits and pitfalls of these functions on a project. Furthermore, a systematic way of classifying machine‐learning based scoring that facilitates comparisons will be presented.This article is categorized under: Data Science > Chemoinformatics Data Science > Artificial Intelligence/Machine Learning Software > Molecular Modeling

Chemical applicability and curvilinear regression models of vertex‐degree‐based topological index: Elliptic Sombor index

AbstractChemical graph theory facilitates different tools that have significant applications in drug design and development. Recently Gutman et al., introduced a novel vertex‐degree‐based topological index called elliptic Sombor index and its application through geometric approach. This article focuses on chemical applicability of elliptic Sombor index through regression models using 22 benzenoid hydrocarbons. In this process, the chemical applicability of elliptic Sombor index with various physicochemical properties of benzenoid hydrocarbons through curvilinear regression models is carried out. The data of the statistical analysis shows that elliptic Sombor index is potential predictive index for the property polarizability.

A QSPR analysis of physical properties of antituberculosis drugs using neighbourhood degree-based topological indices and support vector regression

Topological indices are molecular descriptors used in QSPR modelling to predict the physicochemical properties of molecules. Topological indices are used in numerous applications in drug design. In this work, we compute the neighbourhood degree-based topological indices of 15 antituberculosis drugs, we studied the QSPR analysis of these drugs using support vector regression. The efficiency of support vector regression is determined by comparing it with the classical linear regression. Our QSPR model further shows the superiority of the SVR model as a better predictive model in QSPR analysis of the physical properties of antituberculosis drugs. The findings in this study are a further contribution to the field of chemical graph theory and drug design, providing a deeper understanding of neighbourhood degree-based topological indices and their predictive capabilities in QSPR model.

Revolutionizing Cancer Research and Drug Discovery: The Role of Artificial Intelligence and Machine Learning

Abstract: Artificial Intelligence (AI) and Machine Learning (ML) have revolutionized various industries, including cancer research and drug discovery. This article provides a summary of the history of AI and ML, highlighting their resurgence in the 1990s with advancements in computational power and new algorithms. In the context of drug discovery, AI and ML techniques have been applied to accelerate the development of new drugs, from target identification and lead generation to drug repurposing. AI applications in drug design and virtual screening have improved the efficiency of identifying potential drug candidates. Deep learning, a division of ML, has been particularly effective in predicting protein structures and optimizing lead compounds. In anti-cancer drug target prediction, AI and ML algorithms analyze large-scale genomic, proteomic, and clinical data to identify potential targets for cancer therapy. AI has also transformed cancer imaging and diagnosis by enhancing the accuracy and efficiency of cancer detection, classification, and prognosis. Medical imaging analysis, pathology, and radiology have benefited from AI algorithms’ ability to interpret and analyze various imaging modalities. Moreover, AI applications in cancer treatment have facilitated the development of predictive models for treatment response, enabling personalized and targeted therapies based on individual patient characteristics. The purpose of the study was to give facts regarding the integration of artificial intelligence and machine learning in drug discovery and cancer therapy and its significant prospects for improving efficiency, decreasing costs, and improving patient outcomes.

An ensemble computational model for prediction of clathrin protein by coupling machine learning with discrete cosine transform

Clathrin protein (CP) plays a pivotal role in numerous cellular processes, including endocytosis, signal transduction, and neuronal function. Dysregulation of CP has been associated with a spectrum of diseases. Given its involvement in various cellular functions, CP has garnered significant attention for its potential applications in drug design and medicine, ranging from targeted drug delivery to addressing viral infections, neurological disorders, and cancer. The accurate identification of CP is crucial for unraveling its function and devising novel therapeutic strategies. Computational methods offer a rapid, cost-effective, and less labor-intensive alternative to traditional identification methods, making them especially appealing for high-throughput screening. This paper introduces CL-Pred, a novel computational method for CP identification. CL-Pred leverages three feature descriptors: Dipeptide Deviation from Expected Mean (DDE), Bigram Position Specific Scoring Matrix (BiPSSM), and Position Specific Scoring Matrix-Tetra Slice-Discrete Cosine Transform (PSSM-TS-DCT). The model is trained using three classifiers: Support Vector Machine (SVM), Extremely Randomized Tree (ERT), and Light eXtreme Gradient Boosting (LiXGB). Notably, the LiXGB-based model achieves outstanding performance, demonstrating accuracies of 94.63% and 93.65% on the training and testing datasets, respectively. The proposed CL-Pred method is poised to significantly advance our comprehension of clathrin-mediated endocytosis, cellular physiology, and disease pathogenesis. Furthermore, it holds promise for identifying potential drug targets across a spectrum of diseases. Communicated by Ramaswamy H. Sarma

Cryo-electron microscopy-based drug design.

Structure-based drug design (SBDD) has gained popularity owing to its ability to develop more potent drugs compared to conventional drug-discovery methods. The success of SBDD relies heavily on obtaining the three-dimensional structures of drug targets. X-ray crystallography is the primary method used for solving structures and aiding the SBDD workflow; however, it is not suitable for all targets. With the resolution revolution, enabling routine high-resolution reconstruction of structures, cryogenic electron microscopy (cryo-EM) has emerged as a promising alternative and has attracted increasing attention in SBDD. Cryo-EM offers various advantages over X-ray crystallography and can potentially replace X-ray crystallography in SBDD. To fully utilize cryo-EM in drug discovery, understanding the strengths and weaknesses of this technique and noting the key advancements in the field are crucial. This review provides an overview of the general workflow of cryo-EM in SBDD and highlights technical innovations that enable its application in drug design. Furthermore, the most recent achievements in the cryo-EM methodology for drug discovery are discussed, demonstrating the potential of this technique for advancing drug development. By understanding the capabilities and advancements of cryo-EM, researchers can leverage the benefits of designing more effective drugs. This review concludes with a discussion of the future perspectives of cryo-EM-based SBDD, emphasizing the role of this technique in driving innovations in drug discovery and development. The integration of cryo-EM into the drug design process holds great promise for accelerating the discovery of new and improved therapeutic agents to combat various diseases.