Computational transformation in drug discovery: A comprehensive study on molecular docking and quantitative structure activity relationship (QSAR)

New drug discovery is recognized as a complicated, costly, time-consuming, and difficult process. Computer-aided drug discovery has developed as a potent and promising method for faster, cheaper, and more effective drug creation. Recently, the rapid rise of computational methods for drug discovery, including anticancer medicines, had a substantial and exceptional impact on anticancer drug design, as well as providing beneficial insights into the field of cancer therapy. In this paper, we discussed the quantitative structure-activity relationship (QSAR), which is a significant in-silico tool in rational drug design. The QSAR method is used to optimize the existing leads to improve their biological activities, and physicochemical properties and to predict the biological activities of untested and sometimes unavailable compounds, so QSAR is a significant method in drug designing. This article is a comprehensive review of various QSAR studies conducted which help to create new and potent inhibitors for targeting tubulin, a crucial target in cancer treatment. It particularly focuses on studies that provide structural insights into the compounds targeting tubulin. It should prioritize continually researching specific scaffolds, with a focus on important attachment regions, to gather more powerful molecular data and enhance models. This will lead to a better understanding of drug interactions and the development of improved cancer-targeting inhibitors for tubulin. Communicated by Ramaswamy H. Sarma

The multidrug transporter P-glycoprotein is an ATP binding cassette (ABC) exporter responsible for resistance to tumor cells during chemotherapy. This study was designed with computational approaches aimed at identifying the best potent inhibitors of P-glycoprotein. Although many compounds have been suggested to inhibit P-glycoprotein, however, their information on bioavailability, selectivity, ADMET properties, and molecular interactions has not been revealed. Molecular docking, ADMET analysis, molecular dynamics, Principal component analysis (PCA), and binding free energy calculations were performed. Two compounds D1 and D2 showed the best docking score against P-glycoprotein and both compounds have 4-thiazolidinone derivatives containing indolin-3 one moiety are novel anti-tumor compounds. ADMET calculation analysis predicted D1 and D2 to have acceptable pharmacokinetic properties. The MD simulation discloses that D1-P-glycoprotein and D2-P-glycoprotein complexes are in stable conformation as apo-form. Hydrophobic amino acid such as phenylalanine plays significant on the interactions of inhibitors. Principal component analysis shows that both complexes are relatively similar variables as apo-form except planarity and Columbo energy profile. In addition, Quantitative Structural Activity Relationship (QSAR) of the ligand candidates were subjected to the principal component analysis (PCA) for pattern recognition. Partial-least-square (PLS) regression analysis was further utilized to model drug candidates’ QSAR for subsequent prediction of the binding energy of validated drug candidates. PCA revealed groupings of the drug candidates based on the similarity or differences in drug candidates QSAR. Moreover, the developed PLS regression accurately predicted the values of the binding energy of drug candidates, with low residual error of prediction. Communicated by Ramaswamy H. Sarma

Computer-aided drug discovery and development (CADDD): In silico-chemico-biological approach

Artificial intelligence: Machine learning approach for screening large database and drug discovery

The 3CL Protease of severe acute respiratory syndrome coronavirus (SARS-CoV), responsible for viral replication, has emerged as an essential target for designing anti-coronaviral inhibitors in drug discovery. In recent years, small molecule and peptidomimetic inhibitors have been used to target the inhibition of SARS-CoV 3CL Protease. In this study, we have developed 2D and 3D Quantitative structure activity relationship (QSAR) models on 3CL protease inhibitors with good predictive capability to propose inhibitors with improved affinities. Based on the 3 D contour maps, three new inhibitors were designed in silico, which were further subjected to molecular docking to explore their binding modes. The newly designed compounds showed improved interaction energies toward SARS-CoV-3CLPro due to additional interactions with the active site residues. The molecular docking studies of the most potent compounds revealed specific interactions with Glu 166 and Cys 145. Furthermore, absorption, distribution, metabolism, elimination (ADME) and drug-likeness evaluation revealed improved pharmacokinetic properties for these compounds. The molecular dynamics simulations confirmed the stability of the interactions identified by docking. The results presented would guide the development of new 3CL protease inhibitors with improved affinities in the future. Communicated by Ramaswamy H. Sarma

Molecular docking is a key tool in structural biology and computer-assisted drug design. Molecular docking is a method which predicts the preferred orientation of a ligand when bound in an active site to form a stable complex. It is the most common method used as a structure-based drug design. Here, the authors intend to discuss the various types of docking methods and their development and applications in modern drug discovery. The important basic theories such as sampling algorithm and scoring functions have been discussed briefly. The performances of the different available docking software have also been discussed. This chapter also includes some application examples of docking studies in modern drug discovery such as targeted drug delivery using carbon nanotubes, docking of nucleic acids to find the binding modes and a comparative study between high-throughput screening and structure-based virtual screening.

Swertia chirayita, a medicinal herb inhabiting the challenging terrains and high altitudes of the Himalayas, is a rich source of essential phytochemical isolates. Amarogentin, a bitter secoiridoid glycoside from S. chirayita, shows varied activity in several patho-physiological conditions, predominantly in leishmaniasis and carcinogenesis. Experimental analysis has revealed that amarogentin downregulates the cyclooxygenase-2 (COX-2) activity and helps to curtail skin carcinogenesis in mouse models; however, there exists no account on selective inhibition of the inducible cyclooxygenase (COX) isoform by amarogentin. Hence the computer-aided drug discovery methods were used to unravel the COX-2 inhibitory mechanism of amarogentin and to check its selectivity for the inducible isoform over the constitutive one. The generated theoretical models of both isoforms were subjected to molecular docking analysis with amarogentin and twenty-one other Food and Drug Authority (FDA) approved lead molecules. The post-docking binding energy profile of amarogentin was comparable to the binding energy profiles of the FDA approved selective COX-2 inhibitors. Subsequent molecular dynamics simulation analysis delineated the difference in the stability of both complexes, with amarogentin-COX-2 complex being more stable after 40ns simulation. The total binding free energy calculated by MMGBSA for the amarogentin-COX-2 complex was −52.35 KCal/mol against a binding free energy of −8.57 KCal/mol for amarogentin-COX-1 complex, suggesting a possible selective inhibition of the COX-2 protein by the natural inhibitor. Amarogentin achieves this potential selectivity by small, yet significant, structural differences inherent to the binding cavities of the two isoforms. Hypothetically, it might block the entry of the natural substrates in the hydrophobic binding channel of the COX-2, inhibiting the cyclooxygenation step. To sum up briefly, this work highlights the mechanism of the possible selective COX-2 inhibition by amarogentin and endorses the possibility of obtaining efficient, futuristic and targeted therapeutic agents for relieving inflammation and malignancy from this phytochemical source.

We report molecular docking and quantitative structure–activity relationship (QSAR) studies on plasmepsin-II inhibitor di-tertiary amines as antimalarial agents. The docking studies suggest that the molecules bind with a pocket of the enzyme formed by some non-polar amino acid residues (Ala219, Gly36, Gly216, Phe111, Phe120, Val78, Ileu32, Ileu123, Ileu290, Ileu300, Leu131 and Leu292) and some polar amino acid residues (Asp34, Asp214, Ser37, Ser218, Thr217, Tyr77 and Tyr192). Different QSAR models, obtained using various three-dimensional, physicochemical and/or topological descriptors and showing cross-validated predictive variance values (Q 2) ranging from 0.487 to 0.664 and the external predictive variance values () for the test set ranging from 0.534 to 0.579, support the statistical validity of the QSAR models.

Quantitative structure-activity relationships (QSARs) attempt to correlate chemical structure with activity using statistical approaches. The QSAR models are useful for various purposes including the prediction of activities of untested chemicals. Quantitative structure-activity relationships and other related approaches have attracted broad scientific interest, particularly in the pharmaceutical industry for drug discovery and in toxicology and environmental science for risk assessment. An assortment of new QSAR methods have been developed during the past decade, most of them focused on drug discovery. Besides advancing our fundamental knowledge of QSARs, these scientific efforts have stimulated their application in a wider range of disciplines, such as toxicology, where QSARs have not yet gained full appreciation. In this review, we attempt to summarize the status of QSAR with emphasis on illuminating the utility and limitations of QSAR technology. We will first review two-dimensional (2D) QSAR with a discussion of the availability and appropriate selection of molecular descriptors. We will then proceed to describe three-dimensional (3D) QSAR and key issues associated with this technology, then compare the relative suitability of 2D and 3D QSAR for different applications. Given the recent technological advances in biological research for rapid identification of drug targets, we mention several examples in which QSAR approaches are employed in conjunction with improved knowledge of the structure and function of the target receptor. The review will conclude by discussing statistical validation of QSAR models, a topic that has received sparse attention in recent years despite its critical importance.

B ligand-based and structure-based computational methods are invaluable tools in computer aided drug discovery. This presentation will introduce technological advancements in both disciplines. First, we introduce BCL: ChemInfo, a comprehensive machine learning-based quantitative structure activity relationship (QSAR) modeling framework featuring novel molecular descriptors, diverse automated feature selection, GPU acceleration, and consensus model analysis. Public availability of high-throughput screening (HTS) data is rapidly increasing highlighting the need for ligand-based computational methods, such as BCL: ChemInfo, to accelerate probe development and drug discovery while reducing costs. The framework was benchmarked on publically available HTS data (PubChem) for nine targets, selected as representatives for the major protein families most commonly targeted by therapeutics, to investigate the influence of size and composition of training data, the choice of objective function, effectiveness of several feature selection techniques, as well as the predictive power of consensus predictors using different machine learning techniques. Second, recently advancements in the small molecule docking using RosettaLigand will be presented including simultaneous docking of explicit interface water molecules, and small molecule docking into comparative models. A study performed on protein-centric water docking shows an improvement in ligand placement at a ratio of 9:1 while ligand-centric water docking allows for the recovery of up to 56% of failed docking studies using 341 structures from the CSAR benchmark of diverse protein-ligand complexes. A study on docking into comparative models found RosettaLigand was successful in recovering a native-like binding mode among the top ten scoring binding modes for 21 of 30 cases while template selection based on ligand occupancy rather than template-target identity was discovered to increase success.

Drug discovery processes, such as new drug development, drug synergy, and drug repurposing, consume significant yearly resources. Computer-aided drug discovery can effectively improve the efficiency of drug discovery. Traditional computer methods such as virtual screening and molecular docking have achieved many gratifying results in drug development. However, with the rapid growth of computer science, data structures have changed considerably; with more extensive and dimensional data and more significant amounts of data, traditional computer methods can no longer be applied well. Deep learning methods are based on deep neural network structures that can handle high-dimensional data very well, so they are used in current drug development. This review summarized the applications of deep learning methods in drug discovery, such as drug target discovery, drug de novo design, drug recommendation, drug synergy, and drug response prediction. While applying deep learning methods to drug discovery suffers from a lack of data, transfer learning is an excellent solution to this problem. Furthermore, deep learning methods can extract deeper features and have higher predictive power than other machine learning methods. Deep learning methods have great potential in drug discovery and are expected to facilitate drug discovery development.

Background:Treatments of fungal diseases, including Candidiasis, remain not up to scratch in spite of the mounting catalog of synthetic antifungal agents. These have served as the impetus for investigating new antifungal agents based on natural products. Consequently, genetic algorithm-multiple linear regression (GA-MLR) based QSAR (Quantitative Structure-Activity Relationship) studies of coumarin analogues along with molecular docking were carried out.Methods:Coumarin analogues with their MIC values were used to generate the training and test sets of compounds for QSAR models development; the analogues were also docked into the binding pocket of NMT (MyristoylCoA: protein N-myristoyltransferase).Results and Discussion:The statistical parameters for internal and external validation of QSAR analysis (R2= 0.830, Q2= 0.758, R2Pred= 0.610 and R2m overall= 0.683 ), Y Randomization, Ridge trace, VIF, tolerance and model criteria of Golbraikh and Tropsha data illustrate the robustness of the best proposed QSAR model. Most of the analogues bind to the electrostatic, hydrophobic clamp and display hydrogen bonding with amino acid residues of NMT. Interestingly, the most active coumarin analogue (MolDock score of -189.257) was docked deeply within the binding pocket of NMT, thereby displaying hydrogen bonding with Tyr107, Leu451, Leu450, Gln226, Cys393 and Leu394 amino acid residues.Conclusion:The combinations of descriptors from various descriptor subsets in QSAR analysis have highlighted the role of atomic properties such as polarizability and atomic van der Waals volume to explain the inhibitory activity. The models and related information may pave the way for important insight into the designing of putative NMT inhibitors for Candida albicans.

Ion channels are widely expressed in living cells and play critical roles in various cellular biological functions. Dysfunctional ion channels can cause a variety of diseases, making ion channels attractive targets for drug discovery. Computational approaches, such as molecular docking and molecular dynamic simulations, provide economic and efficient tools for finding modulators of ion channels and for elucidating the action mechanisms of small molecules. In this review, we focus primarily on four types of ion channels (voltage-gated, ligand-gated, acid-sensing, and virus matrix 2 ion channels). The current advancements in computer-aided drug discovery and design targeting ion channels are summarized. First, ligand-based studies for drug design are briefly outlined. Then, we focus on the structurebased studies targeting pore domains, endogenous binding sites and allosteric sites of ion channels. Moreover, we also review the contribution of computational methods to the field of ligand binding and unbinding pathways of ion channels. Finally, we propose future developments for the field.

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.

The greater development in the computational tools and the presence of a huge number of established chemical libraries emerge the inquiry about the ability to integrate the computational techniques in the drug discovery process. This review emphasizes the role of computational tools and software to accelerate the steps in the drug discovery pipeline. The data were gathered from the trendiest research articles and reviews that were indexed in the well-established scholarly search engines. Different vital techniques, such as virtual screening, pharmacophore modeling, molecular docking, molecular dynamic simulations, and quantitative structure activity relationship, are widely employed for target recognition, lead refining, and forecasting binding behavior at the atomic level. The advancement in artificial cognitive computing software significantly expanded the capabilities for rapid analysis of substantial data sets and the generation of toxicity and bioactivity predictive models of drugs. The contributions of these software tools tackled global problems, like drug repurposing during the coronavirus outbreak, and the direction toward personalized medicaments highlight their crucial role in the swift discovery of new treatments. In addition, computational tools are widely employed to enhance the drug formulations, which leads to discovering drugs with optimum pharmacokinetic and toxicity profiles. The difficulties in analyzing the multi-target binding behaviors and the limited success rates of the generated candidates in the experimental validations were the main existing limitations. However, the steep development in the field of artificial intelligence and the hybrid biochemical-computational approaches provided promising horizons to tackle these limitations. Chemical computational tools significantly affect the future of pharmaceutical research by boosting the drug discovery process toward affordable and efficient medicaments.