These drugs are categorized according to their source, like synthetic, semisynthetic, and natural products. Patient situation/stage (lines) and mode of action must be considered during treatment (Ahamad et al., 2017). The finding and creation of new anti-TB therapeutics are widely recognized as one of the world’s most challenging public health issues; however, it is also a significant pharmaceutical challenge. Drug development is a lengthy procedure. Following a clinical trial, it can take decades for a molecule to reach the market. Computational approaches have aided drug development (Doreswamy and Vastrad, 2013; Ojo et al., 2021). The Quantitative Structure-Activity Relationship (QSAR) method is a powerful tool that is used in drug development all over the world. QSAR models are mathematical equations that show how chemical structures and biological processes are linked. The QSAR approach can potentially minimize the time and effort necessary to find novel compounds or increase the efficiency of current ones ( Adeniji et al., 2018). QSAR models are increasingly used with virtual screening and combinatorial libraries to predict the fate of physiologically active compounds (Ahamad et al., 2017). It is possible to use this method in the development of future drugs. Increasing the speed of QSAR-related studies would facilitate the design and optimization of new drug candidates (Adeniji et al., 2018). Another capability of these models is that they can provide a deeper understanding of biological activity mechanisms (Vastrad, 2012). In QSAR modeling, various descriptors were employed, such as constitutional, geometrical, topological, quantum chemical, and other descriptors ( Ojo et al., 2021). This approach might be applied to predict the activity of newly proposed compounds before their synthesis and evaluation ( Vastrad, 2012; Adeniji et al., 2018). Molecular docking is a module that allows two or more molecules to recognize one other by matching their geometry and energy. It is a valuable tool in drug development for establishing the compatibility of molecules (ligands) with their target (receptor). It helps determine how a receptor interacts with its ligand and elucidates its binding process ( Qing et al., 2014; Adeniji et al., 2018).

A pharmacophore must possess several chemical qualities to elicit a response from a receptor target. Pharmacophore models may be created using either a receptor alone or a receptor-ligand combination (Wermuth et al., 1998; Leach et al., 2010; Macalino et al., 2020). Pharmacophores are schematic representations of the main aspects of molecular recognition that may be used to represent and identify compounds on a 2D or 3D level. Physicophore model-based database screening is essential for computer-aided drug development since it gives information on receptor interaction’s geometric and electrical aspects (Qing et al., 2014). According to IUPAC, a Pharmacophore is a combination of steric and electronic qualities required for interaction with a target structure to trigger a biological response. Using a mix of pharmacophore model-based screening and docking studies to find novel drugs has been proven effective (Leach et al., 2010). Pharmacophore screening and docking can be combined to speed up the discovery of new drugs and improve their chances of survival (Macalino et al., 2020). Using molecular docking and other bioinformatic methods to evaluate candidate compounds before in vitro cell culture assays or chemical changes can help speed up drug discovery. Pharmaceutical and medicinal chemists can use QSAR and molecular docking investigations to design and synthesize novel anti-TB drugs (Adeniji et al., 2018).

The process of drug discovery and the development of a novel medicine is costly and time-consuming. Several laboratories and in vivo tests are used to determine therapeutic effectiveness and health risks. As a result, new methods are being developed to limit animal use in research, reducing ethical (and budgetary) concerns ( Bajot, 2010).

The computational tools are mainly utilized to.

(i) perform the molecular structure confirmation (e.g., molecular dynamics);

There are mainly two drug design techniques: structure and ligand-based drug design (SBDD and LBDD). Using SBDD, any target inhibitor molecule can be designed, while LBDD primarily focuses on the chemical interaction between the target receptor and the inhibitor ( Abdel-Ilah et al., 2017). The drug design techniques and groups are presented in Figures 1, 2 (Abdel-Ilah et al., 2017).

1. The simple linear regression (SLR) method generates a QSAR model in equations using a standard linear regression calculation. This technique has proven to be quite promising for developing structure and activity correlations (Verma et al., 2010). Using a straight line, SLR models the relationship between a single independent variable and a dependent variable. It assumes a linear relationship and is widely used for prediction and understanding how the variables are related. The method is simple, interpretable, and widely applied in various scientific fields. SLR might not capture complex relationships that involve multiple variables (Draper and Smith, 1998). Linear Methods, Used in modeling structure-activity relationships in drug discovery, predicts novel compounds’ biological activity (Krishnapuram et al., 2005).

Despite significant advancements in drug design, descriptors used to designate the molecular structure of biologically active compounds remain the primary method for identifying novel lead molecules. For QSAR/QSPR investigations, descriptors are numerical representations of the chemical properties of a molecule. For statistical model construction to be possible, the mathematical representation of these descriptors must be independent of the molecule’s size and number of elements. In QSAR/QSPR modeling, molecular descriptors have evolved into the most important variables. The information conveyed by descriptors typically depends on the type of molecular representation and the defined algorithm for its calculation. Among these are topological indices, geometrical, structural, and physicochemical descriptors.

Constitutional descriptors are basic, widely-applied descriptors that reflect the molecular composition of a compound without providing information about its topology. The most common constitution descriptors are the number of atoms, number of bonds, variety of atoms, ring count, and molecular weight (MW). These descriptors are insensitive to conformational changes and do not distinguish between isomers.

Recent developments in lead discovery, drug design, virtual screening, combinatorial library design, and database search discrimination also highlight the importance of topological descriptors in drug discovery. Topological indices (TIs) are two-dimensional descriptors that consider the intrinsic atomic arrangement of compounds. These descriptors are derived from the topological representation of molecules and can be considered structure-specific. These indices encode numerical information regarding the molecular size, shape, branching, presence of heteroatoms, and number of bonds. By the nature of chemical bonds, these TIs represent the interconnectedness of atoms within molecules. They play a crucial role in modeling various physicochemical properties, biological activities, and pharmacokinetic properties. A molecular graph represents a topological representation of a molecule. This graph is denoted mathematically as G = (V, E), where V is the set of vertices corresponding to the atoms of the molecule, and E is the set of elements representing the binary relationship between pairs of vertices. These chemical graphs depict the molecular structure in a non-numerical format; however, a numerical translation of the graph is required to calculate topological descriptors. The most commonly used descriptors are the Wiener index, the Connectivity indices, the Kier shape, the Balaban J index, and the Zagreb indices. The primary function of these indices is to classify molecules according to their size, degree of branching, flexibility, and overall morphology (Wiener, 1947; Randic, 1975; Balaban, 1982; Roy, 2004).

The 3D coordinates of the atoms in a given molecule are used to derive geometrical descriptors. In comparison to topological descriptors, these descriptors are abundant in information and discrimination power for analogous chemical structures and molecule conformations. In addition, they contain data acquired from atomic van der Waals regions and their overlap on the molecular surface. Despite their high informational density, these descriptors typically have disadvantages as well. Geometric descriptors necessitate geometry optimization and, consequently, the computational burden to calculate them. Thus, for flexible molecules that can assume multiple conformations, new information can be utilized. Nonetheless, this results in the issue that complexity can increase substantially. Moreover, alignment constraints are required for the majority of these descriptors (grid-based descriptors) to accomplish molecule comparability. A molecule’s physical and chemical properties that can be estimated from its two-dimensional structure are physicochemical descriptors. These properties play a significant role in determining the substance concentration in the body. A drug’s efficacy and, consequently, its market value can be enhanced by its possessing the appropriate properties. Thus, examining these properties of a drug not only contributes to the drug’s safety profile but also plays a crucial role in drug discovery by optimizing the compounds chosen. In addition to selecting candidate compounds with the appropriate physicochemical properties, it is necessary to pay special attention to properties such as lipophilicity, solubility, and permeability, which can ensure optimal potency (Leo et al., 1975; Ghose and Crippen, 1986). In contrast, molecular fingerprints have been utilized for decades to investigate large chemical libraries for similar compounds. The information content of 2D signatures is derived from atoms, bond types, and graph distances derived from chemical graphs, where these are represented as bits. Each bit indicates the presence or absence of a predetermined substructure in a compound. A bit in a structural fingerprint corresponds to a chemical property, typically the presence of some substructure. Based on the similarity to a biologically active molecule, these biomarkers enable researchers to identify additional compounds with a higher probability of displaying similar biological potency against the same target. Molecule properties, such as chemical diversity in chemical space, can be characterized using fingerprints. Such evaluations are crucial in the compound selection process before experimental screening. Numerous fingerprint varieties can be used for structural comparisons of various sorts. Such signatures have become a popular option for drug discovery because they offer a decent balance and empirically reasonable proportionality. By quantifying fingerprint overlap with similarity coefficients and using the resulting values to measure molecular similarity, fingerprint similarity searching generates a database ranking. A Tanimoto coefficient (Tc) value of >0.85 indicates a high probability that the test compounds have similar bioactivity. However, Tc is only useful for data with comparable levels of complexity; other coefficients are preferred for data with varying levels of complexity (Willett, 2006; Rogers and Hahn, 2010; Helal et al., 2016).

Initially, 2D fingerprints were designed for similarity searching using a single template, but some studies claim that search performance is enhanced when multiple reference compounds are employed. MACCS, PubChem, and Extended-Connectivity biometrics (ECFP) are the most commonly used biometrics. MACCS is a compilation of 166 bits that encompasses most of the chemical characteristics important for virtual screening. The PubChem signatures database contains 881 bits of descriptors for element counts, aromatic or nonaromatic ring counts, atom pairs, atom neighborhoods, and particular fragments. These 2D signatures have been successfully utilized in the virtual screening of novel active compounds. Using PubChem bioassays, comprehensive bioactivity profiles, dubbed “PubChem high-throughput screening fingerprints” (PubChem HTSFPs), were recently developed. In addition, these PubChem-HTSFPs were utilized in hit expansion experiments for 33 unique targets. These signatures were useful for retrieving matches with structural diversity and the desired bioactivities. ECFPs are a novel class of 2D circular signatures used for molecular characterization. These signatures are an extension of the Morgan algorithm. These circular fingerprints have numerous advantageous characteristics, including i) being easy to calculate; ii) representing a large number of distinct features; and iii) not being reliant on predefined features; thus, they can represent novel structural variation. These fingerprint types effectively employ encoded rich data for similarity searching, compound clustering, and chemical library analyses. In addition, ECFPs are frequently employed in QSAR and QSPR model development for lead optimization and ADMET property forecasting (Willett, 2006; Rogers and Hahn, 2010; Helal et al., 2016).

Therefore, concerning anti-tubercular drug discovery, a combination of different machine-learning-assisted QSAR models is required while implementing molecular descriptors and molecular fingerprints of molecules’ datasets for feature selection against any biological target. The best way to deal with major demerits of different types of molecular descriptors is to include two or more molecular descriptors while constructing the hybrid descriptors-based QSAR models. Hybrid molecular descriptors incorporate multiple molecular characteristics, including topological, electronic, and geometric properties. This exhaustive representation accounts for a broader range of factors that contribute to the activity of a molecule, thereby enhancing the model’s predictive accuracy. The same approach should be applied while considering molecular fingerprints for anti-tubercular drug design by constructing a hybrid fingerprint-based QSAR. Therefore, the use of both hybrid molecular descriptors and molecular fingerprint-based QSAR models will have a distinct and interpretable relationship with the activity of a molecule that will additionally aid researchers in analyzing the contributions of particular characteristics or substructures to the overall activity, thereby facilitating the design of novel compounds with the desired properties. Apart from this, hybrid molecular descriptors and molecular fingerprints can represent a wide variety of chemical structures, including novel and unconventional compounds. This adaptability is essential for investigating new chemical space and identifying potential drug candidates with distinctive structural characteristics. In ensemble modeling, where multiple QSAR models or screening methods are combined to improve predictive performance, hybrid molecular descriptors and molecular fingerprints can also be used. This strategy combines the advantages of various techniques, resulting in more accurate and robust forecasts. Due to their ability to provide a comprehensive, interpretable, and computationally efficient representation of molecular structure and activity, hybrid molecular descriptors and molecular fingerprints are favored for QSAR modeling and drug design. These techniques have significantly accelerated the drug discovery process by allowing researchers to prioritize and design promising compounds for further experimental evaluation (Willett, 2006; Rogers and Hahn, 2010; Helal et al., 2016). As computational methods continue to advance, hybrid descriptors and molecular signatures will likely play a greater role in shaping the future of drug development against tuberculosis.

Chemical space is vital in drug development for various purposes, including library creation, compound classification, selection, structure-activity relationship (SAR) investigation, and understanding structure-property connections (SPR) (Naveja Medina, 2019). It encompasses all descriptors derived from chemical compounds. The chemical space analysis for FDA-approved drugs showed shared traits, including substantial halogen content and molecular weights below 500, consistent with Lipinski’s rule of five. This rule suggests that drugs with an MW less than 500 have better bioabsorption and bioavailability (Prachayasittikul et al., 2015). Chemical space represents the collection of all potential organic compounds, and as its scale increases, cartographic approaches are used to visualize and conceptualize it. Machine learning methods benefit from chemical space approaches as they identify clusters of related compounds. Lipinski’s rule of five indicates that drug-like compounds exhibit drug resemblance with MW < 500 Da, clogP <5, few H-bond donors (<5), and few H-bond acceptors (<10) (Ganesan, 2008). Optimizing this rule is essential for improving BBB permeability by passive diffusion. Factors like MW, log P, and H-bond donors and acceptors influence drug interaction with the blood-brain barrier (BBB) (Lambrinidis and Tsantli, 2018). By analyzing the chemical composition of natural compounds and determining their molecular structures, the investigation identified 11 novel inhibitors for ß-hydroxysteroid dehydrogenase type 1 ( Koch et al., 2005). According to Reayi, diversity-oriented synthesis (DOS), a chemical synthesis strategy for swiftly building a library of compounds, can help deorphanize druggable protein targets ( Reayi and Arya, 2005). ChemGPS-NP was used to examine the chemical space of natural products from different databases (Larsson et al., 2005). They discovered that 40,348 compounds from the Dictionary of Nature Products Database passed Lipinski’s rule of five ( Rosén et al., 2009). To define “bioactive natural compound-likeness,” (Zhou et al., 2010), used structure-activity relationships to investigate the chemical space of natural products, comparable to Lipinski’s rule of five (drug-likeness) (BNC-likeness). The drug-likeness and BNC-likeness models were utilized to compare the structural properties of bioactive and non-bioactive natural products. The Ethnobotanical Database and Dr. Duke’s Phytochemical Database were used to create a dataset of 1,580 natural products from a total of 7,549 natural product constituents. 790 natural compounds were bioactive, while the remaining 790 were not, resulting in a well-balanced dataset. Bioactive natural compound-likeness models were created using SVM with radial basis function kernels, and the training set consisted of 1,580 bioactive substances. An independent external data set of 81 bioactive and 81 non-bioactive natural compounds from commonly used medicinal plants were used to test the models’ performance. The prediction results effectively classified 75 bioactive chemicals, indicating the robustness of the models and their immunity to overfitting. One of the issues with machine learning is overfitting, which arises when noise data is used as an independent variable in developing highly predictive models ( Zhou et al., 2010).

The validation approach tries to provide a model with defined descriptors that is statistically trustworthy because of a cause-and-effect connection rather than a random numerical link. Validation procedures are required to determine a model’s predictive ability. Internal and external validation techniques are the two sorts of validation methods accessible. Training datasets are used by internal techniques such as Q² (squared correlation coefficient), R ² (coefficient of determination or coefficient of multiple determination for multiple regression), chi-squared (X²), and root-mean-squared error (RMSE). When applied to new data sets, the model’s lack of predictability is a significant flaw in this technique. External approaches, on the other hand, are based on the testing set and are considered the most reliable validation ( Abdel-Ilah et al., 2017; Kwon et al., 2019). These statistical methods ensure that the models produced are accurate and unbiased. Cross-validation approaches such as accuracy (ACC), sensitivity (SEN), specificity (SPEC), and Matthew’s correlation coefficient (MCC) ( Prachayasittikul et al., 2015) can be used to assess the model’s internal predictive capability. In contrast, its external predictability can be evaluated using a separate set of molecules (the test set) that were not used in the model creation ( Testa, 1995; Verma et al., 2010). The CV method begins by removing one or more compounds from the training set, which serves as a temporary test set. The remaining data points are used to form a CV model, which is then tested on the deleted molecules to see if it can accurately predict bioactivities using the descriptors from the original model (Figures 4, 5).

External validation, the Y-randomization test, the domain of applicability, and the William plot are some statistical techniques that can be used to assess a QSAR/QSPR model’s predictive ability. External validation is a method of testing a QSAR/QSPR model’s external predictivity by omitting a section of data at the start of the experiment and using the remaining internal set to evaluate optimal learning algorithm parameters ( Prachayasittikul et al., 2015).

The accuracy (ACC), sensitivity (SEN), specificity (SPEC), and Matthew’s correlation coefficient MCC) are used to evaluate the prediction performance of the proposed QSAR/QSPR model using cross-validation (CV), which is given by the following formula Eqs 1–4 A c c u r a c y = T P + T N T P + T N + F P + F N × 100 (1) S e n s i t i v i t y = T P T P + F N × 100 (2) S p e c i f i c i t y = T N T N + F P × 100 (3) M C C = T P × T N − F P × F N √ T P + F N T N + F P T N + F N (4)

TP, TN, FP, and FN- Numbers of true positive, true negative, false positive, and false negative.

Matthew’s correlation coefficient (MCC) measures the gap between actual and expected values. The coefficient is a balanced measure that can be employed when the classes are of various sizes and take into consideration true negatives (TN), true positives (TP), false negatives (FN), and false positives (FP). The equation is used to derive the formula. In machine learning, Matthew’s correlation coefficient (MCC) is used to assess the quality of binary and multiclass classifications. Like most correlation coefficients, MCC has a range of 1 to 1, with 1 denoting the best agreement between actuals and forecasts, −1 denoting an inverse prediction, and 0 denoting no agreement. Alternatively, the prediction is random in comparison to the actual situation ( Mun and Geng, 2019).

Due to an influx of available data and increased computer capacity, machine learning (ML) technologies are coming back in drug development investigations. This has sparked a flurry of artificial intelligence (AI) drug development investigations, in which machine learning (ML) and deep learning (DL) approaches are used to solve issues efficiently and intelligently. Combining structural, sequence, and evolutionary data yields machine-learning models (Verma et al., 2010). An analysis of hydrophobicity, side-chain pKa, solubility, solvent accessibility, and other physiochemical parameters is conducted, as well as the development of a machine-learning model to predict binding and non-binding residues. Only a few models predict binding sites based on one of the binding partners’ structures, whereas others employ information from both partners ( Kumar et al., 2018). The two types of machine learning methodologies are unsupervised and supervised learning. Labels are allocated to training data in supervised learning, and the model can predict labels for specific data inputs once it is ready. On the other hand, unsupervised machine learning algorithms can profit directly from unlabeled molecular pattern data, as they require input data and no output components (Supplementary Table S7) (Singla et al., 2013).

Ehrlich coined the pharmacophore as “a molecular framework that conveys (phoros) the key properties responsible for a drug’s (pharmacon) biological activity” in 1909 ( Yang, 2010). According to the IUPAC nomenclature (Leach et al., 2010), a pharmacophore is a molecule that possesses both steric and electronic properties that enable it to produce efficient supramolecular interactions with a biological target structure and activate or inhibit its biological response (Figure 6).

To better understand ligand-protein interactions, pharmacophore models are developed. They may be utilized to discover new compounds that match the pharmacophore criteria and are thus expected to be active ( Muhammed and Aki-Yalcin, 2021). Using a mix of pharmacophore model-based screening and docking studies to find new drugs has been proven beneficial ( Mesli et al., 2021). If the target’s structure is unknown, a pharmacophore model can be created utilizing structural data from the active ligands that bind to it. The ligand-based pharmacophore modeling technique (Yang, 2010; Muhammed and Aki-Yalcin, 2021) is the name given to this approach.

When the target’s structure is known, pharmacophore models can be constructed utilizing the target’s structural attributes. This is the structure-based pharmacophore modeling approach (Vastrad, 2012). A whole framework of Pharmacophore is shown in Figure 6 (Muhammed and Aki-Yalcin, 2021). A list of pharmacophore modeling software is enlisted in (Supplementary Table S8) (Yang, 2010; Singla et al., 2013; Kumar et al., 2018; Schaller et al., 2020; Muhammed and Aki-Yalcin, 2021).

The pharmacophore analysis has the most important common denominator of the molecular interaction properties shared by a group of active compounds. It is an abstract idea rather than a physical molecule or combination of chemical groups ( Qing et al., 2014). Integrating knowledge about the three-dimensional nature of molecular interactions is another significant component of current pharmacophore research. This point of view is centered on 3d-pharmacophore techniques (Leach et al., 2010), which define the spatial relationship between pharmacophore features. Virtual screening, de novo design, lead optimization, and multitarget drug design have all utilized pharmacophore-based approaches ( Yang, 2010; Kumar et al., 2018; Muhammed and Aki-Yalcin, 2021). There are limitations with pharmacophore scoring functions that limit its ability to realize its intended potential, especially with the current high cost of discovering and developing new medicine. The involvement of other computational methods is critical in overcoming these obstacles. As a result, combining pharmacophore modeling with different computational approaches addresses some of these constraints ( Yang, 2010; Kumar et al., 2018).

A pharmacophore model is comprised of several characteristics that are arranged in a three-dimensional (3D) pattern. The characteristics can be labeled as a single feature or any logic combination consisting of “AND,” “OR,” and “NOT” ( Qing et al., 2014) to blend varied interaction patterns under a single label. Hydrogen bond donors (HBD), hydrogen bond acceptors (HBA), positive features, negative features, aromatic rings, hydrophobic features, and their combinations are all examples of molecular pharmacophore patterns ( Seidel et al., 2020). Different compounds can be compared at the pharmacophore level, known as “pharmacophore fingerprinting.” The pharmacophore is a “query” when only a few pharmacophore properties are analyzed in a 3D model ( Qing et al., 2014). Classification of 3D-QSAR is given in Supplementary Table S4. Pharmacophoric characteristics may be used as a query to search for possible leads from chemical compound databases and produce compounds with specific qualities (lead optimization) ( Qing et al., 2014; Kumar et al., 2018; Muhammed and Aki-Yalcin, 2021). It also uses pharmacophore fingerprints to determine the comparability and diversity of drugs. It may also align molecules based on their three-dimensional arrangement or create a predicted three-dimensional QSAR model ( Kumar et al., 2018). The webservers or databases for QSAR drug design research are given in Supplementary Table S1-3). A list of software used for calculating descriptors and fingerprints is presented in Supplementary Table S5, 6.

Pharmacophore modeling employs organized methods to construct a logical framework to identify other chemical moieties with similar properties against the disease’s target of interest. The steps are as i) ligand synthesis, ii) mapping of pharmacophore features, iii) looking for a common pharmacophore, and iv) calculating the shared pharmacophore score ( Kumar et al., 2018). A pharmacophore can be used to screen new target-specific agonists and antagonists, toxicants, undiscovered targets, and the best molecular docking findings. A pharmacophore model is often used in virtual screens to locate medications that trigger the required biological activity (Yang, 2010; Muhammed and Aki-Yalcin, 2021), as well as fishing drug targets, ligand profiling, docking, and ADMET (absorption, distribution, metabolism, excretion, and toxicity) prediction ( Leach et al., 2010; Yang, 2010; Qing et al., 2014; Muhammed and Aki-Yalcin, 2021).

The most extensively used strategy for structure-based drug development is molecular docking, which has been around since the early 1980s ( Meng et al., 2012). Molecular docking provides an attractive framework for studying drug biomolecular interactions, which is useful for rational drug design and discovery Dar and Mir, 2017). The molecular docking approach may be used to model an atomic-level interaction between a small molecule and a protein, allowing us to characterize small molecule behavior at target protein binding sites and highlight key biochemical processes ( Meng et al., 2012; Aspatwar et al., 2019) (Figure 7) (Ferreira et al., 2015).

When the target protein’s 3D structure is available, molecular docking is one of the most commonly used virtual screening approaches. It is possible to predict both the affinity of the ligand for a protein and the structure of the protein-ligand complex by using this technique, both of which are vital to lead optimization ( Wang and Zhu, 2016). Molecular docking aims to predict the structure of the ligand-receptor complex using computational methods. Docking is accomplished in two steps: sampling ligand conformations in the protein’s active site and then utilizing a scoring function to rate these conformations (Meng et al., 2012; Ferreira et al., 2015). A complete list of docking and ADME software are enlisted in Supplementary Table S9 (Meng et al., 2012; Singla et al., 2013; Ferreira et al., 2015; Dar and Mir, 2017; Kumar et al., 2018; Zhang et al., 2022) Supplementary Table S10 (Singla et al., 2013), respectively.

Covalent inhibition is a method for obtaining irreversible inhibition. Because covalent inhibitors may target proteins with shallow binding cleavage, new inhibitors with better efficacy than non-covalent inhibitors can be developed. Covalent molecular docking has recently been used in computer-aided drug design processes to characterize covalent interactions between inhibitors and biological targets. Several computational approaches for modeling covalent interactions have been developed. Autodock, Autodock Vina, GOLD, and FlexX are the most popular docking tools and software.

On the other hand, these and other similar methods primarily focus on non-covalent interactions (van der Waals interactions, electrostatic interactions, and hydrogen bonding) or the use of alternative empirical or knowledge-based scoring functions to characterize these non-covalent interactions ( Kumalo et al., 2015). Non-covalent interactions (van der Waals interaction, electrostatic interaction, and hydrogen bonding, for example,) or alternative empirical or knowledge-based scoring functions to characterize these non-covalent interactions are mainly used in these and other analogous methodologies. However, not all medications attach non-covalently to the active site; other compounds, such as covalent drugs (Singh et al., 2011), bind covalently. The workflow of covalent docking is represented in Figure 8 (Kumalo et al., 2015).

Many techniques have been developed to achieve the covalent docking of inhibitors to target proteins. However, most covalent docking methods can only predict the binding energy between an electrophilic ligand and a nucleophilic receptor. Popular is the “link atom” approach. The software defines a “link atom” in the ligand and the protein in this strategy. The ligand link atom must occupy the same steric volume as the protein link atom to mimic the covalent binding process. The Gold molecular docking program ( Hartshorn et al., 2007) implements this strategy. Autodock, another widely used molecular docking program, uses a “grid-based technique” and a “modification of the flexible side chain” approach to covalently dock inhibitors to receptors. The covalently linked ligand and the protein attachment are sampled as part of the receptor as a single flexible side chain in a flexible side chain. A Gaussian biassing function focused on the protein attachment atom, and grid-based energy is used in the grid-based approach to bias the covalent bonding ligand posture ( Kumalo et al., 2015).

Katritch et al., 2007, used covalent docking and homology modeling to create a detailed structural model of the ubiquitin-like poxvirus proteinase (ULP) I7L substrate-binding site (S2–S2′). The 3D model of the I7L ligand-binding site was utilized to perform covalent docking and virtual screening of a comprehensive library of around 230,000 accessible ketone and aldehyde compounds to uncover novel smallpox antiviral hits. Out of 456 predicted ligands, 97 inhibitors of I7L proteinase activity were found to be active in biochemical studies (20 percent overall hit rate) ( Katritch et al., 2007). In their study, Wang et al. employed covalent and three-dimensional QSAR modeling to investigate the intermolecular interactions of isatin sulfonamide analogs as caspase-3 inhibitors (Wang and Zhu, 2016). Fifty-nine isatin sulfonamide analogs were docked to the binding site of human caspase-3. The docking research showed the inhibitors’ binding mechanism. A 3D-QSAR approach further supplements docking analysis by offering a “custom” scoring function for the protein under research, capable of predicting bioactivities for ligands comparable to those in the training sets. Structure-based design methodologies (such as docking) were shown to aid in constructing trustworthy QSAR models.

Ma et al. developed and covalently docked a novel family of peptide aldehyde derivatives to increase hydrophobic interactions with a bulky P3 moiety. Covalent docking was used to predict the exchange of the peptide aldehyde compounds with the 20S. The P3-position alterations are crucial for inhibitor efficacy. The docking behavior is similar to that of the crystal complex previously described. The hypothesized binding method might be leveraged to generate more potent 20S proteasome inhibitors ( Ma et al., 2011). A bicyclic class of covalent inhibitors was synthesized by Lawandi et al. to study the optimal shape required to target propyl oligopeptidase (POP) and heal human brain disorders. We hypothesized that these structures could bind covalently to the enzyme’s catalytic serine because they contain nitrile functional groups. From the covalent docking study, two compounds were chosen for production and biological testing. The study unveiled the presence of a potent, remarkably selective, and cell-permeable inhibitor for POP.

Furthermore, docking tests (using the FITTED docking engine and default parameters) demonstrated that the stereogenic center’s arrangement at the ring junction is a limiting factor for optimum activity, which may be used to guide future studies ( Lawandi et al., 2009). Saikia et al. study delve into the intriguing interaction between the antitubercular drug isoniazid (INH) and pristine and Si-doped and single-walled carbon nanotubes (SWNTs) (Saikia et al., 2016). The incorporation of silicon dopants significantly enhances the adsorption of INH onto the relatively inert nanotubes, as evidenced by profound alterations in adsorption energies, charge transfer phenomena, and global reactivity descriptor values. Notably, the study highlights noncovalent functionalization’s efficiency and mobility advantages over covalent counterparts, particularly in facilitating INH movement along the nanotube sidewall. Interestingly, parallel adsorption configurations manifest heightened charge transfer dynamics compared to the perpendicular adsorption orientation. The theoretical investigation posits single-wall carbon nanotubes as promising nano vectors for PZA (pyrazinamide) drug delivery, with covalent functionalization via sidewall and edge attachment, presenting a viable strategy. The study’s findings pave the way for further exploration, aiming to unravel the intricacies of therapeutic release from functionalized SWCNTs, ultimately enriching the landscape of future biomedical applications (Saikia et al., 2013). The list of software used is given in Supplementary Table S11 (Singla et al., 2013).

Molecular dynamics (MD) are more computationally demanding and sophisticated than simulations of other biomolecules. A collection of biomolecule conformations with distinct initial and boundary conditions can be generated by iteratively integrating (numerically) the equations of motion for specific potential simulations ( Kumar et al., 2018). A structural ensemble derived from an MD simulation is used to explore the conformational space of biomolecules, measure thermodynamic variables, and predict the free energy of biological processes. In calculations involving free energy binding, which encompasses a wide range of accuracies and processing needs, many people have tried the MD method to forecast the strength of non-bonded interactions. A very vital technique for the theoretical and computational studies of biomolecules is molecular dynamics simulation (MDS) ( Huang et al., 2010). Examining molecular systems is exceedingly tricky since they usually comprise many particles. Using numerical approaches in molecular dynamics simulation can avoid this analytical intractability. The atoms and molecules may briefly interact while the simulation is running. Each atom’s motion is calculated, and the overall behavior may be checked. It offers numerous advantages over docking because docking merely provides the ligand’s binding free energy among the receptor. MDS can also be used to predict the ligand’s actual interaction with receptors at the atomic level ( Ferreira et al., 2015; Kumar et al., 2018).

Root means square deviation (RMSD) is applied in MDS to forecast receptor or ligand-receptor complexes for their stability and to explain conformational changes. An alternate conformational state consequently to such ligand-induced structure can be generated using MD simulations ( Khan et al., 2016). The projected ligand-receptor complex can be termed unstable if the A-matched docking solution and the ligand conformation produced by MD are different by more than a particular RMSD value ( Kumar et al., 2018). Coupled docking and MD approaches have been widely used to identify new therapeutic drugs from natural compounds and optimize the more recent lead candidate obtained from the natural compound ( De Vivo et al., 2016). MD is also applicable to produce a section of convenient docking structure, mainly when none of the acceptable crystallographic structures for the specific molecular target are offered ( Harvey and De Fabritis, 2012). MD simulations paired with molecular mechanics/Poisson-Boltzmann surface area (MM/PBSA) approaches can provide precise information on drug-target interaction binding effectiveness. This method has also been used to test the inhibitory efficacy of natural chemicals against various protein targets ( Good, 2006).

Using the MD simulation approach, various ways are available to examine atomic-level alterations in biomolecules (Khan et al., 2016 ). Some, like Desmond, have a graphical user interface, while others, like GROMACS and AMBER, run via command lines. GROMACS (AMBER), Nanoscale MD (NAMD), and (CHARMM-GUI) are some well-known and commonly used MD simulation programs. The rise in software and hardware powers is crucial for executing such MD simulations ( Kumar et al., 2018).

It is a popular method for researching biologically known systems. It is also employed in various domains, including the Prediction of protein-ligand combination stability, protein mutation analysis, conformational protein stability prediction, and protein unfolding investigations. It is a powerful instrument demanding much computing power to solve biological puzzles ( Hansoon et al., 2002; Hospital et al., 2015).

A QSAR study was conducted on thirty-four 8-methyl quinolones by Eric et al. to determine their anti-tuberculosis activity. Picking descriptors and building association models relating to structural characteristics of biological activity was done using the genetic algorithm (GA) and multiple linear regression analysis (MLRA). The 3D structure of all compounds was constructed using the SPARTAN “14 v 1.1.0 tool. PADEL software for calculating molecular descriptors of all 34 compounds. The Build QSAR application was used to analyze geometric algorithms and build QSAR models. The internal validation was performed using the cross-validation leave-one-out technique, and external validation was performed by utilizing them to forecast the action of test sets. An examination of the robustness of the QSAR design was also conducted. The GA-MLRA method was used to construct a robust QSAR model to predict the inhibitory action of some quinolones. The results of this study (R ² Pred = 0.7393) imply that for novel 8-methyl quinoline analogs, the pMIC can be calculated using the QSAR model that comes under the model’s applicability domain before synthesizing them (Eric et al., 2016). It applied various computational methodologies to the 2,4-diamino quinazoline moiety to assess its efficacy in tuberculosis between diverse biological functions (Bose et al., 2019). V-Life MDS software was used to study all QSAR studies. Merck molecular force field was used to get 3D structures from 2D QSAR. In 3D QSAR, conformers were created using the conformational Monte Carlo technique, and the conformers with the lowest energy were chosen to align. The pharmacophore identification investigations were performed in the V-life MDS 4.4 Mol sign module to associate geometrical representation of the properties required for the molecule to show activation. The docking investigation was conducted using GOLD software. Thirty-three molecules were docked with four distinct protein molecules, significant for anti-TB action and crystalline protein structure. The new molecular set was designed depending on the pharmacophore results of docking studies and QSAR. After designing, the new molecule set was optimized, and using Monte Carlo conformation search conformers were generated. The 2,4-diamino quinazolines scaffold is promising for development as a molecular lead set for lead optimization, according to the findings of this study. According to the QSAR study, better moieties require a perfect distribution of steric potential and hydrophobicity in the molecular system. Pharmacophore mapping depends on the quinazoline ring’s 1 and 3 nitrogen atoms, the 4-amino phenolates molecule at location 4, the benzene ring, and the electronegative fluorine replacement. According to QSAR and pharmacophore mapping data, replacing the piperidine ring at the 2-amino position and the 4-amino phenolate group over the 4-amino position is the main necessity, as evidenced by the design and assessment of the activity using a 3D QSAR model. All of the features assessed and assumed to represent an active moiety were included in the final molecule ( Bose et al., 2019). Bhardwaj et al. explored the binding affinity of 70 novel piperine analogs and reported the activity of 23 compounds against Mycobacterium tuberculosis. The antitubercular effects of verapamil analogs, which are derivatives of dimethoxy phenyl rings bound to carbon chains, are significant. However, piperine, which also has a methylenedioxyphenyl ring attached to a carbon chain, is found to be a structural requirement for its activity. The similar action of these two compounds was correlated using in silico approaches. Using DRAGON and Chem office software, descriptors were calculated in QSAR studies. The QSAR models were generated by performing MLRA (Multiple Linear Regression Analysis) analysis using CODESSA^®. The structures of the compounds were sketched and optimized using Chem Sketch software. Schrodinger, 2016–1’s GLIDE module investigated drug-receptor interactions and designed new compounds. www.rcsb.org provided the protein structure (3C3W), or the Protein Prep Wizard module was used to make the protein. The binding cavity within the protein was determined using site mapping, which will be utilized in future studies. Using a 2D sketcher, the structure of the ligands was created and optimized. For energy optimization, PLS3 was employed. It was concluded from QSAR and molecular docking studies that the binding energies range of predicted products with one hydrogen bond to five hydrogen bonds was higher than reported compounds with one hydrogen bond to four hydrogen bonds. Compared to reference medications, some predicted molecules have shown good binding affinity. As the docking scores are comparable, we may conclude that the QSAR models created are good, and these are applied for forecasting the anti-TB activity of novel drugs ( Bhardwaj and Dubey, 2017). The findings from the study underscore the power of integrating diverse computational strategies in the pursuit of anti-tubercular drug discovery. The combination of molecular docking, DFT calculations, reactivity descriptors, QSAR modeling, ADMET evaluation, and molecular dynamics simulations presents a formidable approach to expedite the identification of promising drug candidates (Stanzione et al., 2021). Ultimately, these advances contribute significantly to the ongoing battle against TB, offering hope for developing more effective treatments in the near future (Rajkhowa and Deka, 2013; Rajkhowa et al., 2015). A list of products discovered for tuberculosis is presented in Table 1. A list of the drugs (Table 2) in a clinical trial is discovered with the help of these approaches (Ahamad et al., 2017).