**2. Method**

CDFT is used to predict, analyze and interpret the reactivity properties of small drug molecules. Global reactivity descriptors, Fukui indices [11] and the Dual descriptors proposed by Morell et al. [12] are used for the analysis. These theories have been validated by a large number of studies [13–18]. A better microscopic insight to the whole interaction process can be observed by global indices and the other derived reactivity indices for the interacting complexes. In our work, we have selected design and structure-based, informatics-based, fragment-based, smallmolecule microarray screening, dynamic combinatorial screening and use of phenotypic assays based 22 small drug molecules to identify RNA-binding molecules. These drug show several desirable properties as good absorption, distribution, oral bioavailability and have ability to target bulges, loops, junctions, pseudo-knots, or higher order structures. The optimized structures for these 22 small drug molecules are given in **Figure 1**. We have computed relevant electronic properties of the studied drug including global parameters such as EHOMO, ELUMO, Energy gap, IP, EA, electronegativity (χ), global hardness (η), global softness(S), Chemical potential (μ) Softness (S), Electrophilicity index (ω), fraction of electrons transferred (Δ), Nucleophilicity index as well as local ones (Fukui functions and dual descriptors). CDFT results predicted structural and thermodynamic stability and low reactivity for the complexes. Also few complexes were identified as fluorescent biomarkers as their emission lies in the visible region [19]. In another study, the

*Transformation of Drug Discovery towards Artificial Intelligence: An* in Silico *Approach DOI: http://dx.doi.org/10.5772/intechopen.99018*

**Figure 1.** *Optimized structures of 22 small drug-like biologically active molecules. Adopted from Ref. [19].*

global and local descriptors are calculated to study ten Anti-inflammatory steroids (AIS) to understand the structure–activity relationship. The toxicity evaluation of drug and the pharmacokinetic parameters responsible for bioavailability and bioactivity are carried by the bioinformatics Osiris/Molinspiration [20, 21] tools. The physico-chemical properties are studied by G09 software tools. As the structures of small-molecule drugs increase in complexity, the importance of synthetic and *in silico* approaches both play an important role in understanding of ligandreceptor interactions within target classes. The predictive drug discovery tools offered a high degree of specificity within molecular design. Careful arrangement of structural features about a molecule necessitated efficient and practical approaches to model these privileged structures. We have also used similar computational tools to study the 21 molecular new chemical entities (NCEs) approved for the first time by a governing body anywhere in the world during 2019 [22]. Out of 11 therapeutic areas, 10 therapeutic areas as anti-infective/antibiotic, cardiovascular and hematologic, neurological (central nervous system (CNS)), dermatologic, inflammation and immunologic, metabolic, musculoskeletal, oncologic, reproductive, and respiratory drugs were selected. See **Figures 2** and **3**. Osiris Calculations were carried out to predict the toxicity risk in the drug molecules. Results showed drug conform behavior for all studied drugs except Triclabendazole, Trifarotene, Alpilisib and Ertafinitib, which shows high risks of undesired effects like mutagenicity, tumorigenicity, irritating effects and reproductive effects [23].

The understanding of various types of interactions is also crucial for the druglike molecule and its target. These possible interactions between a drug and target consist of covalent bonds, dipole–dipole interactions, ion-dipole interactions, ionic interactions, hydrogen bonding, hydrophobic interactions and charge transfer. The mechanism of drug action can be explained with ionic interactions as during

#### *Density Functional Theory - Recent Advances, New Perspectives and Applications*

#### **Figure 2.** *Chemical structures of studied New Chemical Entities (NCEs) (1-16).*

**Figure 3.** *Chemical structures of studied New Chemical Entities (NCEs) (17-21).*

physical pH condition several functional groups undergo ionization. The weak ion—dipole and dipole–dipole interactions both plays significant role in drugreceptor binding. Weak interactions include hydrogen bonds, hydrophobic interactions and charge transfer which exist between drug and receptor to provide stability to the drug-receptor binding. DFT is utilized to understand the reaction mechanisms of the drug molecule. Various Computational tools are used to precisely calculate the transition state for drug-target complexes. Dipole moment (DM) is also an important parameter which is used to explain observable chemical and physical properties of drug molecules. DM is used to assess cell permeability and oral bioavailability of drugs as complexes with large dipole moment are more soluble in water and less likely to be absorbed through lipophilic membranes

*Transformation of Drug Discovery towards Artificial Intelligence: An* in Silico *Approach DOI: http://dx.doi.org/10.5772/intechopen.99018*

[24, 25]. DM is included as a highly relevant descriptor in explaining the catalytic activity of enzymes in Quantitative Structure–Activity Relationships (QSAR) or Quantitative Structure–Property Relationships (QSPR) studies. For example: QSAR modeling of aromatase inhibition [26], antifungal activity [27], and HIV-1 protease/ cyclin-dependent kinases inhibition [28], QSAR modeling of aromatase inhibition [26], antifungal activity [27], and HIV-1 protease/ cyclin-dependent kinases inhibition [28], and in the estimation of micellar properties such as drug loading capacity (LC) [29] in QSPR model. Since DFT calculations are computationally too demanding for most large-scale virtual screening explorations, or for the incorporation in fast QSAR or QSPR models, so the relevant parameters are calculated by empirical or machine learning (ML) methods.

ML from data precalculated by DFT has emerged as a successful method for drugs as the results are highly accurate and has higher speed compared to the previous approaches [30, 31]. A new class of atomistic simulation techniques combining machine learning (ML) with simulation methods based on quantum mechanical (QM) calculations has emerged in the last decades. These methods can dramatically increase the computational efficiency of QM-based simulations and enable to reach the large system sizes and long timescales required to access properties with relevance for drug industry. Machine learning tools provide early stage filtering to identify promising drug molecules for further screening by computationally more intensive methods. The core quantity of atomistic simulation is the Potential energy landscape (PES), a high dimension function which is the basic ingredient for Monte Carlo (MC) simulations. Simulation methods are more or less computationally efficient depending on the degree of physical approximation. Force fields as AMBER [32], CHARMM [33], GROMOS [34], and OPLS [35] are computationally very efficient since they employ simple pairwise interaction terms and fixed atomic charges for MM based methods in drug discovery pipeline [36]. QM simulations are fully reactive and can describe the complex bonding patterns, polarization effects and charge transfer processes that govern the behavior of biological systems [37].Various machine learning tools as artificial neural networks (ANN), support vector machines (SVM) and genetic programming have been explored to predict inhibitors, blockers, agonists, antagonists, activators and substrates of proteins related to specific therapeutic targets. These methods use screening compound libraries of diverse chemical structures, "noisy" and high-dimensional data to complement QSAR methods, and in cases of unavailable receptor 3D structure to complement structure-based methods. Several open access chemical spaces as, PubChem, ChemBank, DrugBank, and ChemDB are used in virtual screening of Drug molecules. DeepVS is used for docking of 40 receptors and 2950 ligands, showed exceptional performance when 95000 decoys were tested against these receptors [38]. In another study, multiobjective automated replacement algorithm is used to optimize the potency profile of a cyclin-dependent kinase-2 inhibitor by assessing its shape similarity, biochemical activity, and physicochemical properties [39]. GLORY, an innovative tool was used to predict the metabolism of molecules, identifying chemical structures of metabolites formed by cytochrome P450 enzyme family (CYPs) [40]. In another approach, drug combination synergy was used to exploit the largest available dataset reporting synergism of anticancer drugs (NCI-ALMANAC, with over 290,000 synergy determinations) [41].

As the vast chemical space comprising >1060 molecules, fosters the development of a large number of drug molecules [42, 43], sometimes limits the drug development process, making it a time-consuming and highly expensive. So AI is used as it can recognize hit and lead compounds and provide a quicker validation of the drug target and optimization of the drug structure design [42–44]. See **Figure 4**. AI can

**Figure 4.** *Method domains of artificial intelligence (AI). Adopted from Ref. [44].*

aid rational drug design [45]; assist in decision making; determine the right therapy for a patient, including personalized medicines; and manage the clinical data generated and use it for future drug development [46]. AI has done major contributions to the further incorporation of the developed drug in its correct dosage form as well as its optimization, in addition to aiding quick decision-making, leading to faster manufacturing of better-quality products. Robotic synthesis could eventually provide a fully automated drug discovery pipeline driven by AI [47, 48]. AI based approaches can also contribute to the safety and efficacy of the product in clinical trials as well as ensuring proper positioning and costing in the market through comprehensive market analysis and prediction.
