Current Topics in Medicinal Chemistry - Volume 18, Issue 32, 2018

Computer-aided drug discovery (CADD) tools have provided an effective way in the drug discovery pipeline for expediting of this long process and economizing the cost of research and development. Due to the dramatic increase in the availability of human proteins as drug targets and small molecule information due to the advances in bioinformatics, cheminformatics, genomics, proteomics, and structural information, the applicability of in silico drug discovery has been extended. Computational approaches have been used at almost all stages in the drug discovery pipeline including target identification and validation, lead discovery and optimization, and pharmacokinetic and toxicity profiles prediction. As each area covers a variety of computational methods, it is unmanageable to assess comprehensively all areas of CADD applications or every aspect of an area in one review article. However, in this article, we tried to present an overview of computational methods used in almost all the areas concerned with drug design and highlight some of the recent successes.

We review various mathematical and computational techniques for drug discovery exemplifying some recent works pertinent to group theory of nested structures of relevance to phylogeny, topological, computational and combinatorial methods for drug discovery for multiple viral infections. We have reviewed techniques from topology, combinatorics, graph theory and knot theory that facilitate topological and mathematical characterizations of protein-protein interactions, molecular-target interactions, proteomics, genomics and statistical data reduction procedures for a large set of starting chemicals in drug discovery. We have provided an overview of group theoretical techniques pertinent to phylogeny, protein dynamics especially in intrinsically disordered proteins, DNA base permutations and related algorithms. We consider computational techniques derived from high level quantum chemical computations such as QM/MM ONIOM methods, quantum chemical optimization of geometries complexes, and molecular dynamics methods for providing insights into protein-drug interactions. We have considered complexes pertinent to Hepatitis Virus C non-structural protein 5B polymerase receptor binding of C5-Arylidebne rhodanines, complexes of synthetic potential vaccine molecules with dengue virus (DENV) and HIV-1 virus as examples of various simulation studies that exemplify the utility of computational tools. It is demonstrated that these combinatorial and computational techniques in conjunction with experiments can provide promising new insights into drug discovery. These techniques also demonstrate the need to consider a new multiple site or allosteric binding approach to drug discovery, as these studies reveal the existence of multiple binding sites.

The failure of the Integrase Strand Transfer Inhibitors (INSTIs) due to the mutations occurring at the catalytic site of HIV integrase (IN) has led to the design of allosteric integrase inhibitors (ALLINIs). Lens epithelium derived growth factor (LEDGF/p75) is the host cellular cofactor which helps chaining IN to the chromatin. The protein-protein interactions (PPIs) were observed at the allosteric site (LEDGF/p75 binding domain) between LEDGF/p75 of the host cell and IN of virus. In recent years, many small molecules such as CX04328, CHIBA-3053 and CHI-104 have been reported as LEDGF/p75-IN interaction inhibitors (LEDGINs). LEDGINs have emerged as promising therapeutics to halt the PPIs by binding at the interface of both the proteins. In the present work, we correlated the docking scores for the reported LEDGINs containing quinoline scaffold with the in vitro biological data. The hierarchal clustering method was used to divide the compounds into test and training set. The robustness of the generated model was validated by q2 and r2 for the predicted set of compounds. The generated model between the docking score and biological data was assessed to predict the activity of the hits (quinoline scaffold) obtained from virtual screening of LEDGINs providing their structureactivity relationships to aim for the generation of potent agents.

Background: 5α-Reductase (5AR), an NADPH dependent enzyme, is expressed in most of the prostate epithelial cells. By converting testosterone (T) into more potent androgen dihydrotestosterone (DHT), it plays an important role in men physiology and represents an efficient therapeutic target for androgen-dependent diseases. Over the last few years, significant efforts have been made in order to develop 5AR inhibitors (5ARI) to treat Benign Prostatic Hyperplasia because of excessive production of DHT. Methods: In the present study, 2D and 3D QSAR pharmacophore models have been generated for 5ARI based on known IC50 values with extensive validations. The four featured 2D pharmacophore based PLS model correlated the topological interactions (SsOHE-index); semi empirical (Quadrupole2) and physicochemical descriptors (Mol. Wt, Bromines Count, Chlorines Count) with 5AR inhibitory activity, and has the highest correlation coefficient (r2 = 0.98, q2 =0.84; F = 57.87, pred r2 = 0.88). Internal and external validation was carried out using test and proposed set of compounds. The contribution plot of electrostatic field effects and steric interactions generated by 3D-QSAR showed interesting results in terms of internal and external predictability. The well-validated 2D PLS, and 3D kNN models were used to search novel 5AR inhibitors with different chemical scaffold. The compounds were further sorted by applying ADMET properties and in vitro cytotoxicity studies against prostate cancer cell lines PC-3. Molecular docking studies have also been employed to investigate the binding interactions and to study the stability of docked conformation in detail. Results: Several important hydrophobic and hydrogen bond interactions with 5AR lead to the identification of active binding sites of 4AT0 protein in the docked complex, which include the gatekeeper residues ALA 63A (Chain A: ALA63), THR 60 A (Chain A: THR60), and ARG 456 A (Chain A: ARG456), at the hinge region. Conclusion: Overall, this study suggests that the proposed compounds have the potential as effective inhibitors for 5AR.