The process for drug discovery and development is challenging, time consuming and expensive. Computer-aided drug discovery (CADD) tools can act as a virtual shortcut, assisting in the expedition of this long process and potentially reducing the cost of research and development. Today CADD has become an effective and indispensable tool in therapeutic development. The human genome project has made available a substantial amount of sequence data that can be used in various drug discovery projects. Additionally, increasing knowledge of biological structures, as well as increasing computer power have made it possible to use computational methods effectively in various phases of the drug discovery and development pipeline. The importance of in silico tools is greater than ever before and has advanced pharmaceutical research. Here we present an overview of computational methods used in different facets of drug discovery and highlight some of the recent successes. In this review, both structure-based and ligand-based drug discovery methods are discussed. Advances in virtual high-throughput screening, protein structure prediction methods, protein-ligand docking, pharmacophore modeling and QSAR techniques are reviewed.

https://www.beilstein-journals.org/bjoc/content/pdf/1860-5397-12-267.pdf

Computational methods in drug discovery.

Dengue fever, which is a disease caused by the dengue virus (DENV), is a major unsolved issue in many tropical and sub-tropical regions of the world. The absence of treatment that effectively prevent further viral propagation inside the human’s body resulted in a high number of deaths globally each year. Thus, novel anti-dengue therapies are required for effective treatment. Hu-man hexokinase II (HKII), which is the first enzyme in the glycolytic pathway, is an important drug target due to its significant impact on viral replication and survival in host cells. In this study, 23.1 million compounds were computationally-screened against HKII using the Ultrafast Shape Recognition with a CREDO Atom Types (USRCAT) algorithm. In total, 300 compounds with the highest similarity scores relative to three reference molecules, known as Al-pha-D-glucose (GLC), Beta-D-glucose-6-phosphate (BG6), and 2-deoxyglucose (2DG), were aligned. Of these 300 compounds, 165 were chosen for further structure-based screening, based on their similarity scores, ADME analysis, the Lipinski’s Rule of Five, and virtual toxicity test results. The selected analogues were subsequently docked against each domain of the HKII structure (PDB ID: 2NZT) using AutoDock Vina programme. The three top-ranked compounds for each query were then selected from the docking results based on their binding energy, the number of hydrogen bonds formed, and the specific catalytic residues. The best docking results for each analogue were observed for the C-terminus of Chain B. The top-ranked analogues of GLC, compound 10, compound 26, and compound 58, showed predicted binding energies of −7.2, −7.0, and −6.10 kcal/mol and 7, 5, and 2 hydrogen bonds, respectively. The analogues of BG6, compound 30, compound 36, and compound 38, showed predicted binding energies of −7.8, −7.4, and −7.0 kcal/mol and 11, 9, and 5 hydrogen bonds, while the top three analogues of 2DG, known as compound 1, compound 4, and compound 31, showed predicted binding energies of −6.8, −6.3, and −6.3 kcal/mol and 4, 3, and 1 hydrogen bonds, sequentially. The highest-ranked compounds in the docking analysis were then selected for molecular dynamics simulation, where compound 10, compound 30, and compound 1, which are the analogues of GLC, BG6, and 2DG, have shown strong protein-ligand stability with an RMSD value of ±5.0 A° with a 5 H bond, ±4.0 A° with an 8 H bond, and ±0.5 A° with a 2 H bond, respectively, compared to the reference molecules throughout the 20 ns simulation time. Therefore, by using the computational studies, we pro-posed novel compounds, which may act as potential drugs against DENV by inhibiting HKII’s activity.

https://www.mdpi.com/2673-6284/10/1/1/pdf

Virtual screening for potential inhibitors of human hexokinase II for the development of anti-dengue therapeutics

Applied Discriminant Analysis

In December 2019, COVID-19 epidemic was described in Wuhan, China, and the infection has spread widely affecting hundreds of thousands. Herein, an effort was made to identify commercially available drugs in order to repurpose them against coronavirus by the means of structure-based virtual screening. In addition, ZINC15 library was used to identify novel leads against main proteases. Human TMPRSS2 3D structure was first generated using homology modeling approach. Our molecular docking study showed four potential inhibitors against Mpro enzyme, two available drugs (Talampicillin and Lurasidone) and two novel drug-like compounds (ZINC000000702323 and ZINC000012481889). Moreover, four promising inhibitors were identified against TMPRSS2; Rubitecan and Loprazolam drugs, and compounds ZINC000015988935 and ZINC000103558522. ADMET profile showed that the hits from our study are safe and drug-like compounds. Furthermore, molecular dynamic (MD) simulation and binding free energy calculation using the MM-PBSA method was performed to calculate the interaction energy of the top-ranked drugs.Communicated by Ramaswamy H. Sarma.

Drug repurposing for coronavirus (COVID-19): in silico screening of known drugs against coronavirus 3CL hydrolase and protease enzymes.

Structure-activity relationship (SAR) study and design strategies of nitrogen-containing heterocyclic moieties for their anticancer activities.

Major goal of structural biology involve formation of protein-ligand complexes; in which the protein molecules act energetically in the course of binding. Therefore, perceptive of protein-ligand interaction will be very important for structure based drug design. Lack of knowledge of 3D structures has hindered efforts to understand the binding specificities of ligands with protein. With increasing in modeling software and the growing number of known protein structures, homology modeling is rapidly becoming the method of choice for obtaining 3D coordinates of proteins. Homology modeling is a representation of the similarity of environmental residues at topologically corresponding positions in the reference proteins. In the absence of experimental data, model building on the basis of a known 3D structure of a homologous protein is at present the only reliable method to obtain the structural information. Knowledge of the 3D structures of proteins provides invaluable insights into the molecular basis of their functions. The recent advances in homology modeling, particularly in detecting and aligning sequences with template structures, distant homologues, modeling of loops and side chains as well as detecting errors in a model contributed to consistent prediction of protein structure, which was not possible even several years ago. This review focused on the features and a role of homology modeling in predicting protein structure and described current developments in this field with victorious applications at the different stages of the drug design and discovery.

/pdf/homology-modeling-a-fast-tool-for-drug-discovery-current-3xs8gytjin.pdf

Homology modeling a fast tool for drug discovery: Current perspectives

Many active secretions produced by animals have been employed in the development of new drugs to treat diseases such as hypertension and cancer. Snake venom toxins contributed significantly to the treatment of many medical conditions. There are many published studies describing and elucidating the anti-cancer potential of snake venom. Cancer therapy is one of the main areas for the use of protein peptides and enzymes originating from animals of different species. Some of these proteins or peptides and enzymes from snake venom when isolated and evaluated may bind specifically to cancer cell membranes, affecting the migration and proliferation of these cells. Some of substances found in the snake venom present a great potential as anti-tumor agent. In this review, we presented the main results of recent years of research involving the active compounds of snake venom that have anticancer activity.

Therapeutic potential of snake venom in cancer therapy: current perspectives

Dihydroorotate dehydrogenase (DHODH) is a flavin-dependent mitochondrial enzyme that catalyzes fourth reaction of pyrimidine de-novo synthesis. Pyrimidine bases are essential for cellular metabolism and cell growth, and are considered as important precursors used in DNA (thymine and cytosine), RNA (uracil and cytosine), glycoproteins and phospholipids biosynthesis. The significance of pyrimidines biosynthesis in DNA and RNA makes them ideal targets for pharmacological intervention. Inhibitors of DHODH have proven efficacy for the treatment of malaria, autoimmune diseases, cancer, rheumatoid arthritis and psoriasis. Plasmodium falciparum dihydroorotate dehydrogenase (PfDHODH) represents an important target for the treatment of malaria. Many of the clinically relevant anti-tumor and immunosuppressive drugs target human dihydroorotate dehydrogenase (hDHODH), and the two most promising drugs of such kinds are brequinar (antitumor and immunosuppressive) and leflunomide (immunosuppressive). X-ray crystal structures of DHODH in complex with inhibitors reveal common binding region shared by each inhibitor. A number of compounds are identified by high-throughput screening (HTS) of chemical libraries and structure-based computational approaches as selective DHODH inhibitors. Based upon the understanding of molecular interaction of DHODH inhibitors with binding site, some of the common structural features are identified like ability of compounds to interact with ubiquinone (CoQ) binding site and substituents linked to a variety of heterocyclic and heteroaromatic rings responsible for H-bonding with binding site. These findings provide new approaches to design DHODH inhibitors and highlights DHODH as a target for chemotherapeutics. This review is mainly focused on the recent developments in the medicinal chemistry and therapeutic potential of DHODH inhibitors as a target for drug discovery.

Recent developments in the medicinal chemistry and therapeutic potential of dihydroorotate dehydrogenase (DHODH) inhibitors.

Computational screening of databases has become increasingly popular in the pharmaceutical research. Virtual screening uses computer based methods to discover new ligands on the basis of biological structures. Virtual screening is divided into structural based screening (docking) and screening using active compounds as templates (ligand based virtual screening). Ligand based screening techniques mainly focus on comparing molecular similarity analyses of compounds with known and unknown moiety, regardless of the methods of the used algorithm. Docking is a computational tool of structure based drug design to predict protein ligand interaction geometries and binding affinities. In this review we provide an overview of the already used ligand based virtual screening and the docking with various databases, filters, scores and applications in the recent research in the pharmaceutical field.

https://www.mdpi.com/2218-0532/76/3/333/pdf

Virtual Screening: A Fast Tool for Drug Design

Synthesis, anti-inflammatory, analgesic, 5-lipoxygenase (5-LOX) inhibition activities, and molecular docking study of 7-substituted coumarin derivatives.

Vivek K. Vyas

Papers

Homology modeling a fast tool for drug discovery: Current perspectives

Therapeutic potential of snake venom in cancer therapy: current perspectives

Recent developments in the medicinal chemistry and therapeutic potential of dihydroorotate dehydrogenase (DHODH) inhibitors.

Virtual Screening: A Fast Tool for Drug Design

Synthesis, anti-inflammatory, analgesic, 5-lipoxygenase (5-LOX) inhibition activities, and molecular docking study of 7-substituted coumarin derivatives.