Structure based virtual screening to identify selective phosphodiesterase 4B inhibitors

CADD, AI and ML in drug discovery: A comprehensive review

Computer-aided drug design (CADD) is an emerging field that has drawn a lot of interest because of its potential to expedite and lower the cost of the drug development process. Drug discovery research is expensive and time-consuming, and it frequently took 10-15 years for a drug to be commercially available. CADD has significantly impacted this area of research. Further, the combination of CADD with Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) technologies to handle enormous amounts of biological data has reduced the time and cost associated with the drug development process. This review will discuss how CADD, AI, ML, and DL approaches help identify drug candidates and various other steps of the drug discovery process. It will also provide a detailed overview of the different in silico tools used and how these approaches interact.

Drug Discovery

Modern chemistry foundations were made in between the 18th and 19th centuries and have been extended in 20th century. R&D towards synthetic chemistry was introduced during the 1960s. Development of new molecular drugs from the herbal plants to synthetic chemistry is the fundamental scientific improvement. About 10-14 years are needed to develop a new molecule with an average cost of more than $800 million. Pharmaceutical industries spend the highest percentage of revenues, but the achievement of desired molecular entities into the market is not increasing proportionately. As a result, an approximate of 0.01% of new molecular entities are approved by the FDA. The highest failure rate is due to inadequate efficacy exhibited in Phase II of the drug discovery and development stage. Innovative technologies such as combinatorial chemistry, DNA sequencing, high-throughput screening, bioinformatics, computational drug design, and computer modeling are now utilized in the drug discovery. These technologies can accelerate the success rates in introducing new molecular entities into the market.

Interaction of Arylidenechromanone/Flavanone Derivatives with Biological Macromolecules Studied as Human Serum Albumin Binding, Cytotoxic Effect, Biocompatibility Towards Red Blood Cells

The aim of this study was to determine the cytotoxic effect of 3-arylidenechromanone (1) and 3-arylideneflavanone (2) on HL-60 and NALM-6 cell lines (two human leukemia cell lines) and a WM-115 melanoma cell line. Both compounds exhibited high cytotoxic activity with higher cytotoxicity exerted by compound 2, for which IC₅₀ values below 10 µM were found for each cell line. For compound 1, the IC₅₀ values were higher than 10 µM for HL-60 and WM-115 cell lines, but IC₅₀ < 10 µM was found for the NALM-6 cell line. Both compounds, at the concentrations close to IC₅₀ (concentration range: 5–24 µM/L for compound 1 and 6–10 µM/L for compound 2), are not toxic towards red blood cells. The synthesized compounds were characterized using spectroscopic methods ¹H- and ¹³C-NMR, IR, MS, elemental analysis, and X-ray diffraction. The lipophilicity of both synthesized compounds was determined using an RP-TLC method and the logP values found were compared with the theoretical ones taken from the Molinspiration Cheminformatics (miLogP) software package. The mode of binding of both compounds to human serum albumin was assessed using molecular docking methods.

3D QSAR Pharmacophore Based Virtual Screening for Identification of Potential Inhibitors for CDC25B

Owing to its fundamental roles in cell cycle phases, the cell division cycle 25B (CDC25B) was broadly considered as potent clinical drug target for cancers. In this study, 3D QSAR pharmacophore models for CDC25B inhibitors were developed by the module of Hypogen. Three methods (cost analysis, test set prediction, and Fisher's test) were applied to validate that the models could be used to predict the biological activities of compounds. Subsequently, 26 compounds satisfied Lipinski's rule of five were obtained by the virtual screening of the Hypo-1-CDC25B against ZINC databases. It was then discovered that 9 identified molecules had better binding affinity than a known CDC25B inhibitors-compound 1 using docking studies. The molecular dynamics simulations showed that the compound had favorable conformations for binding to the CDC25B. Thus, our findings here would be helpful to discover potent lead compounds for the treatment of cancers.

Virtual Screening Approach of Bacterial Peptide Deformylase Inhibitors Results in New Antibiotics

The increasing resistance of bacteria to antibacterial therapy poses an enormous health problem, it renders the development of new antibacterial agents with novel mechanism of action an urgent need. Peptide deformylase, a metalloenzyme which catalytically removes N-formyl group from N-terminal methionine of newly synthesized polypeptides, is an important target in antibacterial drug discovery. In this study, we report the structure-based virtual screening of ZINC database in order to discover potential hits as bacterial peptide deformylase enzyme inhibitors with more affinity as compared to GSK1322322, previously known inhibitor. After virtual screening, fifteen compounds of the top hits predicted were purchased and evaluated in vitro for their antibacterial activities against one Gram positive (Staphylococcus aureus) and three Gram negative (Escherichia coli, Pseudomonas aeruginosa and Klebsiella. pneumoniae) bacteria in different concentrations by disc diffusion method. Out of these, three compounds, ZINC00039650, ZINC03872971 and ZINC00126407, exhibited significant zone of inhibition. The results obtained were confirmed using the dilution method. Thus, these proposed compounds may aid the development of more efficient antibacterial agents.

Leucine-684: A conserved residue of an AMP-acetyl CoA synthetase (AceCS) from Leishmania donovani is involved in substrate recognition, catalysis and acetylation

AMP-acetyl CoA synthetase (AMP-AceCS) is a key enzyme which catalyzes the activation of acetate to acetyl CoA, an important intermediate at the cross roads of various anabolic and catabolic pathways. Multiple sequence alignment of Leishmania donovani AceCS with other organisms revealed the presence of a highly conserved leucine residue at 684 position which is known to be crucial for acetylation by protein acetyl transferases in other organisms. In an attempt to understand the role of leucine residue at 684 position in L. donovani acetyl CoA synthetase (LdAceCS), it was mutated to proline (P) by site directed mutagenesis. Kinetic analysis of the L684P-LdAceCS mutant revealed approximately two fold increased binding affinity with acetate, whereas fivefold decreased affinity was observed with ATP. There was insignificant change in secondary structure as revealed by CD however, two fold decreased fluorescence intensity was observed at an emission maxima of 340 nm. Interestingly, L684P mutation abolished the acetylation of the mutant enzyme indicating the importance of L684 in acetylation of the enzyme. Changes in biochemical parameters of the mutant protein were validated by homology modeling of the wild type and mutant LdAceCS enzyme using Salmonella enterica AceCS crystal structure as template. Our data provides evidence for the role of leucine 684 residue in substrate recognition, catalysis and acetylation of the AceCS enzyme.

Pharmacophore generation and atom based 3D-QSAR of quinoline derivatives as selective phosphodiesterase 4B inhibitors

Reported PDE4B inhibitors were used to design QSAR based pharmacophore model. Using developed pharmacophore model, virtual screening was performed followed by cross-docking to identify novel PDE4B specific inhibitors.

Molecular docking and structure-based drug design strategies

Pharmaceutical research has successfully incorporated a wealth of molecular modeling methods, within a variety of drug discovery programs, to study complex biological and chemical systems. The integration of computational and experimental strategies has been of great value in the identification and development of novel promising compounds. Broadly used in modern drug design, molecular docking methods explore the ligand conformations adopted within the binding sites of macromolecular targets. This approach also estimates the ligand-receptor binding free energy by evaluating critical phenomena involved in the intermolecular recognition process. Today, as a variety of docking algorithms are available, an understanding of the advantages and limitations of each method is of fundamental importance in the development of effective strategies and the generation of relevant results. The purpose of this review is to examine current molecular docking strategies used in drug discovery and medicinal chemistry, exploring the advances in the field and the role played by the integration of structure- and ligand-based methods.