The Development of Pharmacophore Modeling: Generation and Recent Applications in Drug Discovery

Drug discovery is a step-by-step process very important in biopharmaceutical field. We are interested in identifying new investigational drug-likes as potential inhibitors of determinate biological-therapeutic targets, trying to decrease the side effects and to safeguard the human health. However, it is a long and very expensive process. Therefore, we are using a new computational strategy, based on Pharmacophore modeling, to select bioactive substances (natural or synthetic), through the integration of bioinformatics online tools and local resource and platforms, in order to include into the strategy also knowledge from high-throughput studies, for new potential lead compounds generation-optimization, trying to accelerate the early phase of the drug development process. The protocol of this new computational strategy is characterized by a multi-step design focused on: 1) screening in RCSB-PDB for a crystal structure of a specific biological target, suitable for the following steps; 2) pharmacophore modeling and virtual computational screening, by using public domain databases of bioactive compounds, as the ZINC12 database [5], in order to find a promising molecule that could become a new potential medicine. 3) molecular and biological evaluation, to check the compounds selected by virtual screening, for their biological properties through public databases, as PubChem Compound, SciFinder, and Chemicalize to trace their origin and underline their most important physical-chemical features, PathPred (an enzyme-catalyzed metabolic pathway predictor server) to highlight and identify their biosynthetic-metabolic pathways and investigating the biotransformation of best candidates, analyzing their metabolites and their potential biological activity. Moreover, ADMET/toxicity predictor server applying the Lipinski-Veber filter are used to calculate the bioavailability the ADMET/toxicity properties. After this check, only molecules with good bioavailability, good predicted activity and good ADMET properties are considered as hits compounds or drug-likes to direct the design of next experimental assays [6]. Finally, the lead compounds selected are analyzed through molecular dynamics simulations. 4) simulations of molecular dynamics on the best lead compounds, to investigate atomic details of protein-compound molecular interactions in different conditions (different organic solutions, organisms and systems). REFERENCES [1] Dubey A, Facchiano A, Ramteke PW, Marabotti A. “In silico approach to find chymase inhibitors among biogenic compounds.” Future Med Chem. 2016; 8(8):841-51 [2] Dubey A, Marabotti A, Ramteke PW, Facchiano A. "Interaction of human chymase with ginkgolides, terpene trilactones of Ginkgo biloba investigated by molecular docking simulations.” Biochem Biophys Res Commun. 2016; 473(2):449-54. [3] Katara P. “Role of bioinformatics and pharmacogenomics in drug discovery and development process”. Netw Model Anal Health Inform Bioinforma 2013; 2: 225-230. [4] Sunseri J. and Koes D. R. “Pharmit: Interactive Exploration of Chemical Space”.Nucl. Acids Res. 2016; 44(W1): W442-448. [5] Irwin J.J. and Shoichet B.K. “ZINC- A free database of Commercially Available Compounds for Virtual Screening”. J.Chem.Inf.Model. 2005; 45: 177-182. [6] Kaserer T., Beck K. R., Akram M., Odermatt A., Schuster D. “Pharmacophore Models and Pharmacophore-Based Virtual Screening: Concepts and Application Exemplified on Hydroxysteroid Dehydrogenases”.Molecules 2015; 20: 22799–22832.

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Drug discovery by integration of pharmacophore modeling, virtual screening and biological evaluation by means of bioinformatics resources

10.7287/peerj.preprints.2639 ◽

2016 ◽

Author(s):

Serena Dotolo ◽

Angelo Facchiano

Keyword(s):

Molecular Dynamics ◽

Drug Discovery ◽

Virtual Screening ◽

Development Process ◽

Chemical Space ◽

Pharmacophore Modeling ◽

Biological Evaluation ◽

Lead Compounds ◽

Computational Screening ◽

Computational Strategy

Drug discovery is a step-by-step process very important in biopharmaceutical field. We are interested in identifying new investigational drug-likes as potential inhibitors of determinate biological-therapeutic targets, trying to decrease the side effects and to safeguard the human health. However, it is a long and very expensive process. Therefore, we are using a new computational strategy, based on Pharmacophore modeling, to select bioactive substances (natural or synthetic), through the integration of bioinformatics online tools and local resource and platforms, in order to include into the strategy also knowledge from high-throughput studies, for new potential lead compounds generation-optimization, trying to accelerate the early phase of the drug development process. The protocol of this new computational strategy is characterized by a multi-step design focused on: 1) screening in RCSB-PDB for a crystal structure of a specific biological target, suitable for the following steps; 2) pharmacophore modeling and virtual computational screening, by using public domain databases of bioactive compounds, as the ZINC12 database [5], in order to find a promising molecule that could become a new potential medicine. 3) molecular and biological evaluation, to check the compounds selected by virtual screening, for their biological properties through public databases, as PubChem Compound, SciFinder, and Chemicalize to trace their origin and underline their most important physical-chemical features, PathPred (an enzyme-catalyzed metabolic pathway predictor server) to highlight and identify their biosynthetic-metabolic pathways and investigating the biotransformation of best candidates, analyzing their metabolites and their potential biological activity. Moreover, ADMET/toxicity predictor server applying the Lipinski-Veber filter are used to calculate the bioavailability the ADMET/toxicity properties. After this check, only molecules with good bioavailability, good predicted activity and good ADMET properties are considered as hits compounds or drug-likes to direct the design of next experimental assays [6]. Finally, the lead compounds selected are analyzed through molecular dynamics simulations. 4) simulations of molecular dynamics on the best lead compounds, to investigate atomic details of protein-compound molecular interactions in different conditions (different organic solutions, organisms and systems). REFERENCES [1] Dubey A, Facchiano A, Ramteke PW, Marabotti A. “In silico approach to find chymase inhibitors among biogenic compounds.” Future Med Chem. 2016; 8(8):841-51 [2] Dubey A, Marabotti A, Ramteke PW, Facchiano A. "Interaction of human chymase with ginkgolides, terpene trilactones of Ginkgo biloba investigated by molecular docking simulations.” Biochem Biophys Res Commun. 2016; 473(2):449-54. [3] Katara P. “Role of bioinformatics and pharmacogenomics in drug discovery and development process”. Netw Model Anal Health Inform Bioinforma 2013; 2: 225-230. [4] Sunseri J. and Koes D. R. “Pharmit: Interactive Exploration of Chemical Space”.Nucl. Acids Res. 2016; 44(W1): W442-448. [5] Irwin J.J. and Shoichet B.K. “ZINC- A free database of Commercially Available Compounds for Virtual Screening”. J.Chem.Inf.Model. 2005; 45: 177-182. [6] Kaserer T., Beck K. R., Akram M., Odermatt A., Schuster D. “Pharmacophore Models and Pharmacophore-Based Virtual Screening: Concepts and Application Exemplified on Hydroxysteroid Dehydrogenases”.Molecules 2015; 20: 22799–22832.

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Virtual Screening and Its Applications in Drug Discovery Process

Advances in Medical Technologies and Clinical Practice - Computer Applications in Drug Discovery and Development ◽

10.4018/978-1-5225-7326-5.ch005 ◽

2019 ◽

pp. 101-126

Author(s):

Gurusamy Mariappan ◽

Anju Kumari

Keyword(s):

Drug Discovery ◽

Virtual Screening ◽

Drug Design ◽

Screening Tool ◽

Log P ◽

Discovery Process ◽

Drug Discovery Process ◽

Financial Loss ◽

Structure Based Drug Design ◽

Modern Drug

Virtual screening plays an important role in the modern drug discovery process. The pharma companies invest huge amounts of money and time in drug discovery and screening. However, at the final stage of clinical trials, several molecules fail, which results in a large financial loss. To overcome this, a virtual screening tool was developed with super predictive power. The virtual screening tool is not only restricted tool small molecules but also to macromolecules such as protein, enzyme, receptors, etc. This gives an insight into structure-based and Ligand-based drug design. VS gives reliable information to direct the process of drug discovery (e.g., when the 3D image of the receptor is known, structure-based drug design is recommended). The pharmacophore-based model is advisable when the information about the receptor or any macromolecule is unknown. In this ADME, parameters such as Log P, bioavailability, and QSAR can be used as filters. This chapter shows both models with various representative examples that facilitate the scientist to use computational screening tools in modern drug discovery processes.

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Potentiators exert distinct effects on human, murine, and Xenopus CFTR

AJP Lung Cellular and Molecular Physiology ◽

10.1152/ajplung.00056.2016 ◽

2016 ◽

Vol 311 (2) ◽

pp. L192-L207 ◽

Cited By ~ 8

Author(s):

Guiying Cui ◽

Netaly Khazanov ◽

Brandon B. Stauffer ◽

Daniel T. Infield ◽

Barry R. Imhoff ◽

...

Keyword(s):

Cystic Fibrosis ◽

Binding Sites ◽

Pharmacophore Modeling ◽

New Drugs ◽

Molecular Pharmacology ◽

Specific Behavior ◽

Lead Compounds ◽

Cftr Mutations ◽

Different Origins ◽

Insight Into

VX-770 (Ivacaftor) has been approved for clinical usage in cystic fibrosis patients with several CFTR mutations. Yet the binding site(s) on CFTR for this compound and other small molecule potentiators are unknown. We hypothesize that insight into this question could be gained by comparing the effect of potentiators on CFTR channels from different origins, e.g., human, mouse, and Xenopus (frog). In the present study, we combined this comparative molecular pharmacology approach with that of computer-aided drug discovery to identify and characterize new potentiators of CFTR and to explore possible mechanism of action. Our results demonstrate that 1) VX-770, NPPB, GlyH-101, P1, P2, and P3 all exhibited ortholog-specific behavior in that they potentiated hCFTR, mCFTR, and xCFTR with different efficacies; 2) P1, P2, and P3 potentiated hCFTR in excised macropatches in a manner dependent on the degree of PKA-mediated stimulation; 3) P1 and P2 did not have additive effects, suggesting that these compounds might share binding sites. Also 4) using a pharmacophore modeling approach, we identified three new potentiators (IOWH-032, OSSK-2, and OSSK-3) that have structures similar to GlyH-101 and that also exhibit ortholog-specific potentiation of CFTR. These could potentially serve as lead compounds for development of new drugs for the treatment of cystic fibrosis. The ortholog-specific behavior of these compounds suggest that a comparative pharmacology approach, using cross-ortholog chimeras, may be useful for identification of binding sites on human CFTR.

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ABBV-744 as a potential inhibitor of SARS-CoV-2 main protease enzyme against COVID-19

Scientific Reports ◽

10.1038/s41598-020-79918-3 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Zeynab Fakhar ◽

Shama Khan ◽

Suliman Y. AlOmar ◽

Afrah Alkhuriji ◽

Aijaz Ahmad

Keyword(s):

Virtual Screening ◽

Drug Repurposing ◽

Pharmacophore Modeling ◽

Binding Energies ◽

Active Inhibitor ◽

Lead Compounds ◽

Protease Enzyme ◽

Main Protease ◽

Dynamics Simulations ◽

Potential Inhibitors

AbstractA new pathogen severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has spread worldwide and become pandemic with thousands new deaths and infected cases globally. To address coronavirus disease (COVID-19), currently no effective drug or vaccine is available. This necessity motivated us to explore potential lead compounds by considering drug repurposing approach targeting main protease (Mpro) enzyme of SARS-CoV-2. This enzyme considered to be an attractive drug target as it contributes significantly in mediating viral replication and transcription. Herein, comprehensive computational investigations were performed to identify potential inhibitors of SARS-CoV-2 Mpro enzyme. The structure-based pharmacophore modeling was developed based on the co-crystallized structure of the enzyme with its biological active inhibitor. The generated hypotheses were applied for virtual screening based PhaseScore. Docking based virtual screening workflow was used to generate hit compounds using HTVS, SP and XP based Glide GScore. The pharmacological and physicochemical properties of the selected lead compounds were characterized using ADMET. Molecular dynamics simulations were performed to explore the binding affinities of the considered lead compounds. Binding energies revealed that compound ABBV-744 binds to the Mpro with strong affinity (ΔGbind −45.43 kcal/mol), and the complex is more stable in comparison with other protein–ligand complexes. Our study classified three best compounds which could be considered as promising inhibitors against main protease SARS-CoV-2 virus.

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A Review on Parallel Virtual Screening Softwares for High-Performance Computers

Pharmaceuticals ◽

10.3390/ph15010063 ◽

2022 ◽

Vol 15 (1) ◽

pp. 63

Author(s):

Natarajan Arul Murugan ◽

Artur Podobas ◽

Davide Gadioli ◽

Emanuele Vitali ◽

Gianluca Palermo ◽

...

Keyword(s):

Drug Discovery ◽

Virtual Screening ◽

High Throughput Screening ◽

High Performance ◽

Parallel Implementation ◽

Scoring Function ◽

Scoring Functions ◽

Screening Programs ◽

Lead Compounds ◽

High Performance Computers

Drug discovery is the most expensive, time-demanding, and challenging project in biopharmaceutical companies which aims at the identification and optimization of lead compounds from large-sized chemical libraries. The lead compounds should have high-affinity binding and specificity for a target associated with a disease, and, in addition, they should have favorable pharmacodynamic and pharmacokinetic properties (grouped as ADMET properties). Overall, drug discovery is a multivariable optimization and can be carried out in supercomputers using a reliable scoring function which is a measure of binding affinity or inhibition potential of the drug-like compound. The major problem is that the number of compounds in the chemical spaces is huge, making the computational drug discovery very demanding. However, it is cheaper and less time-consuming when compared to experimental high-throughput screening. As the problem is to find the most stable (global) minima for numerous protein–ligand complexes (on the order of 106 to 1012), the parallel implementation of in silico virtual screening can be exploited to ensure drug discovery in affordable time. In this review, we discuss such implementations of parallelization algorithms in virtual screening programs. The nature of different scoring functions and search algorithms are discussed, together with a performance analysis of several docking softwares ported on high-performance computing architectures.

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ABBV-744 and Onalespid as Potential Inhibitors of SARS-CoV-2 main Protease Enzyme: A Promising Therapeutics in COVID-19?

10.21203/rs.3.rs-37619/v1 ◽

2020 ◽

Author(s):

Zeynab Fakhar ◽

Shama Khan ◽

Aijaz Ahmad

Keyword(s):

Virtual Screening ◽

Drug Repurposing ◽

Pharmacophore Modeling ◽

Binding Studies ◽

Active Inhibitor ◽

Lead Compounds ◽

Protease Enzyme ◽

Main Protease ◽

Dynamics Simulations ◽

Potential Inhibitors

Abstract A new pathogen severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has spread worldwide and become pandemic with thousands new deaths and infected cases globally. To address coronavirus disease (COVID-19), currently no effective drug or vaccine is available. This necessity motivated us to explore potential lead compounds by considering drug repurposing approach targeting main protease (Mpro) enzyme of SARS-CoV-2. This enzyme considered to be an attractive drug target as it contributes significantly in mediating viral replication and transcription. Herein, comprehensive computational investigations were performed to identify potential inhibitors of SARS-CoV-2 Mpro enzyme. The structure-based pharmacophore modeling was developed based on the co-crystallized structure of the enzyme with its biological active inhibitor. The generated hypotheses were applied for virtual screening based PhaseScore. Docking based virtual screening work-flow was used to generate hit compounds using HTVS, SP and XP based Glide GScore. The pharmacological and physicochemical properties of the best hit compounds were characterized using ADMET. Molecular dynamics simulations were performed to explore the binding affinities of the considered compounds. Binding studies revealed that compound ABBV-744 binds to the Mpro with strong affinity (Gbind -45.43 kcal/mol), and the complex is more stable in comparison with other protein-ligand complexes. Our study classified three best compounds which could be considered as promising inhibitors against main protease SARS-CoV-2 virus.

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Forging New Scaffolds from Old: Combining Scaffold Hopping and Hierarchical Virtual Screening for Identifying Novel Bcl-2 Inhibitors

Current Topics in Medicinal Chemistry ◽

10.2174/1568026619666190618142432 ◽

2019 ◽

Vol 19 (13) ◽

pp. 1162-1172 ◽

Cited By ~ 3

Author(s):

Vishnupriya Kanakaveti ◽

Sakthivel Rathinasamy ◽

Suresh K. Rayala ◽

Michael Gromiha

Keyword(s):

Drug Discovery ◽

Virtual Screening ◽

Physicochemical Characterization ◽

B Cell Lymphoma ◽

Rational Approach ◽

Screening Methods ◽

Scaffold Hopping ◽

Discovery Process ◽

Drug Discovery Process ◽

Lead Compounds

Background: Though virtual screening methods have proven to be potent in various instances, the technique is practically incomplete to quench the need of drug discovery process. Thus, the quest for novel designing approaches and chemotypes for improved efficacy of lead compounds has been intensified and logistic approaches such as scaffold hopping and hierarchical virtual screening methods were evolved. Till now, in all the previous attempts these two approaches were applied separately. Objective: In the current work, we made a novel attempt in terms of blending scaffold hopping and hierarchical virtual screening. The prime objective is to assess the hybrid method for its efficacy in identifying active lead molecules for emerging PPI target Bcl-2 (B-cell Lymphoma 2). Method: We designed novel scaffolds from the reported cores and screened a set of 8270 compounds using both scaffold hopping and hierarchical virtual screening for Bcl-2 protein. Also, we enumerated the libraries using clustering, PAINS filtering, physicochemical characterization and SAR matching. Results: We generated a focused library of compounds towards Bcl-2 interface, screened the 8270 compounds and identified top hits for seven families upon fine filtering with PAINS algorithm, features, SAR mapping, synthetic accessibility and similarity search. Our approach retrieved a set of 50 lead compounds. Conclusions: Finding rational approach meeting the needs of drug discovery process for PPI targets is the need of the hour which can be fulfilled by an extended scaffold hopping approach resulting in focused PPI targeting by providing novel leads with better potency.

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