SEARCH FOR IN-SILICO APPLICATIONS IN DRUG DISCOVERY AND APPLICATIONS OF DIFFERENT DISCIPLINES IN IT: A SURVEY

2018 ◽  
Vol 9 (1) ◽  
pp. 552-561
Author(s):  
Parthajit Roy ◽  
Keyword(s):  
Drug Discovery ◽  
In Silico

scholarly journals Targeting the SARS-CoV-2 main protease using FDA-approved Isavuconazonium, a P2–P3 α-ketoamide derivative and Pentagastrin: An in-silico drug discovery approach

2020 ◽  
Vol 101 ◽  
pp. 107730 ◽  
Author(s):  
Ikechukwu Achilonu ◽  
Emmanuel Amarachi Iwuchukwu ◽  
Okechinyere Juliet Achilonu ◽  
Manuel Antonio Fernandes ◽  
Yasien Sayed
Keyword(s):  
Drug Discovery ◽  
In Silico ◽  
Main Protease

Predictive DMPK: In Silico ADME Predictions in Drug Discovery

2013 ◽  
Vol 10 (4) ◽  
pp. 1151-1152 ◽  
Author(s):  
Jane R. Kenny
Keyword(s):  
Drug Discovery ◽  
In Silico

Deployment of in silico and in vitro safety assays in early-stage drug discovery

2012 ◽  
Vol 4 (10) ◽  
pp. 1211-1213 ◽  
Author(s):  
Yvonne Will ◽  
Thomas Schroeter
Keyword(s):  
Drug Discovery ◽  
In Silico ◽  
Early Stage

Screening of phytochemicals from Curcuma longa for their inhibitory activity on SARS-CoV-2: An in-silico study

2021 ◽  
Vol 19 ◽  
Author(s):  
Preeya Negi ◽  
Lalita Das ◽  
Surya Prakash ◽  
Vaishali M. Patil
Keyword(s):  
Drug Discovery ◽  
In Silico ◽  
Curcuma Longa ◽  
Docking Studies ◽  
Primary Objective ◽  
In Silico Screening ◽  
Rational Drug ◽  
In Silico Study ◽  
Speed Up ◽  
Novel Coronavirus

Introduction: Natural products or phytochemicals have always been useful as effective therapeutics and for providing the lead for rational drug discovery approaches specific to anti-viral therapeutics. Methods: The ongoing pandemic caused by novel coronavirus has created a demand for effective therapeutics. Thus, to achieve the primary objective to search for effective anti-viral therapeutics, in silico screening of phytochemicals present in Curcuma longa extract (ex. Curcumin) has been planned. Results: The present work involves the evaluation of ADME properties and molecular docking studies. Conclusion: The application of rationalized drug discovery approaches to screen the diverse natural resources will speed up the anti-COVID drug discovery efforts and benefit the global community.

scholarly journals An integrated dataset for in silico drug discovery

2010 ◽  
Vol 7 (3) ◽  
Author(s):  
Simon J Cockell ◽  
Jochen Weile ◽  
Phillip Lord ◽  
Claire Wipat ◽  
Dmytro Andriychenko ◽  
...  
Keyword(s):  
Systems Biology ◽  
Drug Discovery ◽  
Drug Development ◽  
Data Integration ◽  
In Silico ◽  
Drug Repositioning ◽  
Integrated Systems ◽  
Integration Platform ◽  
Treatment Indications ◽  
New Treatment

SummaryDrug development is expensive and prone to failure. It is potentially much less risky and expensive to reuse a drug developed for one condition for treating a second disease, than it is to develop an entirely new compound. Systematic approaches to drug repositioning are needed to increase throughput and find candidates more reliably. Here we address this need with an integrated systems biology dataset, developed using the Ondex data integration platform, for the in silico discovery of new drug repositioning candidates. We demonstrate that the information in this dataset allows known repositioning examples to be discovered. We also propose a means of automating the search for new treatment indications of existing compounds.

scholarly journals Pengaruh Minimisasi Energi MMFF94 dengan MarvinSketch dan Open Babel PyRx pada Penambatan Molekular Turunan Oksindola Tersubstitusi

ALCHEMY ◽  
2020 ◽  
Vol 8 (2) ◽  
pp. 33-40
Author(s):  
Atika Umi Hanif ◽  
Prima Agusti Lukis ◽  
Arif Fadlan
Keyword(s):  
Molecular Docking ◽  
Drug Discovery ◽  
Force Field ◽  
Binding Affinity ◽  
Energy Minimization ◽  
In Silico ◽  
Initial State ◽  
Molecular Force ◽  
Molecular Force Field ◽  
Docking Energy

 In silico technique is widely used for drug discovery because it can predict the conformation of ligands in protein macromolecules and it can calculate the binding affinity. The energy minimization is carried out to make the ligand more stable near the initial state during molecular docking process. The Merck Molecular Force Field (MMFF94) is one type of energy minimization process often used in organic compounds. The molecular docking of substituted oxindole derivatives on indoleamine macromolecules 2,3-dioxygenase (IDO-1, PDB: 2D0T) by MMFF94 minimization operated by MarvinSketch and Open Babel in PyRx showed different results. The binding affinity energy obtained was also quite different, but the ligands have the same conformation and bind the same residue with slightly different bond distances. Keywords: Molecular docking, energy minimization, substituted oxindole, Merck Molecular Force Field 94  Teknik in silico banyak digunakan untuk penemuan senyawa obat karena dapat memprediksi konformasi suatu ligan dalam makromolekul protein dan mampu menghitung nilai afinitas ikatan. Proses minimisasi energi dilakukan untuk menjadikan ligan lebih stabil mendekati keadaan awal selama penambatan molekular berlangsung. Merck Molecular Force Field (MMFF94) adalah salah satu jenis persamaan minimisasi energi yang sering digunakan pada senyawa organik. Hasil pengujian pengaruh minimisasi energi dengan MMFF94 menggunakan program MarvinSketch dan Open Babel dalam PyRx pada turunan oksindola tersubstitusi alkil terhadap makromolekul 2,3-dioxygenase indoleamine (IDO-1, PDB: 2D0T) menunjukkan hasil dengan nilai yang berbeda. Energi afinitas ikatan yang didapatkan juga cukup berbeda, namun ligan memiliki konformasi yang sama dan mengikat residu yang sama dengan jarak ikatan yang sedikit berbeda. Kata kunci: Penambatan molekular, minimisasi energi, oksindola tersubstitusi, Merck Molecular Force Field 94

scholarly journals An In-Silico Approach for Designing a Potential Antagonistic Molecule Targeting β2-adrenoreceptor Having Therapeutic Significance

2020 ◽  
Vol 10 (2) ◽  
pp. 2063-2069
Keyword(s):  
Drug Discovery ◽  
In Silico ◽  
Active Sites ◽  
De Novo ◽  
Cellular Level ◽  
Ligand Molecule ◽  
Best Fitting ◽  
Β2 Adrenergic Receptor ◽  
External Stimuli ◽  
G Protein Coupled

One of the largest families of membrane proteins, the G protein-coupled receptors (GPCRs) has been a very important target of drug discovery as they are involved in having a regulatory role in a variety of signaling pathways at the cellular level in response to external stimuli. Modern in-silico and crystallographic approaches have further made it easier to peep into their structures. In this study, β2 adrenergic receptor (β2AR) has been targeted, and a new ligand molecule using the de-novo approach has been proposed. Using 1-Amino-3-(2,3-dihydro-1H-indol-4-yloxy)-propan-2-ol, the best fitting binding fragments were established with a significant dissociation constant value of 5-7 nanomolar. The flexibility of specific active sites was also investigated, and it was observed that residues 114 (V), 117 (V), 203 (S), 286 (W), and 289 (F) played a crucial role in accommodating ligand for the best binding. Upon examination of the bioavailability parameters, the ligand var9 exhibited significant inhibitory characteristics having lower toxicity values and high drug likeliness properties. Findings certainly hold significance in terms of targeting GPCRs in getting insight into structure-based drug designing and drug discovery.

scholarly journals Providing the ‘Best’ Lipophilicity Assessment in a Drug Discovery Environment

2021 ◽  
Author(s):  
george chang ◽  
Nathaniel Woody ◽  
Christopher Keefer
Keyword(s):  
Machine Learning ◽  
Drug Discovery ◽  
High Throughput ◽  
In Silico ◽  
Shake Flask ◽  
Chromatographic Method ◽  
Learning Approach ◽  
Rule Based ◽  
Machine Learning Approach ◽  
High Throughput Screens

Lipophilicity is a fundamental structural property that influences almost every aspect of drug discovery. Within Pfizer, we have two complementary high-throughput screens for measuring lipophilicity as a distribution coefficient (LogD) – a miniaturized shake-flask method (SFLogD) and a chromatographic method (ELogD). The results from these two assays are not the same (see Figure 1), with each assay being applicable or more reliable in particular chemical spaces. In addition to LogD assays, the ability to predict the LogD value for virtual compounds is equally vital. Here we present an in-silico LogD model, applicable to all chemical spaces, based on the integration of the LogD data from both assays. We developed two approaches towards a single LogD model – a Rule-based and a Machine Learning approach. Ultimately, the Machine Learning LogD model was found to be superior to both internally developed and commercial LogD models.<br>

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