Computational drug design

Lorber, David M.

doi:10.1016/s1074-5521(99)80093-3

Cited by 13 publications

(4 citation statements)

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“…In an early stage of the drug discovery process, the focus is on reducing the number of drug candidates and this problem has been deciphered using computational approaches. 2 A drug is a small molecule which activates or inhibits the function of protein, as proteins are one of the popular targets for the drug designing process. 3 The interaction between a protein and ligand is specific.…”

Section: Introductionmentioning

confidence: 99%

A machine learning approach towards the prediction of protein–ligand binding affinity based on fundamental molecular properties

2018

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There is an exigency of transformation of the enormous amount of biological data available in various forms into some significant knowledge. We have tried to implement Machine Learning (ML) algorithm models on the protein-ligand binding affinity data already available to predict the binding affinity of the unknown. ML methods are appreciably faster and cheaper as compared to traditional experimental methods or computational scoring approaches. The prerequisites of this prediction are sufficient and unbiased features of training data and a prediction model which can fit the data well. In our study, we have applied Random forest and Gaussian process regression algorithms from the Weka package on proteinligand binding affinity, which encompasses protein and ligand binding information from PdbBind database. The models are trained on the basis of selective fundamental information of both proteins and ligand, which can be effortlessly fetched from online databases or can be calculated with the availability of structure. The assessment of the models was made on the basis of correlation coefficient (R 2 ) and root mean square error (RMSE). The Random forest model gave R 2 and RMSE of 0.76 and 1.31respectively. We have also used our features and prediction models on the dataset used by others and found that our model with our features outperformed the existing ones.

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Section: Introductionmentioning

confidence: 99%

A machine learning approach towards the prediction of protein–ligand binding affinity based on fundamental molecular properties

2018

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“…Before choosing the protein, the activities of the specific ligand were investigated, and the strong activities were taken into consideration. For the interaction, the protein was received in PDB format from the RCBS protein library, , as shown in Figure B,D. With the help of AutodockVina and Chimera, the titled molecule is studied further for biological activity.…”

Section: Resultsmentioning

confidence: 99%

Synthesis, Electrochemical Studies, Molecular Docking, and Biological Evaluation as an Antimicrobial Agent of 5-Amino-6-cyano-3-hydroxybenzo[c]coumarin Using Ni–Cu–Al–CO₃ Hydrotalcite as a Catalyst

et al. 2022

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New insights via a multicomponent cyclocondensation reaction of 2-hydroxy benzaldehyde acetoacetic ester and malononitrile were attained under efficient conditions. A detailed mechanistic study exhibits that the reaction via multicomponent, one-pot, two-step incooperating three-components’ reactants under nominal conditions efficiently synthesizes the intermediates in shorter than reported time along with the formation of a novel compound [5-amino-6-cyano-3-hydroxybenzo[ c ]coumarin] in the presence of Ni–Cu–Al–CO 3 hydrotalcite as a heterogeneous catalyst. Hydrotalcite functions as an efficient and versatile catalyst as it is safe, easy to work up, and recyclable many times under ambient conditions, and the reaction time is shorter. The aforementioned conditions make these solid basic heterogeneous catalysts environmentally friendly. The product yield obtained was 89%. The product was characterized using FTIR, LCMS, and NMR. Electrochemical studies were also carried out to check the reduction and oxidation behavior. The synthesized product showed antimicrobial activity. Antimicrobial activity against human pathogens, viz, S. Aureus , P. Aeruginosa , and P. Bulgaria , was studied by using the agar well diffusion method. The molecular docking studies of 5-amino-6-cyano-3-hydroxybenzo[ c ]coumarin exhibit its suitable attachment at the active center of the type IIA topoisomerases and gyrase enzymes which suggest its potential antibacterial activity when compared using ciprofloxacin drug as the control.

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“…In this paper, we have advanced a new hypothesis that reprogramming of tumor cells can be achieved by avoiding toxic chemotherapy and instead by using a completely different set of pharmacological agents, in particular psychoactive compounds. Their repurposing can be achieved by rational drug design [101] involving derivatization that should take into account the molecular structure of the target and such aspects as blood-brain-barrier permeation.…”

Section: Resultsmentioning

confidence: 99%

Ion Channel and Neurotransmitter Modulators as Electroceutical Approaches to the Control of Cancer

Tuszyński

Tilli

Levin

2017

CPD

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The activities of individual cells must be tightly coordinated in order to build and maintain complex 3- dimensional body structures during embryogenesis and regeneration. Thus, one way to view cancer is within systems biology as a network disorder affecting the ability of cells to properly interact with a morphodynamic field of instructive signals that keeps proliferation and migration orchestrated toward the anatomical needs of the host organism. One layer of this set of instructive microenvironmental cues is bioelectrical. Voltage gradients among all somatic cells (not just excitable nerve and muscle) control cell behavior, and the ionic coupling of cells into networks via electrochemical synapses allows them to implement tissue-level patterning decisions. These gradients have been increasingly implicated in the induction and suppression of tumorigenesis and metastasis, in the emerging links between developmental bioelectricity to the cancer problem. Consistent with the well-known role of neurotransmitter molecules in transducing electrical activity to downstream cascades in the brain, serotonergic signaling has likewise been implicated in cancer. Here, we review these recent data and propose new approaches for manipulating bioelectric and neurotransmitter pathways in cancer biology based on a bioelectric view of cancer. To support this methodology, we present new data on the effects of the SSRI Prozac and its analog (ZINC ID = ZINC06811610) on survival of both cancer (MCF7) and normal (MCF10A) breast cells exposed to these compounds. We found an IC50 concentration (25 µM for Prozac and 100 µM for the Prozac analog) at which these compounds inhibited tumor cell survival and proliferation. Additionally, at these concentrations, we did not observe alterations in a non-tumoral cell line. This constitutes a proof-of-concept demonstration for our hypothesis that the use of both existing and novel drugs as electroceuticals could serve as an alternative to highly toxic chemotherapy strategies replacing or augmenting them with less toxic alternatives. We believe this new approach forms an exciting roadmap for future biomedical advances.

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Computational drug design

Cited by 13 publications

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A machine learning approach towards the prediction of protein–ligand binding affinity based on fundamental molecular properties

A machine learning approach towards the prediction of protein–ligand binding affinity based on fundamental molecular properties

Synthesis, Electrochemical Studies, Molecular Docking, and Biological Evaluation as an Antimicrobial Agent of 5-Amino-6-cyano-3-hydroxybenzo[c]coumarin Using Ni–Cu–Al–CO₃ Hydrotalcite as a Catalyst

Ion Channel and Neurotransmitter Modulators as Electroceutical Approaches to the Control of Cancer

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Computational drug design

Cited by 13 publications

References 0 publications

A machine learning approach towards the prediction of protein–ligand binding affinity based on fundamental molecular properties

A machine learning approach towards the prediction of protein–ligand binding affinity based on fundamental molecular properties

Synthesis, Electrochemical Studies, Molecular Docking, and Biological Evaluation as an Antimicrobial Agent of 5-Amino-6-cyano-3-hydroxybenzo[c]coumarin Using Ni–Cu–Al–CO3 Hydrotalcite as a Catalyst

Ion Channel and Neurotransmitter Modulators as Electroceutical Approaches to the Control of Cancer

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Synthesis, Electrochemical Studies, Molecular Docking, and Biological Evaluation as an Antimicrobial Agent of 5-Amino-6-cyano-3-hydroxybenzo[c]coumarin Using Ni–Cu–Al–CO₃ Hydrotalcite as a Catalyst