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Swarm Intelligence and Evolutionary Algorithms for Drug Design and Development
Published in Sandeep Kumar, Anand Nayyar, Anand Paul, Swarm Intelligence and Evolutionary Algorithms in Healthcare and Drug Development, 2019
The QSAR modelling may be considered as one of the developed fields with respect to areas in drug development through computational chemistry. Similar kind of molecules with little change in aspects of its structure can show different biological traits altogether. This kind of relationship between molecular structures as well as the biological activities may be regarded as prime concentration factor of QSAR modelling [46]. The property predictions or any activity of interest have the capacity to save both time, money as well as minimize the usage of costly experimental designs, e.g. animal testing [47,48].
Superoxide Dismutase, Mitochondrial Dysfunction, and Neurodegenerative Diseases
Published in Shamim I. Ahmad, Handbook of Mitochondrial Dysfunction, 2019
Jahaun Azadmanesh, Gloria E. O. Borgstahl
Computational chemistry supports a compromise between the two binding modes. These studies suggest O2•– binds directly to Mn(III) but not to Mn(II) [82,93]. Said another way, the substrate at the active site alternates between inner and outer-sphere binding depending on which half-reaction of the catalytic cycle MnSOD is using.
Overview of Drug Development
Published in Mark Chang, John Balser, Jim Roach, Robin Bliss, Innovative Strategies, Statistical Solutions and Simulations for Modern Clinical Trials, 2019
Mark Chang, John Balser, Jim Roach, Robin Bliss
Computer aided molecular design and modeling is the central part of computational chemistry, which use 3-D structures of compounds in virtual chemical compound libraries to determine the SARs of ligand-protein receptor binding. The aim of computational chemistry is to perform virtual screening using computer-generated ligands. Libraries of virtual ligands are generated on computer based on certain building blocks or framework (scaffolds) of chemical compounds. Methods such as genetic algorithm and genetic programming can be used, which simulates the genetic evolutionary process to produce ’generations’ of virtual compounds with new structures that have improved ability to bind the receptor protein, similar to the concept of ’survival for the fittest’ in the biological process. See Chapter 13 for more discussion.
Design, synthesis, molecular modelling and antitumor evaluation of S-glucosylated rhodanines through topo II inhibition and DNA intercalation
Published in Journal of Enzyme Inhibition and Medicinal Chemistry, 2023
Ahmed I. Khodair, Fatimah M. Alzahrani, Mohamed K. Awad, Siham A. Al-Issa, Ghaferah H. Al-Hazmi, Mohamed S. Nafie
Computational chemistry has come a long way in the past few decades, and it is now commonly used alongside experimental methods to study organic and biological structures and reactions. Structures, characteristics of molecules, processes, and selectivity of reactions can all be better understood with the use of computations59. Density functional theory (DFT) is widely used to calculate many different types of molecular properties, including but not limited to molecular structures, vibrational frequencies, chemical shifts, non-linear optical (NLO) effects, natural bond orbital (NBO) analysis, molecular electrostatic potential, frontier molecular orbitals, and thermodynamic properties60–68. Herein, we detail the design, synthesis, anticancer screening, and spectroscopic analysis of a series of nitrogen glucosylated carrying 2-thioxo-4-thiazolidinone bases. The purpose of this work is to use density functional theory to analyse how alterations to molecular and electronic structure affect the biological activity of the substances under research, and to try to locate a strong correlation between theoretical data and actual observations.
Diagnosis, prevention, and treatment of coronavirus disease: a review
Published in Expert Review of Anti-infective Therapy, 2022
Manoj Kumar Sarangi, Sasmita Padhi, Shrivardhan Dheeman, Santosh Kumar Karn, L. D. Patel, Dong Kee Yi, Sitansu Sekhar Nanda
Recent findings on the toxicity concerns of NPs show that careful investigation is needed to amplify their theranostic ability for respiratory viruses such as SARS-CoV-2. Among the reported novel NPs, a few can be used in COVID-19 treatment owing to different mechanisms of action: (i) polymers that show rapid mucus penetration and do not remain stuck; (ii) biodegradable NPs with the stability to overcome cell membranes and act in the lung with minimal levels of toxicity, causing no lesions during treatment; and (iii) NPs that have undergone modification of their chemical structures by the addition of surface-capping agents such as polyethylene glycol (PEG). Interestingly, hydrogels called ‘Nanotraps’ are helpful to trap living viruses, RNA, and proteins. This novel technology could be used for treating infections caused by SARS-CoV-2 and other emerging viruses [89]. Advancements in computational models have led to the identification of complex interactions between nanomaterials and the development of their efficacy. This has improved our knowledge on the differential effects of NPs on healthy and tumor cells, resulting in better predictions regarding their pharmacokinetics and pharmacodynamics. Therefore, in silico approaches for the repurposing of drugs, as well as molecular docking, molecular dynamics, and computational chemistry are valuable tools for conducting pre-clinical and clinical trials of nanomaterials. In this pandemic, considering the urgent need for nanomedicines, in silico analysis has been useful for the formulation of new effective NPs against SARS-CoV-2 [89].
Binding affinity in drug design: experimental and computational techniques
Published in Expert Opinion on Drug Discovery, 2019
Visvaldas Kairys, Lina Baranauskiene, Migle Kazlauskiene, Daumantas Matulis, Egidijus Kazlauskas
The electronic structure-based methods in principle are able to achieve absolute accuracy of 1–3 kcal/mol [6]. It should be noted that the calculation of relative binding affinities of a series of compounds is generally easier and more accurate than calculation of the absolute binding affinities. A recent very thorough review of QM binding affinity calculations by Ryde and Söderhjelm [77] concludes with an interesting fact that MM methods performed better than QM in the SAMPL4 host-guest blind binding affinity prediction challenge [78] due to a variety of reasons, one of them being a simple cancellation of errors. Since MM parameters are often QM-derived, it is obvious that QM calculations still have a large leeway for development. Indeed, the speed and accuracy of QM calculations are slowly but constantly being improved. Development of efficient and accurate hybrid Density Functional Theory functionals by Grimme’s group [79] is but one example of such progress. The main challenges and perspectives of the quantum methods in computational chemistry have been recently comprehensively outlined by Grimme and Schreiner [80].