Overview of Drug Development
Mark Chang, John Balser, Jim Roach, Robin Bliss in Innovative Strategies, Statistical Solutions and Simulations for Modern Clinical Trials, 2019
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.
Swarm Intelligence and Evolutionary Algorithms for Drug Design and Development
Sandeep Kumar, Anand Nayyar, Anand Paul in 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].
Conversion of Natural Products from Renewable Resources in Pharmaceuticals by Cytochromes P450
Peter Grunwald in Pharmaceutical Biocatalysis, 2019
In the previous sections, we have already mentioned that cytochromes P450 physiologically act on thousands of different substrates and they are important in the biosynthesis and degradation of many different natural compounds. Taking into account that a third of the drugs approved in the past 20 years by the FDA are natural products or derivatives (Newman and Cragg, 2012; Carter et al., 2011), P450 enzymes are one of the best possible choices to synthesize and modify natural products and to develop new compounds. In 2015, the Nobel Prize in Physiology or Medicine was awarded to William C. Campbell and Prof. Satoshi Omura for the discovery of the microbial natural product avermectins, and to Youyou Tu for the discovery of the antimalaria agent artemisinin, which was already used in the traditional Chinese medicine. Thanks to the progresses made in microbial genomics and metagenomics, computational chemistry and metabolic engineering, natural product–inspired drug design is currently a re-emerging area (Shen, 2015). Computational chemistry is becoming a powerful tool to design new compounds with increased pharmacological properties and to identify the possible macromolecular targets (Rodrigues et al., 2016). Moreover, complex biosynthetic pathways, such as the ones present in plants, have been introduced in host organisms such as Escherichia coli and yeasts to accommodate the necessity to increase the synthesis of specific metabolites and lower the costs of production. The economic issue has also been addressed by engineering metabolic pathways in fermentative processes that start from drug precursors and low-cost molecules that can be used as carbon source coming from renewable resources. This opportunity fits very well in the modern concept of biorefinery, where the valorization of biowaste and by-products is a crucial challenge to replace fossil sources for the production of carbon-based products (Fava et al., 2015).
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.
Advancing drug discovery via GPU-based deep learning
Published in Expert Opinion on Drug Discovery, 2018
Erik Gawehn, Jan A. Hiss, J. B. Brown, Gisbert Schneider
Graphics processing units (GPUs) have been used in the computer game and movie industries for several decades. Though initial applications of GPUs were exclusively for creation of computer graphics, GPUs have matured to a state where they can be applied for non-graphical purposes. The newer general purpose graphics processing units (GPGPUs) have received considerable attention in computational chemistry, notably as molecular dynamics simulations have exploited the vector and matrix processing capabilities of GPGPUs to improve the time to compute the per-frame trajectories of individual atoms in a system by orders of magnitude [1,2]. Another pharmaceutically relevant application of GPU technology has been to construct systems for analysis of high-content imaging systems, notably phenotypic cellular response to chemical perturbation. As images are matrices of values, leveraging matrix capabilities of GPUs has aided automatic image segmentation, annotation, and analysis.
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].
Related Knowledge Centers
- Chemistry
- Molecular Dynamics
- Molecule
- Spectroscopy
- Theoretical Chemistry
- Experiment
- Cross Section
- Scattering
- Ab Initio Quantum Chemistry Methods
- Molecular Mechanics