Quantum Mechanics of Molecular Structures

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As the beam is scattered by the nuclei within the molecule, the scattered waves interfere with each other to generate a diffraction pattern. In week 4, we study the fundamental mechanism of electron scattering and how the resulting diffraction images reveal the geometrical structure of molecules. By the end of the course, you will be able to understand molecular vibration plays an important role in determining the geometrical structure of molecules and gain a fuller understanding of molecular structure from the information obtained by the two methodologies.

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Found in Quantum Mechanics , Science. Go to class. Become a Data Scientist datacamp. Start now for free! Sign up. Overview Knowing the geometrical structure of the molecules around us is one of the most important and fundamental issues in the field of chemistry. The usefulness of quantum mechanics QM in drug—protein interaction cannot be overemphasized; however, this approach has little significance in some empirical methods. In this review, we discuss recent developments in, and application of, QM to medically relevant biomolecules.

We critically discuss the different types of QM-based methods and their proposed application to incorporating them into drug-design and -discovery workflows while trying to answer a critical question: are QM-based methods of real help in drug-design and -discovery research and industry? Drug discovery plays an important role in the growth of any pharmaceutical company and society, as newer and safer drugs are launched in the market with the sole objective of improving the therapeutic value and safety of drugs.

The pharmaceutical industry has consistently shown that it can discover and develop innovative medicines for a wide range of diseases. Drug research, as it is called today, began when chemistry had reached the peak of its career, allowing chemical principles and theories to be applied to problems outside the scope of chemistry, and when pharmacology became an independent scientific discipline on its own.

By , some of the important foundations of chemistry theory had been laid. It was during this period that the concept of targeting enzymes and designing drugs as inhibitors came into existence. Table 1 Some of the important discoveries in medicine in the last two centuries. Figure 1 Flowchart of drug-discovery and -development process.

One of the factors contributing to the high attrition rates is an active compound with unacceptable absorption, distribution, metabolism, excretion, and toxicity ADMET adverse effects that thus needs to be withdrawn from development. The process of finding a molecule that binds to the target protein has now moved from the laboratory to the computer. Therefore, many promising compounds will regrettably have to be rejected once they are found to show unacceptable adverse effects in humans. Furthermore, compared to the status a decade ago, protein structure-based DD is swiftly gathering energy, and results have shown a remarkable increase in the structural knowledge of medically relevant proteins through various methods, 13 — 18 as well as computer-aided programs.

The large number of structural studies on medically relevant proteins suggests that the structure of a potential drug target is treasurable knowledge for any pharmaceutical company, not only for lead discovery and lead optimization but also in the later stages of drug development, where such concerns as toxicity, bioavailability, and binding modes of potential drug candidates to the target protein are extremely important.

A drug reveals its action when it binds to its biological target enzyme, nucleic acid, or antibody , typically receptors. Receptors possess the active sites for the binding of a drug. Therefore, it is important to know the structure of the target, in order to design a good drug and identify an accurate binding site. However, some biologically relevant biomolecules lack X-ray crystal structures. To resolve this, homology modeling has been implemented, 19 and modeled proteins behave somewhat like the real proteins in their native biological environment when simulated.

The entire process is about the simulation of the biological targets using quantum mechanics QM calculation, which is based on the principles of chemistry and physics. CADD is aimed at improving the development and efficacy of drugs using modern computational tools that are fast and cost-effective compared to conventional methods. Figure 2 Different in silico tools used in drug design.

The CADD approach has been applied to various successful drugs, some of which are in use in the market. Examples include imatinib 28 and nilotinib. MM is commonly applied in large systems to calculate molecular structures and relative potential energies of a molecular conformation or atom arrangement. The exclusion of electrons in MM is justified on the basis of Born—Oppenheimer approximation, 39 which states that electronic and nuclear motions can be uncoupled from each other and considered separately.

Energy differences between conformations are significant in such calculations, rather than absolute values of potential energies. MM can simply be viewed as a ball-and-spring model of atoms and molecules with classical forces between them.

Artificial intelligence algorithm can learn the laws of quantum mechanics

Potential energy functions are equipped with parameters designed to reproduce experimental properties. Energy contributions of the latter constitute van der Waals and electrostatic interactions:. Energy contributions from special treatment of hydrogen bonding and stretch—bend coupling interactions may also be seen in MM.

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QM is important in understanding the behavior of systems at the atomic level. According to QM, an electron bound to an atom cannot possess any arbitrary energy or occupy any position in space. H can also be defined as:. QM methods include ab initio 44 density functional theory DFT 45 — 47 and semiempirical calculations. Classical mechanics, also called MM, is the alternative to QM when chemical reactions do not need to be considered in a simulation.

Molecular Modelling by Dr Marek Szczerba

It is the use and extent of this information that distinguishes different MM models. QM has been said to succeed outstandingly in the area where MM failed. In contrast to QM, MM ignores electrons, fails to illustrate reality, and also computes the energy of a system as only a function of the nuclear positions. Generally, QM incorporates four phenomena for which MM cannot justify.

These include quantization of some physical properties, quantum entanglement, the principle of uncertainty, and wave—particle duality. QM is applied in the determination of interactions between possible drugs and enzyme active sites. In answering research questions, computational chemists have a vast selection of methodologies at their disposal. MM can be used to study very large molecules, because other QM methods, such as semiempirical calculations, ab initio, and DFT are relatively slow and would exhaust computational resources.

However, MM methods are unable to address interactions between the ligand and the receptor in metal-containing systems. QM-MM is thus the crucial component in computational drug discovery. Five key facets are imperative in planning a QM-MM calculation on an enzyme: choice of the QM method, choice of MM force field, segregation of the system into QM and MM regions, simulation type eg, MD simulation or calculation of potential energy profiles , and whether advanced conformational sampling will be performed.

The choice of QM method is crucial. A plethora of different QM methods exists, ranging from fast, semiempirical methods eg, AM1, PM3, SCC-DFTB; low accuracy and maximum of 2, atoms to more accurate but more computationally expensive Hartree—Fock and density-functional eg, B3LYP; medium accuracy and maximum of atoms , and molecular orbital ab initio eg, MP2, coupled cluster; very high accuracy and maximum of 20 atoms methods.

Not all methods are applicable to all systems, for reasons of accuracy, practicality, or lack of parameters eg, for semiempirical methods Table 2. Generally, but not always, improved accuracy comes at the price of increased calculation expenses. The use of supercomputing to calculate QM has been attributed to expensive calculations for small systems. However, the use of Hadoop 80 could make QM faster and more scalable and efficient.

Hadoop could allow for better cluster utilization as well to accommodate larger jobs, which will help QM, as it needs more computational resources to run calculations for larger systems.

Tangible advances in the use of QM to solve relevant pharmaceutical problems have been seen in the last decade, eg, the use of the hybrid QM-MM approach to determine the free-energy landscape of the enzymatic reaction mechanism. However, there are concerns regarding the accuracy of these methods, particularly in the area of docking and scoring 90 — 93 and QSAR. The in silico approach is fast and environmentally friendly, but it does not replace experimentation. Regardless, failures encountered in the pharmaceutical industry at the drug-discovery stage can be attributed to a number of factors that are not limited to wrong force-field parameters, especially for metals, 96 disregard for protein flexibility 97 , 98 or domain of applicability, 99 or nonrigorous validation of the QSAR model.

QM, a method used to replicate an experimental work accurately, proffers a potential solution to the failures mentioned. The reaction mechanisms of these enzymes have been studied using the QM-MM approach. The application of QM is limited to relatively small systems Table 2. On the other hand, MM methods can treat millions of atoms or more. In addition, a recent article implicitly explained that the need for suitable computational approaches or tools could enhance success rates in the drug-discovery process.

The process of docking involves the correct prediction of ligand conformation and orientation within a targeted binding site, while scoring predicts the binding free energy of a complex formation. Each docking program is ideal for precise docking problems; however, combining different computational methods can improve the reliability and accuracy of results.

As such, implementation of QM docking would systematically improve the accuracy of description of enzyme—ligand interactions, as well as binding affinity.

AI Is Learning Quantum Mechanics to Design New Molecules

Limitations in scoring functions are being increasingly exposed, particularly as more challenging and electronically complex pockets are being probed, eg, systems with metals. Calculations indicated generally improved poses after docking. The ability of QM-MM docking has been further evaluated in other studies to predict the poses of metalloproteins. Therefore, there appears to be evidence that QM-based models provide scoring functions that can improve the quality of predicted docking poses for challenging receptors.

Virtual screening has become a powerful tool in the drug-discovery process to search for novel compounds with desired properties. This is followed by a scoring function.

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QSAR is a mathematical representation that attempts to correlate a set of compounds with dependent variables activity values, eg, K i , EC 50 , ED 50 , IC 50 and a set of independent variables called descriptors. As the ADMET properties of a drug determine its activity, the development of a new drug with reasonable ADMET makes drug discovery a more difficult and challenging process in the pharmaceutical industry.

The pharmaceutical industry is progressively operating in an era where development costs are constantly under pressure, higher percentages of drugs are demanded, and the drug-discovery process is a trial-and-error run. The profits that flow in with the discovery of new drugs have always been the motivation for the industry to keep up the pace and keep abreast with the endless demand for medicines.

In recent years, the use of CADD to simulate drug—receptor interactions has made rational DD feasible and cost-effective. However, more attention should be paid to the way pharmaceutical companies use in silico tools. While docking, virtual screening, QSAR, and MM manage computational resources and allow rapid scans of large libraries, the accuracy of the results is in question when it comes to experimental data correlation. Therefore, using a combination of QM to parameterize the molecules and MM to describe and solvate the protein, a more accurate understanding of binding affinity and protein—molecule interaction could be gained Figure 3.

Furthermore, using QSAR to predict the activity of an existent molecule may lead to remarkable savings with respect to development time and cost Table 3.