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How is molecular orbital theory used in drug research?
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Mercy Phillip Ahmed
How is molecular orbital theory used in drug research?
Common drug design approach does not involve much QC. There are two general approaches - QSAR and docking
QSAR
The idea is that a family of chemicals (training set) with some having desired biological activity, is used to train a pattern recognition algorithm. The trained algorithm is then used on a library of candidates and once recognised as active are then synthesised and tested in a lab.
Docking
The idea is to construct a molecule strongly interacting with a biological important target (a receptor or an enzyme). Since intermolecular interaction are weak, their calculation from first principles is very computationally expensive and is not practical in most cases. Since biologically relevant molecules are usually fairly trivial, even if complex molecules (For example, there is usually no need to consider interactions involving continuous conjugated systems), the intermolecular interactions are easily described using approach of molecular mechanics. So, in this approach a set of molecules is generated and then tested for interaction with a protein target. The tight spot is to find a way two molecules can bind, typically involving search over extremely large conformer space. Billions of potential candidates for ligand-protein complex must be tested, so the computationally cheapest solution (molecular mechanics) is used.
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As you can see, neither approach normally uses orbital theory or any complex QC, and even if such complex QC is involved, it is clearly subservient. Instead machine learning algorithms and ways to efficiently search conformation space for low-energy conformers are the focus of research.
Common drug design approach does not involve much QC. There are two general approaches - QSAR and docking
QSAR
The idea is that a family of chemicals (training set) with some having desired biological activity, is used to train a pattern recognition algorithm. The trained algorithm is then used on a library of candidates and once recognised as active are then synthesised and tested in a lab.
Docking
The idea is to construct a molecule strongly interacting with a biological important target (a receptor or an enzyme). Since intermolecular interaction are weak, their calculation from first principles is very computationally expensive and is not practical in most cases. Since biologically relevant molecules are usually fairly trivial, even if complex molecules (For example, there is usually no need to consider interactions involving continuous conjugated systems), the intermolecular interactions are easily described using approach of molecular mechanics. So, in this approach a set of molecules is generated and then tested for interaction with a protein target. The tight spot is to find a way two molecules can bind, typically involving search over extremely large conformer space. Billions of potential candidates for ligand-protein complex must be tested, so the computationally cheapest solution (molecular mechanics) is used.
========
As you can see, neither approach normally uses orbital theory or any complex QC, and even if such complex QC is involved, it is clearly subservient. Instead machine learning algorithms and ways to efficiently search conformation space for low-energy conformers are the focus of research.
@Mithoron And it often, if not usually, isnt used. It may be a part of generation of descriptors for QSAR and in complicated cases it probably can be used in docking to calcualte interaction energies, but Im not aware of anything beyond that.More
Well, thats all quite OK, but it shows rather how its not used ;) Isnt QM-MM sometimes used, for example?More
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Given a crystal structure of a protein, some people are starting to do QM/MM modelling of the binding pocket. It's necessary to assume that the protein does not change conformation during binding, to keep the computation time reasonable. Unfortunately, this assumption is often false.
Given a crystal structure of a protein, some people are starting to do QM/MM modelling of the binding pocket. It's necessary to assume that the protein does not change conformation during binding, to keep the computation time reasonable. Unfortunately, this assumption is often false.
Common drug design approach does not involve much QC. There are two general approaches - QSAR and docking
QSAR
The idea is that a family of chemicals (training set) with some having desired biological activity, is used to train a pattern recognition algorithm. The trained algorithm is then used on a library of candidates and once recognised as active are then synthesised and tested in a lab.
Docking
The idea is to construct a molecule strongly interacting with a biological important target (a receptor or an enzyme). Since intermolecular interaction are weak, their calculation from first principles is very computationally expensive and is not practical in most cases. Since biologically relevant molecules are usually fairly trivial, even if complex molecules (For example, there is usually no need to consider interactions involving continuous conjugated systems), the intermolecular interactions are easily described using approach of molecular mechanics. So, in this approach a set of molecules is generated and then tested for interaction with a protein target. The tight spot is to find a way two molecules can bind, typically involving search over extremely large conformer space. Billions of potential candidates for ligand-protein complex must be tested, so the computationally cheapest solution (molecular mechanics) is used.
========
As you can see, neither approach normally uses orbital theory or any complex QC, and even if such complex QC is involved, it is clearly subservient. Instead machine learning algorithms and ways to efficiently search conformation space for low-energy conformers are the focus of research.
Common drug design approach does not involve much QC. There are two general approaches - QSAR and docking
QSAR
The idea is that a family of chemicals (training set) with some having desired biological activity, is used to train a pattern recognition algorithm. The trained algorithm is then used on a library of candidates and once recognised as active are then synthesised and tested in a lab.
Docking
The idea is to construct a molecule strongly interacting with a biological important target (a receptor or an enzyme). Since intermolecular interaction are weak, their calculation from first principles is very computationally expensive and is not practical in most cases. Since biologically relevant molecules are usually fairly trivial, even if complex molecules (For example, there is usually no need to consider interactions involving continuous conjugated systems), the intermolecular interactions are easily described using approach of molecular mechanics. So, in this approach a set of molecules is generated and then tested for interaction with a protein target. The tight spot is to find a way two molecules can bind, typically involving search over extremely large conformer space. Billions of potential candidates for ligand-protein complex must be tested, so the computationally cheapest solution (molecular mechanics) is used.
========
As you can see, neither approach normally uses orbital theory or any complex QC, and even if such complex QC is involved, it is clearly subservient. Instead machine learning algorithms and ways to efficiently search conformation space for low-energy conformers are the focus of research.
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Given a crystal structure of a protein, some people are starting to do QM/MM modelling of the binding pocket. It's necessary to assume that the protein does not change conformation during binding, to keep the computation time reasonable. Unfortunately, this assumption is often false.
Given a crystal structure of a protein, some people are starting to do QM/MM modelling of the binding pocket. It's necessary to assume that the protein does not change conformation during binding, to keep the computation time reasonable. Unfortunately, this assumption is often false.
More
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