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Thus, a compatibility graph between two molecules can be traversed in a much faster way.
Using such fingerprints, the similarity between two molecules can be calculated in a straightforward manner with simple similarity metrics such as Tanimoto [1], Dice [2], and so on.
The ST, CT, and ComboT scores between two molecules can be evaluated in two different molecular superpositions [24 26]: (1) in the ST- or shape-optimized superposition, and (2) in the CT- or feature-optimization superposition.
A reaction between two molecules can only occur, if the reactants are close enough together.
After the preprocessing, the actual similarity calculation between two molecules can be conducted.
The first one is the space of biological functions, where the distance between two molecules can be defined by the number of steps (edges) in a graph defining pairwise functional relations (such as protein-protein interactions) along the shortest path connecting them.
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The better the molecular graphs (i.e. the topology and the atom types) of two molecules can be matched, the greater the similarity between these two molecules (1 = identical, 0 = dissimilar).
Thus, these methods can handle experimental FLIM data where only two molecules can be simultaneously monitored.
Regarding the strength of interaction between two molecules, we can assess it by determining the value of binding affinity, K A. Among these five mutant aptamers, the Seq15_12_35 had the largest K A value, and Seq16 had the worst K A value.
Thereby, every possible binary interaction between two molecules in the system can be accounted for, resulting in an unprecedented resolution regarding oligomeric species and improved predictive capabilities based on a small number of parameters.
(64) The self-dimer function shows the potentially stable structures that can form between two molecules.
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