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Computer-assisted organic synthesis software is a type of application software used in organic chemistry in tandem with computational chemistry to help facilitate the tasks of designing, predicting, and producing chemical reactions. CAOS aims to identify a series of chemical reactions which, from a starting compound, can produce a desired molecule.
In general, a machine learning system can usually be trained to recognize elements of a certain class given sufficient samples. [30] For example, machine learning methods can be trained to identify specific visual features such as splice sites. [31] Support vector machines have been extensively used in cancer genomic studies. [32]
The invariance properties of molecular descriptors can be defined as the ability of the algorithm for their calculation to give a descriptor value that is independent of the particular characteristics of the molecular representation, such as atom numbering or labeling, spatial reference frame, molecular conformations, etc. Invariance to molecular numbering or labeling is assumed as a minimal ...
The Tinker package is based on several related codes: (a) the canonical Tinker, version 8, (b) the Tinker9 package as a direct extension of canonical Tinker to GPU systems, (c) the Tinker-HP package for massively parallel MPI applications on hybrid CPU and GPU-based systems, (d) Tinker-FFE for visualization of Tinker calculations via a Java-based graphical interface, and (e) the Tinker-OpenMM ...
It is well known for instance that within a particular family of chemical compounds, especially of organic chemistry, that there are strong correlations between structure and observed properties. A simple example is the relationship between the number of carbons in alkanes and their boiling points .
Machine learning and data mining often employ the same methods and overlap significantly, but while machine learning focuses on prediction, based on known properties learned from the training data, data mining focuses on the discovery of (previously) unknown properties in the data (this is the analysis step of knowledge discovery in databases).
As an example, ant colony optimization [3] is a class of optimization algorithms modeled on the actions of an ant colony. [4] Artificial 'ants' (e.g. simulation agents) locate optimal solutions by moving through a parameter space representing all possible solutions.
The project was conceived around 1988. [6] At first it was intended to be a companion to a chemical reactor design course. Full development was started by John W. Eaton in 1992. The first alpha release dates back to 4 January 1993 and on 17 February 1994 version 1.0 was released. Version 9.2.0 was released on 7 June 2024. [7]