Knowledge-based prediction predictions, derivation from

Velec, H.F.G., Gohlke, H., Klebe, G. DrugScoreCSD-knowledge-based scoring function derived from small molecule crystal data with superior recognition rate of near-native ligand poses and better affinity prediction. J. Med. Chem. 2005, 48, 6296-303. 

The transition metal based catalytic species derived from hydrogen peroxide or alkyl hydroperoxides are currently regarded as the most active oxidants for the majority of inorganic and organic substrates " An understanding of the mechanism of these processes is therefore a crucial point in the chemistry of catalytic oxidations. This knowledge allows one to predict not only the nature of the products in a given process, but also the stereochemical outcome in asymmetric reactions. 

The need for rapidly accessible estimation of toxicity has led to the development of software and other algorithms that will generate estimations of toxicity, usually for organic compounds [79] such methodology is termed an expert system, which has been defined [34] as any formalised system, not necessarily computer-based, which enables a user to obtain rational predictions about the toxicity of chemicals. Essentially, expert systems fall into two classes— those relying on statistical approaches and those based on explicit rules derived from human knowledge. 

The electronic ground state that a particular metal center adopts is a function of the chromophore. In many cases the ground state may be derived from chemical knowledge (e. g., octahedral cobalt(HI) ( Aig) or tetrahedral Ni(II) (3T() complexes). However, based on the molecular mechanics formalism alone, this problem cannot be solved in a general way. Let us consider coordination compounds which are close to the spin-crossover limit (for example hexacoordinate iron(II) (1Aig/5T2g)). In these cases it is not possible to assign the atom type of the metal center without further information (experimental or theoretical). Therefore, molecular mechanics alone is not always able to predict the structural properties. 

Expert systems have been defined as any formal systems , which make predictions about the toxicity of chemicals. All expert systems for the prediction of toxicity are built on experimental data and/or rules derived from such data (Dearden 2003). The expert systems can be further divided into two subclasses based on the method of generating rules. The one method is a knowledge- or rule-based expert system, for which experts/toxicologists create rules based on a list of structural features that have been related to a specified toxicity (Durham and Pearl 2001). An example of a typical knowledge- or rule-based system is DEREK, which will be described later. 

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