The optimization of reactions used to synthesize target compounds is pivotal to chemical research and discovery, whether in developing a route for manufacturing a life-saving medicine1 or unlocking the potential of a new material2. But reaction optimization requires iterative experiments to balance the often conflicting effects of numerous coupled variables, and frequently involves finding the sweet spot among thousands of possible sets of experimental conditions. Expert synthetic chemists currently navigate this expansive experimental void using simplified model reactions, heuristic approaches and intuition derived from observation of experimental data3. Writing in Nature, Shields et al.4 report machine-learning software that can optimize diverse classes of reaction with fewer iterations, on average, than are needed by humans. Machine learning has emerged as a useful tool for various aspects of chemical synthesis, because it is ideally suited to extrapolating predictive models that are used to solve synthetic problems by recognizing patterns in multidimensional data sets5.

Our blood transports many chemicals besides oxygen and carbon dioxide. Some of these molecules provide useful indicators of the state of our health. Indeed, measuring such biomarkers is a common feature of clinical blood tests. Other molecules present, such as hormones and drugs, directly affect health by modulating processes such as metabolism and immune responses. Writing in Nature, Bar et al.1 shed light on the factors that affect the recipe for human blood's chemical brew.

U.S. stocks are slumping in Monday morning trading as healthcare companies and banks take the biggest losses. Energy companies are inching higher as oil prices rise. Major indexes in Europe and Asia are also starting the week on a steep skid. The Dow Jones industrial average was down 108 points, or 0.6%, to 18,153 as of 10:15 a.m. The Standard & Poor's 500 index fell 10 points, or 0.4%, to 2,155.

In particular its task domain is the analysis of mass spectra, chemical data gathered routinely from a relatively new analytical instrument, the mass spectrometer. This collaboration of chemists and computer scientists has produced what appears to be an interesting program from the viewpoint of artificial intelligence and a useful tool from the viewpoint of chemistry. For this discussion it is sufficient to say that a mass spectrometer is an instrument into which is put a minute sample of some chemical compound and out of which comes data usually represented as a bar graph. This is what is referred to here as the mass spectrum. The x-points of the bar graph represent the masses of ions produced and the y-points represent the relative abundances of ions of these masses. The first, preliminary inference (or planning), obtains clues from the data as to which classes of chemical compounds are suggested or forbidden by the data.

Georgeff's controlled production systems 65 8.3.3

This paper presents an "expert system" devised to aid organic chemists in determining the structure (i.e. the arrangement of atoms and bonds) of newly isolated, naturally occurring compounds. The system exploits a data base of rules for analyzing.013 C nuclear magnetic resonance spectra" [2. 13 C spectroscopy is a relatively new technique; only in the last ten years, at most, has this analytic approach been practical. Currently, no general interpretive schemes exist for thoroughly analyzing 13C spectra. A few limited classes of compounds have been investigated in detail, and highly specific schemes for interpreting their 13C spectra have been developed. The only generally applicabl- "interpretation rules" rely on correlation J of spectral and substructural features.

CONGEN: the constrained generation of chemical structures.

An algorithm and its implementation as a computer program is described which for the first time permits the enumeration and construction of all the distinct stereoisomers possible which are consistent with a given empirical formula. The algorithm finds the stereocenters in a chemical structure, takes full account of any symmetry and produces the stereoisomers with cis/trans and R/C designations along with a canonical (unique) name. Examples of its use and a discussion of potential applications are given.

For This paper and the following one' describe the current effort most applications the familiar geometric point group is chosen to provide the CONG EN (for constrained structure generation) .22 In some spectroscopic applications It is necessary to take program with stereochemical capabilities.* This paper is primarily internal motion into account and specify a nonrigid symmetry concerned with the chemical and mathematical theory group.2b For applications in dynamic stereochemistry it is necessary to this effort. The following paper is concerned primarily necessary to consider the group of all permutations of identical with novel algorithms and the computer implementation.7 atoms and often several subgroups.3 Symmetry groups that A third paper considers the theory in greater mathematical include the point group and operations that invert chiral centers detail with some extensions to other topics.' are useful both in constructing chirality functions4 and in specifying the pseudochirality of a structure.5

Reprinted with permission from the Journal of the American Chemical Society, Vol.99,No.7,pp.2063-2069. Abstract: A stereoisomerizat ion mode can be defined as a set of symmetry equivalent degenerate rearrangements of a molecular skeleton. The key mathematical constructions in this definition are the double cosets of the skeletal point group in some larger permutation group of identically substituted skeletal sites. S,,L x S.s. is defined which includes all permutations which act on ligand and site labels separately. The generalized stereoisomerization modes are found to be collections of double cosets in this group.