Biomedical subjects

Thomas Bäck

Publications and source records attributed to Thomas Bäck.

4 recordsLinked to original sources

PubMed2006

DNA computing of solutions to knapsack problems.

One line of DNA computing research focuses on parallel search algorithms, which can be used to solve many optimization problems. DNA in solution can provide an enormous molecular library, which can be searched by molecular biological techniques. We have implemented such a parallel search for solutions to knapsack problems, which ask for the best way to pack a knapsack of limited volume. Several instances of knapsack problems were solved using DNA. We demonstrate how the computations can be extended by in vivo translation of the DNA library into protein. This combination of DNA and protein allows for multi-criterion optimization. The knapsack computations performed can then be seen as protein optimizations, one of the most complex computations performed by natural systems.

Algorithms↗

PubMedDate not supplied

The molecule evoluator. An interactive evolutionary algorithm for the design of drug-like molecules.

We developed a software tool to design drug-like molecules, the "Molecule Evoluator", which we introduce and describe here. An atom-based evolutionary approach was used allowing both several types of mutation and crossover to occur. The novelty, we claim, is the unprecedented interactive evolution, in which the user acts as a fitness function. This brings a human being's creativity, implicit knowledge, and imagination into the design process, next to the more standard chemical rules. Proof-of-concept was demonstrated in a number of ways, both computationally and in the lab. Thus, we synthesized a number of compounds designed with the aid of the Molecule Evoluator. One of these is described here, a new chemical entity with activity on alpha-adrenergic receptors.

Algorithms↗

PubMedDate not supplied

Mining a chemical database for fragment co-occurrence: discovery of "chemical clichés".

Nowadays millions of different compounds are known, their structures stored in electronic databases. Analysis of these data could yield valuable insights into the laws of chemistry and the habits of chemists. We have therefore explored the public database of the National Cancer Institute (>250,000 compounds) by pattern searching. We split the molecules of this database into fragments to find out which fragments exist, how frequent they are, and whether the occurrence of one fragment in a molecule is related to the occurrence of another, nonoverlapping fragment. It turns out that some fragments and combinations of fragments are so frequent that they can be called "chemical clichés". We believe that the fragment data can give insight into the chemical space explored so far by synthesis. The lists of fragments and their (co-)occurrences can help create novel chemical compounds by (i) systematically listing the most popular and therefore most easily used substituents and ring systems for synthesizing new compounds, (ii) being an easily accessible repository for rarer fragments suitable for lead compound optimization, and (iii) pointing out some of the yet unexplored parts of chemical space.

Antineoplastic Agents↗

PubMedDate not supplied

Substructure mining using elaborate chemical representation.

Substructure mining algorithms are important drug discovery tools since they can find substructures that affect physicochemical and biological properties. Current methods, however, only consider a part of all chemical information that is present within a data set of compounds. Therefore, the overall aim of our study was to enable more exhaustive data mining by designing methods that detect all substructures of any size, shape, and level of chemical detail. A means of chemical representation was developed that uses atomic hierarchies, thus enabling substructure mining to consider general and/or highly specific features. As a proof-of-concept, the efficient, multipurpose graph mining system Gaston learned substructures of any size and shape from a mutagenicity data set that was represented in this manner. From these substructures, we extracted a set of only six nonredundant, discriminative substructures that represent relevant biochemical knowledge. Our results demonstrate the individual and synergistic importance of elaborate chemical representation and mining for nonlinear substructures. We conclude that the combination of elaborate chemical representation and Gaston provides an excellent method for 2D substructure mining as this recipe systematically explores all substructures in different levels of chemical detail.

Databases as Topic↗