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Hans Briem

Publications and source records attributed to Hans Briem.

5 recordsLinked to original sources
PubMed2007

Macrocyclic aminopyrimidines as multitarget CDK and VEGF-R inhibitors with potent antiproliferative activities.

X-ray structures from CDK2-aminopyrimidine inhibitor complexes led to the idea to stabilize the active conformation of aminopyrimidine inhibitors by incorporating the recognition site into a macrocyclic framework. A modular synthesis approach that relies on a new late-stage macrocyclization protocol that enables fast and efficient synthesis of macrocyclic aminopyrimidines was developed. A set of structurally diverse derivatives was prepared. Macrocyclic aminopyrimidines were shown to be multitarget inhibitors of CDK1/2 and VEGF-RTKs. In addition, potent antiproliferative activities toward various human tumor cells and a human tumor xenograft model were demonstrated.

Amines↗
PubMed2005

Classifying "kinase inhibitor-likeness" by using machine-learning methods.

By using an in-house data set of small-molecule structures, encoded by Ghose-Crippen parameters, several machine learning techniques were applied to distinguish between kinase inhibitors and other molecules with no reported activity on any protein kinase. All four approaches pursued--support-vector machines (SVM), artificial neural networks (ANN), k nearest neighbor classification with GA-optimized feature selection (GA/kNN), and recursive partitioning (RP)--proved capable of providing a reasonable discrimination. Nevertheless, substantial differences in performance among the methods were observed. For all techniques tested, the use of a consensus vote of the 13 different models derived improved the quality of the predictions in terms of accuracy, precision, recall, and F1 value. Support-vector machines, followed by the GA/kNN combination, outperformed the other techniques when comparing the average of individual models. By using the respective majority votes, the prediction of neural networks yielded the highest F1 value, followed by SVMs.

Algorithms↗
PubMed2002

Flexsim-R: a virtual affinity fingerprint descriptor to calculate similarities of functional groups.

Methods to describe the similarity of fragments occurring in drug-like molecules are of fundamental importance in computational drug design. In the early phase of lead discovery, they can help to select diverse building blocks for combinatorial compound libraries intended for broad screening. In lead optimization, such methods can guide bioisosteric replacements of one functional group by another or serve as descriptors for QSAR calculations. In this paper, we outline the development of a novel 3D descriptor, termed Flexsim-R, which is a further extension of our virtual affinity fingerprint idea. Descriptors are calculated based on docking of small fragments such as building blocks for combinatorial chemistry or functional groups of drug-like molecules into a reference panel of protein binding sites. The method is validated by examining the neighborhood behavior of the affinity fingerprints and by deriving predictive QSAR models for a couple of literature peptide data sets.

Algorithms↗
PubMedDate not supplied

Mining high-throughput screening data of combinatorial libraries: development of a filter to distinguish hits from nonhits.

Kohonen neural networks generate projections of large data sets defined in high-dimensional space. The resulting self-organizing maps can be used in many applications in the drug discovery process, such as to analyze combinatorial libraries for their similarity or diversity and to select descriptors for structure-activity relationships. The ability to investigate thousands of compounds in parallel also allows one to conduct a study based on single-dose experiments of high-throughput screening campaigns, which are known to have a greater uncertainty than IC50 or Ki values. This is demonstrated here for a data set of 5513 compounds from one combinatorial library. Furthermore, a method was developed that uses self-organizing maps not only as an indicator of structure-activity relationships, but as the basis of a classification system allowing predictive modeling of combinatorial libraries.

Journal Article↗