Improving structural similarity based virtual screening using background knowledge.

Background: Virtual screening in the form of similarity rankings is often applied in the early drug discovery process to rank and prioritize compounds from a database. This similarity ranking can be achieved with structural similarity measures. However, their general nature can lead to insufficient performance in some application cases.

Similarity boosted quantitative structure-activity relationship--a systematic study of enhancing structural descriptors by molecular similarity.

The concept of molecular similarity is one of the most central in the fields of predictive toxicology and quantitative structure-activity relationship (QSAR) research. Many toxicological responses result from a multimechanistic process and, consequently, structural diversity among the active compounds is likely. Combining this knowledge, we introduce similarity boosted QSAR modeling, where we calculate molecular descriptors using similarities with respect to representative reference compounds to aid a statistical learning algorithm in distinguishing between different structural classes.

Using Local Models to Improve (Q)SAR Predictivity.

We present a novel (Q)SAR approach that detects groups of structures for local (Q)SAR modeling. The algorithm combines clustering and classification or regression for making predictions on chemical structure data. A clustering procedure producing clusters with shared structural scaffolds is applied as a preprocessing step, before a (local) model is learned for each relevant cluster.

Collaborative development of predictive toxicology applications.

OpenTox provides an interoperable, standards-based Framework for the support of predictive toxicology data management, algorithms, modelling, validation and reporting. It is relevant to satisfying the chemical safety assessment requirements of the REACH legislation as it supports access to experimental data, (Quantitative) Structure-Activity Relationship models, and toxicological information through an integrating platform that adheres to regulatory requirements and OECD validation principles. Initial research defined the essential components of the Framework including the approach to data access, schema and management, use of controlled vocabularies and ontologies, architecture, web service and communications protocols, and selection and integration of algorithms for predictive modelling.

Protein solubility: sequence based prediction and experimental verification.

Motivation: Obtaining soluble proteins in sufficient concentrations is a recurring limiting factor in various experimental studies. Solubility is an individual trait of proteins which, under a given set of experimental conditions, is determined by their amino acid sequence. Accurate theoretical prediction of solubility from sequence is instrumental for setting priorities on targets in large-scale proteomics projects.