The majority of compounds in the drug-like chemical space show flexibility regarding their three-dimensional structure which, in solution, results in an equilibrium of multiple interconvertible conformational states. Knowledge of the putative bound-state conformation of a molecule is an essential prerequisite for the successful application of many computer-aided drug design methods that aim to assess or predict its capability to bind to a particular target receptor of interest. An established approach to predict bioactive conformers in the absence of receptor structure information is to sample the low-energy conformational space of the investigated molecules and derive representative conformer ensembles which can then be expected to comprise members that closely resemble possible bound-state ligand conformations. The high relevance of such conformer generation functionality led to the development of a wide panel of dedicated commercial and open-source software tools throughout the last decades. Several published benchmarking studies have shown that open-source tools lag behind their commercial competitors in many key aspects like accuracy in reproducing bioactive conformations, speed of processing, output ensemble size, range of applicability, stability and user-friendliness. In this work, we introduce the novel open-source conformer ensemble generator CONFORT, which builds upon proven concepts and algorithms and aims at delivering state-of-the-art performance for all types of organic molecules in the drug-like chemical space. The ability of CONFORT, and several well-known commercial and open-source conformer ensemble generators, to reproduce experimental 3D structures as well as their computational efficiency and robustness has been assessed thoroughly both for typical drug-like molecules and macrocyclic structures. For small molecules, CONFORT outperforms all other tested open-source conformer generators and is head-to-head with commercial generators, both in terms of processing speed and accuracy in the reproduction of bioactive conformations. In the case of macrocyclic structures, CONFORT is able to reproduce experimental 3D structures with clearly higher accuracy than all other tested generators. To our knowledge, CONFORT is the first open-source conformer ensemble generator that is able to truly keep up with commercial software in this field and thus represents a valuable addition to the open-source software toolbox for computer-aided drug design.

Chemical features of small molecules can be abstracted to 3D pharmacophore models, which are easy to generate, interpret, and adapt by medicinal chemists. Three-dimensional pharmacophores can be used to efficiently match and align molecules according to their chemical feature pattern, which facilitates the virtual screening of even large compound databases. Existing alignment methods, used in computational drug discovery and bio-activity prediction, are often not suitable for finding matches between pharmacophores accurately as they purely aim to minimize RMSD or maximize volume overlap, when the actual goal is to match as many features as possible within the positional tolerances of the pharmacophore features. As a consequence, the obtained alignment results are often suboptimal in terms of the number of geometrically matched feature pairs, which increases the false-negative rate, thus negatively affecting the outcome of virtual screening experiments. We addressed this issue by introducing a new alignment algorithm, Greedy 3-Point Search (G3PS), which aims at finding optimal alignments by using a matching-feature-pair maximizing search strategy while at the same time being faster than competing methods.

Dissemination of novel research methods, especially in the form of chemoinformatics software, depends heavily on their ease of applicability for nonexpert users with only a little or no programming skills and knowledge in computer science. Visual programming has become widely popular over the last few years, also enabling researchers without in-depth programming skills to develop tailored data processing pipelines using elements from a repository of predefined standard procedures. In this work, we present the development of a set of nodes for the KNIME platform implementing the QPhAR algorithm. We show how the developed KNIME nodes can be included in a typical workflow for biological activity prediction. Furthermore, we present bestpractice guidelines that should be followed to obtain high-quality QPhAR models. Finally, we show a typical workflow to train and optimise a QPhAR model in KNIME for a set of given input compounds, applying the discussed best practices.