Search

The primary goal of ligand-based virtual screening is to identify active compounds consisting of a core scaffold that is not found in the current active compound pool. Scaffold hopping is the term used for this purpose. In the present study, topological representations of pharmacophore features on chemical graphs were investigated for scaffold hopping. Pharmacophore graphs (PhGs), which consist of pharmacophore features as nodes and their topological distances as edges, were used as a representation of important information on compounds being active. We investigated ranking methods for prioritizing PhGs for scaffold hopping. The proposed method, NScaffold, which ranks PhGs based on the number of scaffolds covered by the PhGs, outperforms other conventional methods. As a demonstrative case, using a thrombin inhibitor data set, we interpreted the highest-ranked PhGs by NScaffold from the protein–ligand interaction point of view. It resulted that the NScaffold method successfully retrieved three known important interactions, showing the potential for identifying scaffold-hopped compounds with interpretable PhGs.

Soft sensors have been widely employed in industrial processes to estimate process variables that are difficult to measure in real time. Taking into account process dynamics in soft sensor models is important not only for high predictability, but also for the interpretation of the models. One simple way of introducing dynamics is to use process variables X with time-delays. In this study, we propose a strategy of building soft sensor models for selecting appropriate variables X with time-delays, by incorporating ensemble learning into genetic algorithm-based process variables and dynamics selection (GAVDS). The construction of soft sensor models using simulation data sets and publicly available process operation data sets showed that the ensemble GAVDS method was superior to GAVDS in terms of model predictability. Furthermore, it appeared to select reasonable time delay regions for process variables X.

In ligand-based drug design, quantitative structure–activity relationship (QSAR) models play an important role in activity prediction. One of the major end points of QSAR models is half-maximal inhibitory concentration (IC50). Experimental IC50 data from various research groups have been accumulated in publicly accessible databases, providing an opportunity for us to use such data in predictive QSAR models. In this study, we focused on using a ranking-oriented QSAR model as a predictive model because relative potency strength within the same assay is solid information that is not based on any mechanical assumptions. We conducted rigorous validation using the ChEMBL database and previously reported data sets. Ranking support vector machine (ranking-SVM) models trained on compounds from similar assays were as good as support vector regression (SVR) with the Tanimoto kernel trained on compounds from all the assays. As effective ways of data integration, for ranking-SVM, integrated compounds should be selected from only similar assays in terms of compounds. For SVR with the Tanimoto kernel, entire compounds from different assays can be incorporated.

Activity cliffs (ACs) are formed by pairs of structurally similar compounds with large differences in potency. Predicting ACs is of high interest in lead optimization for drug discovery. Previous AC prediction models that focused on matched molecular pair (MMP) cliffs produced adequate performances. However, the extrapolation ability of these models is unclear because the main scaffold for MMPs, the core structure, could exist in both training and test data sets. Also, representation of MMPs did not consider the attachment points where the core and R-group substituents are connected. In this study, we aimed to improve a ligand-based AC prediction method using molecular fingerprints. We incorporated applicability domain, which was defined using R-path fingerprints to consider the local environment around an attachment point. Rigorous evaluation of the extrapolation ability of AC prediction models showed that MMP-cliffs were accurately predicted for nine biological targets. Furthermore, incorporation of training MMPs with cores distinct from those of test MMPs improved the predictability compared with using training MMPs with only similar cores.