Using four years of data from a nationally representative consumer survey, we examined trends in telehealth usage over time and the role state telehealth policies play in telehealth use. Telehealth use increased dramatically during the period 2013-16, with new modalities such as live video, live chat, texting, and mobile apps gaining traction. The rate of live video communication rose from 6.6 percent in June 2013 to 21.6 percent in December 2016. However, underserved populations-including Medicaid, low-income, and rural populations-did not use live video communication as widely as other groups did. Less restrictive state telehealth policies were not associated with increased usage overall or among underserved populations. This study suggests that state efforts alone to remove barriers to using telehealth might not be sufficient for increasing use, and new incentives for providers and consumers to adopt and use telehealth may be needed.

Activity landscapes are defined by potency and similarity distributions of active compounds and reflect the nature of structure-activity relationships (SARs). Three-dimensional (3D) activity landscapes are reminiscent of topographical maps and particularly intuitive representations of compound similarity and potency distributions. From their topologies, SAR characteristics can be deduced. Accordingly, idealized theoretical landscape models have been utilized to rationalize SAR features, but "true" 3D activity landscapes have not yet been described in detail. Herein we present a computational approach to derive approximate 3D activity landscapes for actual compound data sets and to analyze exemplary landscape representations. These activity landscapes are generated within a consistent reference frame so that they can be compared across different activity classes. We show that SAR features of compound data sets can be derived from the topology of landscape models. A notable correlation is observed between global SAR phenotypes, assigned on the basis of SAR discontinuity scoring, and characteristic landscape topologies. We also show that different molecular representations can substantially alter the topology of activity landscapes for a given data set and modulate the formation of activity cliffs, which represent the most prominent landscape features. Depending on the choice of molecular representations, compounds forming a steep activity cliff in a given landscape might be separated in another and no longer form a cliff. However, comparison of alternative activity landscapes makes it possible to focus on compound subsets having high SAR information content.

Malaria is a disease affecting hundreds of millions of people across the world, mainly in developing countries and especially in sub-Saharan Africa. It is the cause of hundreds of thousands of deaths each year and there is an ever-present need to identify and develop effective new therapies to tackle the disease and overcome increasing drug resistance. Here, we extend a previous study in which a number of partners collaborated to develop a consensus in silico model that can be used to identify novel molecules that may have antimalarial properties. The performance of machine learning methods generally improves with the number of data points available for training. One practical challenge in building large training sets is that the data are often proprietary and cannot be straightforwardly integrated. Here, this was addressed by sharing QSAR models, each built on a private data set. We describe the development of an open-source software platform for creating such models, a comprehensive evaluation of methods to create a single consensus model and a web platform called MAIP available at https://www.ebi.ac.uk/chembl/maip/. MAIP is freely available for the wider community to make large-scale predictions of potential malaria inhibiting compounds. This project also highlights some of the practical challenges in reproducing published computational methods and the opportunities that open-source software can offer to the community.

Preclinical Research Herein we discuss the concept of matched molecular pairs (MMPs) and relevant medicinal chemistry applications. In recent years, this concept has become popular in medicinal chemistry as it provides a formal and general basis to establish structural relationships between compounds, identify chemical changes that transform structurally related compounds into each other, and facilitate a large‐scale analysis of structure–activity relationships. Algorithms have been developed for the systematic computational identification of qualifying pairs in large compound databases. In addition to a number of other areas such as graphical analysis and visualization of structure–activity relationships, systematic MMP analysis has significantly advanced the field of computational medicinal chemistry, providing a basis for many practical applications. Characteristic features of this concept include that it eliminates subjective criteria from structural comparisons, alleviates the need to employ predefined molecular hierarchies in studying structural relationships, and elucidates distinguishing structural features between active compounds in a highly intuitive manner. Hence, the results of computational MMP analysis are readily accessible to medicinal chemists, which makes this type of analysis highly attractive for a variety of applications.

Activity landscapes provide a comprehensive description of structure-activity relationships (SARs). An information theoretic assessment of their features, namely, activity cliffs, similarity cliffs, smooth-SAR, and featureless regions, is presented based on the probability of occurrence of these features. It is shown that activity cliffs provide highly informative SARs compared to smooth-SAR regions, although the latter are the basis for most QSAR studies. This follows since small structural changes in the former are coupled with relatively large changes in activity, thus pinpointing specific structural features associated with the changes in activity. In contrast, Smooth-SAR regions are typically associated with relatively small changes in both structure and activity. Surprisingly, similarity cliffs, which occur when both compounds in a compound-pair have approximately equal activities but significantly different structures, are the most prevalent feature of activity landscapes. Hence, from an information theoretic point of view, they are the least informative landscape feature. Nevertheless, similarity cliffs do provide SAR information on potentially new active compound classes, and in that sense they are quite useful in drug discovery programs since they provide alternative possibilities should ADMET or other issues arise during the discovery and earlier preclinical development phases of drug research.