The lead optimization process in drug discovery campaigns is an arduous endeavour where the input of many medicinal chemists is weighed in order to reach a desired molecular property profile. Building the expertise to successfully drive such projects collaboratively is a very time-consuming process that typically spans many years within a chemist’s career. In this work we aim to replicate this process by applying artificial intelligence learning-to-rank techniques on feedback that was obtained from 35 chemists at Novartis over the course of several months. We exemplify the usefulness of the learned proxies in routine tasks such as compound prioritization, motif rationalization, and biased de novo drug design. Annotated response data is provided, and developed models and code made available through a permissive open-source license.

In visual crowding, the perception of a target deteriorates in the presence of nearby flankers. Traditionally, target-flanker interactions have been considered as local, mostly deleterious, low-level, and feature specific, occurring when information is pooled along the visual processing hierarchy. Recently, a vast literature of high-level effects in crowding (grouping effects and face-holistic crowding in particular) led to a different understanding of crowding, as a global, complex, and multilevel phenomenon that cannot be captured or explained by simple pooling models. It was recently argued that these high-level effects may still be captured by more sophisticated pooling models, such as the Texture Tiling model (TTM). Unlike simple pooling models, the high-dimensional pooling stage of the TTM preserves rich information about a crowded stimulus and, in principle, this information may be sufficient to drive high-level and global aspects of crowding. In addition, it was proposed that grouping effects in crowding may be explained by post-perceptual target cueing. Here, we extensively tested the predictions of the TTM on the results of six different studies that highlighted high-level effects in crowding. Our results show that the TTM cannot explain any of these high-level effects, and that the behavior of the model is equivalent to a simple pooling model. In addition, we show that grouping effects in crowding cannot be predicted by post-perceptual factors, such as target cueing. Taken together, these results reinforce once more the idea that complex target-flanker interactions determine crowding and that crowding occurs at multiple levels of the visual hierarchy.

In crowding, perception of a target deteriorates in the presence of nearby flankers. Surprisingly, perception can be rescued from crowding if additional flankers are added (uncrowding). Uncrowding is a major challenge for all classic models of crowding and vision in general, because the global configuration of the entire stimulus is crucial. However, it is unclear which characteristics of the configuration impact (un)crowding. Here, we systematically dissected flanker configurations and showed that (un)crowding cannot be easily explained by the effects of the sub-parts or low-level features of the stimulus configuration. Our modeling results suggest that (un)crowding requires global processing. These results are well in line with previous studies showing the importance of global aspects in crowding.

It has been claimed that video gamers possess increased perceptual and cognitive skills compared to non-video gamers. Here, we examined to which extent gaming performance in CS:GO (Counter-Strike: Global Offensive) correlates with visual performance. We tested 94 players ranging from beginners to experts with a battery of visual paradigms, such as visual acuity and contrast detection. In addition, we assessed performance in specific gaming skills, such as shooting and tracking, and administered personality traits. All measures together explained about 70% of the variance of the players’ rank. In particular, regression models showed that a few visual abilities, such as visual acuity in the periphery and the susceptibility to the Honeycomb illusion, were strongly associated with the players’ rank. Although the causality of the effect remains unknown, our results show that high-rank players perform better in certain visual skills compared to low-rank players.

Scikit-Mol is a open-source toolkit that aims to bridge the gap between two well-established toolkits, RDKit and Scikit-Learn, in order to provide a simple interface for building cheminformatics models. By leveraging the strengths of both RDKit and Scikit-Learn, Scikit-Mol provides a powerful platform for creating predictive modeling in drug discovery and materials design. Unlike other toolkits that often integrate both chemistry and machine learning, Scikit-Mol rather aims to be a simple bridge between the two, reducing the maintenance effort required to keep up with changes and new features in e.g. Scikit-Learn. A simple example of Scikit-Mol's functionality is provided, demonstrating its compatibility with Scikit-Learn pipelines. Overall, Scikit-Mol provides a useful and flexible package for building self-contained and self-documented cheminformatics models with minimal maintenance required.