Co-crystallization is an accessible way to control physicochemical characteristics of organic crystals, which finds many biomedical applications. In this work, we present Generative Method for Co-crystal Design (GEMCODE), a novel pipeline for automated co-crystal screening based on the hybridization of deep generative models and evolutionary optimization for broader exploration of the target chemical space. GEMCODE enables fast *de novo* co-crystal design with target tabletability profiles, which is crucial for the development of pharmaceuticals. With a series of experimental studies highlighting validation and discovery cases, we show that GEMCODE is effective even under realistic computational constraints. Furthermore, we explore the potential of language models in generating co-crystals. Finally, we present numerous previously unknown co-crystals predicted by GEMCODE and discuss its potential in accelerating drug development.

In autonomous driving, object detection and semantic segmentation are critical tasks for path planning and control of an autonomous vehicle. Recent approaches are based on supervised learning methods, with large datasets sampled in the target domain. However, annotating training data for supervised learning methods is a high resource and time-consuming task. In this work, we propose to exploit artificial LiDAR data for object detection and semantic segmentation. We use the CARLA simulator [1] to generate artificial data of autonomous driving scenarios and propose ways to mitigate the differences between artificial and real-world data (domain generalization). We modeled both the noise and the missed reflections (denoted point dropout) that occur in real-world data collection, and show their effects in the detection and segmentation tasks. We assess the potential benefits of using pre-trained models on artificial data when fine-tuning with all, or a fraction, of the available real-world data (domain adaptation). We find clear improvements when using artificial data to pretrain a network, which allows to use a reduced amount of realworld data, and boost the performance of the trained models.