Matija Marijan

: Accurate identification of drug–target interactions is consequential for novel drug discovery and development. Deep learning methods for drug–target affinity (DTA) prediction have shown great promise in accelerating drug discovery and reducing development costs. Although graph neural networks have improved drug representation learning for DTA prediction tasks, many models still struggle to effectively and efficiently capture protein information, limiting overall prediction accuracy. In this work, we systematically evaluate the impact of pre-trained protein language models (PLMs) on the downstream task of predicting binding affinity between drugs and target proteins. We design multiple experiments across four different molecular representation backbones and assess the effect of incorporating PLM embeddings, comparing their performance to classical 1D convolution methods. We evaluate four families of PLMs which we integrate into PLM-GraphDTA, each built on distinct architectures and optimized for different tasks, including structure prediction, function prediction, and sequence unmasking. Additionally, we evaluate DeepGraphDTA, an architectural modification of the baseline convolution method designed to improve protein representation learning. The models are evaluated on two benchmark datasets, Davis and KIBA, using concordance index (CI) and mean squared error (MSE) as performance metrics. We further evaluate the generalization power of each model using cold-start train and test splits, and analyze the per-protein contribution to total CI. The results indicate simple architectural modifications to traditional convolution methods may be sufficient to bridge the gap to large pre-trained PLMs.

Unmanned aerial vehicles open new possibilities for developing technologies that support more sustainable and efficient agriculture. This paper presents a non-invasive method for repelling rodents from crop fields using ultrasound. The proposed system is implemented as a spherical-cap ultrasound loudspeaker array consisting of eight transducers, mounted on a drone that overflies the field while emitting sound in the 20–70 kHz range. The hardware design includes both the loudspeaker array and a custom printed circuit board hosting power amplifiers and a signal generator tailored to drive multiple ultrasonic transducers. In parallel, a genetic algorithm is used to compute flight paths that maximize coverage and increase the probability of driving rodents away from the protected area. As part of the validation phase, artificial intelligence models for rodent detection using a thermal camera are developed to provide quantitative feedback on system performance. The complete prototype is evaluated through a series of experiments conducted both in controlled laboratory conditions and in the field. Field trials highlight which parts of the concept are already effective and identify open challenges that need to be addressed in future work to move from a research prototype toward a deployable product.