Predicting the outcome of biological assays based on high-throughput imaging data is a highly promising task in drug discovery since it can tremendously increase hit rates and suggest novel chemical scaffolds. However, end-to-end learning with convolutional neural networks (CNNs) has not been assessed for the task biological assay prediction despite the success of these networks at visual recognition. We compared several CNNs trained directly on high-throughput imaging data to a) CNNs trained on cell-centric crops and to b) the current state-of-the-art: fully connected networks trained on precalculated morphological cell features. The comparison was performed on the Cell Painting data set, the largest publicly available data set of microscopic images of cells with approximately 30,000 compound treatments. We found that CNNs perform significantly better at predicting the outcome of assays than fully connected networks operating on precomputed morphological features of cells. Surprisingly, the best performing method could predict 32% of the 209 biological assays at high predictive performance (AUC > 0.9) indicating that the cell morphology changes contain a large amount of information about compound activities. Our results suggest that many biological assays could be replaced by high-throughput imaging together with convolutional neural networks and that the costly cell segmentation and feature extraction step can be replaced by convolutional neural networks.

Due to the current severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic, there is an urgent need for novel therapies and drugs. We conducted a large-scale virtual screening for small molecules that are potential CoV-2 inhibitors. To this end, we utilized "ChemAI," a deep neural network trained on more than 220M data points across 3.6M molecules from three public drug-discovery databases. With ChemAI, we screened and ranked one billion molecules from the ZINC database for favourable effects against CoV-2. We then reduced the result to the 30,000 top-ranked compounds, which are readily accessible and purchasable via the ZINC database. Additionally, we screened the DrugBank using ChemAI to allow for drug repurposing, which would be a fast way towards a therapy. We provide these top-ranked compounds of ZINC and Drug-Bank as a library for further screening with bioassays at https://github.com/ml-jku/ sars-cov-inhibitors-chemai.

Recent advances in artificial intelligence, particularly in the field of deep learning, have enabled researchers to create compelling algorithms for medical image analysis. Histological slides of basal cell carcinomas (BCCs), the most frequent skin tumor, are accessed by pathologists on a daily basis and are therefore well suited for automated pre-screening by neural networks for the identification of cancerous regions and swift tumor classification.In this proof-of-concept study, we implemented an accurate and intuitively interpretable artificial neural network (ANN) for the detection of BCCs in histological whole slide images. Furthermore, we identified and compared differences in the diagnostic histological features and recognition patterns relevant for machine learning algorithms versus expert pathologists.An attention-ANN was trained with whole slide images of BCCs to identify tumor regions (n=820). The diagnosis-relevant regions used by the ANN were compared to regions of interest for pathologists, detected by eye-tracking techniques.This ANN accurately identified BCC tumor regions on images of histologic slides (AUC: 0.993, 95%

Currently, bioimaging databases cannot be queried by chemical structures that induce the phenotypic effects captured by the image. We present a novel retrieval system based on contrastive learning that is able to identify the chemical structure inducing the phenotype out of ∼2,000 candidates with a top-1 accuracy >70 times higher than a random baseline.

Contrastive learning with the InfoNCE objective is exceptionally successful in various self-supervised learning tasks. Recently, the CLIP model yielded impressive results on zero-shot transfer learning when using InfoNCE for learning visual representations from natural language supervision. However, InfoNCE as a lower bound on the mutual information has been shown to perform poorly for high mutual information. In contrast, the InfoLOOB upper bound (leave one out bound) works well for high mutual information but suffers from large variance and instabilities. We introduce "Contrastive Leave One Out Boost" (CLOOB), where modern Hopfield networks boost learning with the InfoLOOB objective. Modern Hopfield networks replace the original embeddings by retrieved embeddings in the InfoLOOB objective. The retrieved embeddings give InfoLOOB two assets. Firstly, the retrieved embeddings stabilize InfoLOOB, since they are less noisy and more similar to one another than the original embeddings. Secondly, they are enriched by correlations, since the covariance structure of embeddings is reinforced through retrievals. We compare CLOOB to CLIP after learning on the Conceptual Captions and the YFCC dataset with respect to their zero-shot transfer learning performance on other datasets. CLOOB consistently outperforms CLIP at zero-shot transfer learning across all considered architectures and datasets.