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Antiviral drug discovery has traditionally targeted viral proteins, while host-directed strategies remain under-explored. We present a systematic drug repurposing strategy that uses morphological profiling to identify host-targeting antivirals. Using cell painting, we demonstrate that SARS-CoV-2 infection can be accurately determined from the morphological profile of virus-infected cells. Moreover, morphological features reveal how host cells respond to viral exposure, offering insights into antiviral activity, host-cell health, and putative mechanisms of action of the compounds. Screening 5,275 repurposable compounds, we identified candidates that reversed the infected phenotype, including ones not detected by conventional cytopathicity and antibody-based assays. After deprioritization of confounding phospholipidosis, we present 74 hit candidates, including unreported compounds targeting host processes implicated in viral infection. This adaptable and scalable platform is suited for diverse viruses and cell systems. We provide a resource of open-access screening data, images, and analysis pipelines to advance antiviral discovery and pandemic preparedness.

AI agents are emerging as transformative tools in drug discovery, with the ability to autonomously rea act and learn through complicated research w flows. Building on large language models and spe ized tools, these systems can integrate biomedical data, execute tasks, conduct experiments and itera-tively refine hypotheses. We provide a concep overview of agentic AI architectures and illustrate their applications across key stages of drug discov including literature synthesis, automated prot generation, toxicity prediction, small-molecule s thesis, drug repurposing and end-to-end decisi making. Early implementations demonstrate subs tial gains in speed, reproducibility and scalabil son, orkcialtual ery, ocol ynontanity. We discuss the challenges related to data heterogene outline future directions toward technology in su ity, system reliability, privacy, benchmarking and pport of science and translation.

Ligand-based drug design (LBDD) has long driven therapeutic innovation; however, its traditional potency-centered paradigm often oversimplifies biological complexity. Advances in artificial intelligence (AI) now enable multiobjective strategies that integrate polypharmacology, safety and environmental sustainability. Despite this progress, a unified framework that systematically incorporates these dimensions within AI-augmented LBDD is lacking. Here, we propose that embedding benign-by-design principles into multiobjective optimization can enable the proactive mitigation of toxicity, off-target effects and ecological impact from the early design and discovery stages. This shift redefines LBDD as a complexity-aware and ethically grounded discipline capable of delivering safer and more sustainable therapeutics. Accordingly, the primary objective of this review is to outline a unifying framework for AI-enabled LBDD that moves beyond potency-centered optimization by integrating efficacy, safety, sustainability and societal impact.

Persistent organic pollutants often co-occur in human exposure environments, yet their combined effects on disease risk remain poorly understood. This study examined associations between serum concentrations of 14 per- and polyfluorinated substances (PFAS) and three hydroxylated polychlorinated biphenyls (OH-PCBs) and the onset of multiple sclerosis (MS), utilizing data from the Swedish population-based Epidemiological Investigation of Multiple Sclerosis (EIMS) cohort, comprising 907 MS cases and 907 matched controls. We employed single-substance logistic regression and quantile g-computation to evaluate cumulative and individual compound associations. We considered linear and non-linear risk patterns while adjusting for lifestyle factors and MS-associated HLA alleles.

Our analysis revealed non-linear associations for several individual compounds, particularly perfluorooctane sulfonic acid (PFOS), perfluorononanoic acid, 2,2′,3,4′,5,5′,6-heptachloro-4-biphenylol (4-OH-CB187), and 2,2′,4,4′,5,5′-, Hexachloro-3-biphenylol (3-OH-CB153), with increased odds of MS. Interaction analyses further indicated that the association between PFOS and MS odds was modified by the presence of the HLA-B*44:02 allele, known for its protective effect on MS risk. Mixture modeling highlighted that combined exposures to PFAS and OH-PCBs significantly increased MS odds, even when associations for individual compounds were weak or absent.

These findings emphasize the complexity of associations between environmental contaminants, lifestyle and genetic risk factors, and the odds of MS. They underscore the importance of addressing co-exposure in environmental health research and call for further studies to elucidate underlying biological mechanisms.

Accurately predicting the activity of a chemical in each bioactivity assay based on its already known properties is extremely useful in drug development. Unfortunately, we discovered that many assays in widely used assay-activity benchmark datasets directly relate to cell health and cytotoxicity. Many other assays intend to capture a more specific phenotype, but their active compounds impact cell count, while inactives do not. In both cases, counting cells achieves unexpectedly high performance in these benchmarks, making them less useful for discerning whether additional properties, such as phenotypic profiles (mRNA or Cell Painting), provide additional useful information on bioactivity. To accomplish this goal, we recommend filtering benchmarks to exclude such assays and including a cell-count baseline. Using a benchmark with 24 protein-target assays, we confirm that models leveraging Cell Painting image-based profiles outperformed the baseline cell count model. We propose several other practical recommendations for benchmarking machine learning models for predicting bioactivity and assessing the added value of mRNA, protein, or image-based profiles.

Morphological profiling is a common approach to investigate the modes of action (MOAs) of compounds. Most methods rely on fixed-cell assays, which provide only a single snapshot at a predefined time point and overlook the dynamic nature of cellular responses. In contrast, live-cell imaging tracks responses over time, offering deeper insight into compound-specific effects and mechanisms; however, time-series analysis of image data remains challenging due to limited analytical tools. We present Live Cell Temporal Profiling (LCTP), a workflow for morphological profiling of label-free livecell time series data that yields interpretable, biologically relevant results. We showcase LCTP in an MOA classification study using label-free data. The workflow integrates established deep-learning components, cell segmentation, live/dead classification, and single-cell feature extraction, with data-driven models to capture MOA-specific temporal phenotypes and produce time-resolved profiles that can be compared across compounds and cell lines. We assess MOA classification performance using double-blinded cross-validation simulating a real-world screening scenario. LCTP significantly improves MOA classification over single-time point analysis, consistently across both cell lines used in the study. Time-resolved phenotypic modelling reveals transient, sustained, and delayed responses, clarifying compound-specific temporal effects and mechanisms across MOAs. The presented workflow is modular: each step removes irrelevant information, enriching signal, and enabling straightforward updates as technologies evolve and as new technologies become available, while supporting reuse across studies broadly. We believe LCTP adds substantial value to high-throughput compound screening, showing that live-cell imaging combined with this workflow yields informative visualizations of temporal effects and improved MOA classification.

Machine learning (ML) is increasingly used to interrogate biological systems whose complexity resists law-like, deductive explanation. As a result, embeddings, clusters, and attributions are often overinterpreted, dependencies are left implicit, and claims about explainability are often insufficiently bounded. In this work, we present a framework for contextualizing how machine learning contributes to scientific understanding in biology via compression, qualitative intelligibility, and dependency models. Compression is achieved when inductive biases encode biological structure, reducing the effective hypothesis space and yielding representations aligned with known biology. Qualitative intelligibility is supported when high-dimensional measurements are mapped to human-graspable objects, such as embeddings, clusters, and trajectories, that enable accurate qualitative reasoning without exact calculation. Dependency modelling is realized when learned models make explicit the pattern of relations among system components and thereby guide prediction and intervention. We examine how these principles manifest in successful ML applications and discuss considerations that emerge from this framework. Overall, when viewed through these lenses, ML can transform predictive success into intervention-guiding knowledge in the life sciences.

Exposure to per- and polyfluorinated substances (PFAS) and hydroxylated polychlorinated biphenyls (OH-PCBs) is associated with adverse human health effects, including immunosuppression. It is unknown if these substances can affect the course of autoimmune diseases. This study was based on 907 individuals with multiple sclerosis (MS) and 907 matched controls, where the MS cases were followed longitudinally using the Swedish MS register. We demonstrate sex- and disease-specific differences in serum PFAS concentrations between individuals with MS and controls. Moreover, two OH-PCBs (4-OH-CB187 and 3-OH-CB153) are associated with an increased risk of developing multiple sclerosis, regardless of sex and immigration status. With a clinical follow-up time of up to 18 years, an increase in serum concentrations of perfluorooctanoic acid (PFOA), perfluorooctane sulfonic acid (PFOS), and perfluorodecanoic acid (PFDA) decreases the risk of confirmed disability worsening in both sexes, as well as perfluoroheptanesulfonic acid (PFHpS) and perfluorononanoic acid (PFNA), only in males with MS. These results show previously unknown associations between OH-PCBs and the risk of developing MS, as well as the inverse associations between PFAS exposure and the risk of disability worsening in MS.

Modern quantitative image analysis techniques have enabled high-throughput, high-content imaging experiments. Image-based profiling leverages the rich information in images to identify similarities or differences among biological samples, rather than measuring a few features, as in high-content screening. Here, we review a decade of advancements and applications of Cell Painting, a microscopy-based cell-labeling assay aiming to capture a cell's state, introduced in 2013 to optimize and standardize image-based profiling. Cell Painting's ability to capture cellular responses to various perturbations has expanded owing to improvements in the protocol, adaptations for different perturbations, and enhanced methodologies for feature extraction, quality control, and batch-effect correction. Cell Painting is a versatile tool that has been used in various applications, alone or with other -omics data, to decipher the mechanism of action of a compound, its toxicity profile, and other biological effects. Future advances will likely involve computational and experimental techniques, new publicly available datasets, and integration with other high-content data types.

Repurposing of existing drugs for new indications has attracted substantial attention owing to its potential to accelerate drug development and reduce costs. Hundreds of computational resources such as databases and predictive platforms have been developed that can be applied for drug repurposing, making it challenging to select the right resource for a specific drug repurposing project. With the aim of helping to address this challenge, here we overview computational approaches to drug repurposing based on a comprehensive survey of available in silico resources using a purpose-built drug repurposing ontology that classifies the resources into hierarchical categories and provides application-specific information. We also present an expert evaluation of selected resources and three drug repurposing case studies implemented within the Horizon Europe REMEDi4ALL project to demonstrate the practical use of the resources. This comprehensive Review with expert evaluations and case studies provides guidelines and recommendations on the best use of various in silico resources for drug repurposing and establishes a basis for a sustainable and extendable drug repurposing web catalogue.