Herman van Vlijmen

Uncertainty quantification (UQ) has been recognized as a prerequisite for reliable and trustworthy computational modeling in drug discovery. Two widely considered paradigms, Bayesian methods (deep ensemble and MC dropout) and evidential learning, differ in their computational demands and expressivity of uncertainties, excelling in complementary settings. Here, we propose hybrid approaches that combine both paradigms and benchmark them on the Papyrus++ data set across two end points (xC₅₀, K_x) and multiple split strategies. Our ensemble of evidential models (EOE) consistently achieves the best overall performance, yielding the lowest RMSE and leading CRPS and interval scores, including under the most challenging distributional shifts. While large ensembles often excel in rejection-based utility, EOE matches or surpasses them at a fraction of the computational cost. Statistical tests confirm its advantage, and a hardware-agnostic compute analysis highlights favorable performance-efficiency trade-offs. These results demonstrate that combining evidential and Bayesian principles yields more accurate and informative uncertainties for bioactivity modeling, with EOE offering a robust─and computationally practical─default for uncertainty-aware decision-making in drug discovery.

Autosomal dominant polycystic kidney disease (ADPKD) is the most common hereditary kidney disease. Its progressively expanding, fluid‐filled renal cysts eventually lead to end‐stage renal disease. Despite the relatively high prevalence, treatment options are currently limited to a single drug approved by the FDA and EMA. Here, we investigated human ADPKD patient‐derived three‐dimensional cyst cultures (3DCC) as an in vitro model for ADPKD and drug repurposing research. First, we analyzed the proteomes of 3DCC derived from healthy and diseased tissues. We then compared the protein expression profiles with those of reference tissues, mainly from the same patients. We quantified 290 proteins affecting drug disposition and proposed target proteins for drug treatment. Lastly, we investigated the functional response of the quantified target proteins after exposure to repurposing candidates in the 3DCC. Proteomic profiling of human 3DCC reflected previously reported pathophysiological alterations, including aberrant protein expression in inflammation and metabolic reprogramming. While the 3DCCs largely recapitulated the disease phenotype in vitro, drug transporter expression was reduced compared to in vivo conditions. Target proteins for proposed repurposing candidates showed similar expression in vitro and in tissues. Exposure to these repurposing candidates inhibited cyst swelling in vitro, supporting the suitability of the 3DCC for ADPKD drug screening. In summary, our results provide new insights into the ADPKD proteome and offer a starting point for further research to improve treatment options for affected individuals.

Respiratory syncytial virus (RSV) remains a public health burden due to unmet therapeutic needs. We recently reported the discovery of a non-nucleoside inhibitor of the RSV polymerase and characterized its binding to a novel pocket within the capping domain of the polymerase. Here, we describe our strategy to diversify the chemical matter targeting this site by screening our DNA-encoded chemical libraries, leading to the discovery of a novel and potent series of molecules that inhibits RSV polymerase's biochemical activity, as well as its viral replication in cells. Structural analysis via cryo-EM revealed novel contacts made within the capping domain binding pocket. By leveraging these structural insights for preliminary SAR exploration, we generated analogues for which potency and metabolic stability were improved more than 60- and 40-fold, respectively, over the initial hit. This work provides a path forward for further advanced SAR exploration and development of therapeutics against RSV.

Despite the availability of medicines preventing respiratory syncytial virus (RSV) infection, post-exposure treatment options are needed for addressing patient's needs. RSV non-nucleoside polymerase inhibitors (NNI) have emerged as a promising asset for which our group previously disclosed JNJ-8003 with potent in vitro antiviral activity and pronounced in vivo efficacy. In this work, a structural-guided design to modify the linker vector of JNJ-8003 resulted in the identification of 2-oxacyclo pyridine-containing derivatives whose various ring closing strategies are described. In addition, bioisosteric replacement of an amide bond with triazole retained potency, and cryo-electron microscopy (cryo-EM) confirmed binding in the capping domain. Subsequent NMR conformational analysis suggested a correlation between the potency and conformations. Our efforts have fulfilled the aim of identifying linker modifications with maintained biological activity while enriching structural diversity and allowing modulations of other parameters.

Membrane proteins have diverse functions within cells and are well-established drug targets. The advances in membrane protein structural biology have revealed drug and lipid binding sites on membrane proteins, while computational methods such as molecular simulations can resolve the thermodynamic basis of these interactions. Particularly, alchemical free energy calculations have shown promise in the calculation of reliable and reproducible binding free energies of protein-ligand and protein-lipid complexes in membrane-associated systems. In this review, we present an overview of representative alchemical free energy studies on G-protein-coupled receptors, ion channels, transporters as well as protein-lipid interactions, with emphasis on best practices and critical aspects of running these simulations. Additionally, we analyze challenges and successes when running alchemical free energy calculations on membrane-associated proteins. Finally, we highlight the value of alchemical free energy calculations calculations in drug discovery and their applicability in the pharmaceutical industry.

Evaluation of kinetic parameters of drug-target binding, k_on, k_off, and residence time (RT), in addition to the traditional in vitro parameter of affinity is receiving increasing attention in the early stages of drug discovery. Target binding kinetics emerges as a meaningful concept for the evaluation of a ligand’s duration of action and more generally drug efficacy and safety. We report the biological evaluation of a novel series of spirobenzo-oxazinepiperidinone derivatives as inhibitors of the human equilibrative nucleoside transporter 1 (hENT1, SLC29A1). The compounds were evaluated in radioligand binding experiments, i.e., displacement, competition association, and washout assays, to evaluate their affinity and binding kinetic parameters. We also linked these pharmacological parameters to the compounds’ chemical characteristics, and learned that separate moieties of the molecules governed target affinity and binding kinetics. Among the 29 compounds tested, 28 stood out with high affinity and a long residence time of 87 min. These findings reveal the importance of supplementing affinity data with binding kinetics at transport proteins such as hENT1.

The notion that data should be Findable, Accessible, Interoperable and Reusable, according to the FAIR Principles, has become a global norm for good data stewardship and a prerequisite for reproducibility. Nowadays, FAIR guides data policy actions and professional practices in the public and private sectors. Despite such global endorsements, however, the FAIR Principles are aspirational, remaining elusive at best, and intimidating at worst. To address the lack of practical guidance, and help with capability gaps, we developed the FAIR Cookbook, an open, online resource of hands-on recipes for “FAIR doers” in the Life Sciences. Created by researchers and data managers professionals in academia, (bio)pharmaceutical companies and information service industries, the FAIR Cookbook covers the key steps in a FAIRification journey, the levels and indicators of FAIRness, the maturity model, the technologies, the tools and the standards available, as well as the skills required, and the challenges to achieve and improve data FAIRness. Part of the ELIXIR ecosystem, and recommended by funders, the FAIR Cookbook is open to contributions of new recipes.

G Protein-coupled receptors (GPCRs) are the most frequently exploited drug target family, moreover they are often found mutated in cancer. Here we used a dataset of mutations found in patient samples derived from the Genomic Data Commons and compared it to the natural human variance as exemplified by data from the 1000 genomes project. We explored cancer-related mutation patterns in all GPCR classes combined and individually. While the location of the mutations across the protein domains did not differ significantly in the two datasets, a mutation enrichment in cancer patients was observed among class-specific conserved motifs in GPCRs such as the Class A “DRY” motif. A Two-Entropy Analysis confirmed the correlation between residue conservation and cancer-related mutation frequency. We subsequently created a ranking of high scoring GPCRs, using a multi-objective approach (Pareto Front Ranking). Our approach was confirmed by re-discovery of established cancer targets such as the LPA and mGlu receptor families, but also discovered novel GPCRs which had not been linked to cancer before such as the P2Y Receptor 10 (P2RY10). Overall, this study presents a list of GPCRs that are amenable to experimental follow up to elucidate their role in cancer.