Helicases are essential motor enzymes that are becoming established targets in oncology and infectious diseases. The PIF1 DNA helicase has functions in genome stability and there is strong evidence for a relation between elevated human PIF1 expression and poor outcomes in cancer patients. We will present the results of our campaign towards the identification of PIF1 inhibitors, including an X-ray crystallographic fragment screen, analogues synthesis and structure-based virtual screening of Edelris biorelevant Keymical Space^TM, comprising innovative synthetic NP-like, sp³-rich topologiesⁱ. Two different scaffolds were identified and confirmed in vitro for their hPIF1 helicase inhibition. Our results show a strong potential as starting point for novel hPIF1 inhibitors.^ii,iii

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ⁱ Nelson, A.; Roche, D. Innovative approaches to the design and synthesis of small molecule libraries. Bioorg. Med. Chem.2015, 23, 2613. 10.1016/j.bmc.2015.02.046
ⁱⁱ Wever, M.J.A. & al. Structure-based discovery of first inhibitors targeting the helicase activity of human PIF1, Nucleic Acids Res, 2024, 12616-12632. 10.1093/nar/gkae897
ⁱⁱⁱ Wever, M.J.A. & al. Identification of hPIF1 helicase inhibitors by virtual screening of a Fsp3-enriched library, Bioorg. Med. Chem. Lett. 2025, 130314. 10.1016/j.bmcl.2025.130314

The clinical success of covalent KRAS^G12C inhibition prompts further expansion of the concept to target non-cysteine oncogenic mutation sites as in KRAS^G12D. This endeavor was hampered by the lack of suitable electrophiles for the selective, covalent engagement of aspartate. Thanks to the recent discovery of β-lactone-bearing covalent inhibitors, new opportunities are emerging. Based on X-ray crystallographic insights and quantum chemical calculations, this presentation will describe the elucidation of structure-activity and -stability correlations to advance such electrophiles for drug discovery. Guided by predictions of transition state barrier heights for the attack of aspartate 12 at the β-lactone electrophile and structure-based design, substituted β-lactones were generated, aiming to balance specific reactivity and chemical and metabolic stability. The optimization strategy is driven by MS-based and cellular covalent target occupancy assays and PD marker analysis, proteome-wide profiling, and synthetic chemistry. With this work, the goal is to expand the use of β-lactones as chemoselective electrophiles in medicinal chemistry.

The MET kinase is a clinically validated oncogenic driver, yet the emergence of clinical resistance to ATP-competitive inhibitors necessitates the exploration of novel mechanisms of inhibition. This presentation details a structure-based drug design campaign aimed at identifying and optimizing allosteric inhibitors capable of targeting both wild-type MET and its resistant mutants. Beginning with a novel sulfonamide hit from a DNA-encoded library technology (DELT) screen, our hit-to-lead optimization was principally driven by iterative cycles of molecular modeling and X-ray crystallography. This strategy enabled the rational design of macrocyclic analogues, successfully targeting the type III allosteric pocket. The resulting macrocycles demonstrate significant gains in potency while maintaining favorable molecular properties, representing promising starting points for next-generation MET inhibitors.

Targeted protein degradation has emerged in recent years as a new modality to control protein levels in vivo. Among the many competing degradation technologies, most research to date has focused on heterobifunctional protein degraders (such as PROTACs), although inherent concerns about the molecular properties of these “large small molecules” has sparked intense interest in the development of smaller degraders, such as molecular glues. As a rule, molecular glues are smaller and are thus more attractive as potential therapeutics – but because a single molecule is responsible for simultaneously binding to two disparate proteins, the rational discovery of molecular glues to date has proven difficult. In this work, we will discuss multiple computational techniques implemented in MOE for modeling ternary complexes containing molecular glues. Special focus will be paid to lessons learned from our well-established PROTAC modeling toolset. In particular, it will be shown that the most effective molecular glue models result from treating molecular glues as “linkerless PROTACs.” Finally, recent work in developing techniques to prioritize prospective molecular glue designs based on their predicted degradation efficacy will be presented.

Analysis is an essential component of the DMTA cycle. Indeed, Structure Activity Relationships analysis (SAR) is a key source of inspiration for compound design. However, SAR analysis is a very time-consuming process, impeding the frequency of regular updates. Moreover, the evolving context demands the consideration and analysis of an increasing array of parameters to design effective and sustainable plant protection products. To address these challenges, Bayer initiated a collaboration with Discngine to develop a new tool to accelerate SAR analysis and reporting across multiple endpoints. The ultimate goal is to support, guide and improve decision making in projects both in terms of quality and efficiency. The tool contains several modules enabling the creation of SAR overviews in a fully streamlined manner and facilitate in-depth analysis of Matched Molecular Pairs (MMP) and R-Group deconvolution reports. We will present the rational to engage into the development of this tool, provide an overview highlighting its capabilities and its potential to significantly facilitate SAR analysis and impact on compound design. Lastly, we will discuss our extended collaboration with Discngine and the productization of one of our SAR reporting module, now available for commercial use as a Discngine solution, ‘SAR Slides’.

The proinflammatory cytokine IL-17 is important for host defense but is also a key driver in various inflammatory and autoimmune diseases. While clinical anti-IL-17 antibodies such as secukinumab (Cosentyx®) have demonstrated efficacy in conditions like psoriasis, psoriatic arthritis, and ankylosing spondylitis, the pursuit of orally bioavailable small molecule alternatives has been hampered by safety and other development issues. To overcome these challenges, we adopted a strategy aimed at expanding the structural diversity of small molecule IL-17 inhibitors, with the goal of achieving a more favorable safety and pharmacokinetic profile. For idea prioritization, we harnessed diverse in silico tools, including quantum chemistry-based property and conformational predictions, binding free energy calculations, and ensemble molecular docking, alongside comprehensive structural analysis of the IL-17A homodimer and hypothesis-driven idea generation. This rational approach led to the identification of several novel chemical series that serve as promising starting points for further optimization, with some compounds already demonstrating encouraging in vivo properties and efficacy.

Deep learning has driven major breakthroughs in protein structure prediction, however the next critical advance is accurately predicting how proteins interact with small molecule ligands, to enable real-world applications such as drug discovery. Recent cofolding methods aim to address this challenge, but evaluating their performance has been inconclusive due to the lack of relevant benchmarking datasets. Here we present a comprehensive evaluation of four leading all-atom cofolding methods using our newly introduced benchmark dataset Runs N’ Poses, which comprises 2,600 high-resolution protein-ligand systems released after the training cutoff used by these methods. We demonstrate that current cofolding approaches largely memorise ligand poses from their training data, hindering their use for de novo drug design. This highlights that data scarcity and the need for improved architectures are major bottlenecks in the field.

Ring systems form the structural backbone of most bioactive molecules. They define molecular shape, position substituents in three-dimensional space, influence global physico-chemical properties, and frequently contribute directly to biological activity. This presentation will explore ring systems in bioactive molecules from a cheminformatics perspective. The most prevalent ring types found in bioactive compounds will be identified and compared with those commonly used in synthetic molecules. An intuitive method for visualizing the chemical space of bioactive rings will be introduced. In addition, trends in the medicinal chemistry literature, highlighting ring systems that are gaining or losing popularity in drug discovery will be explained. Finally, information on how these insights can be applied in the rational design of innovative, high-value bioactive molecules will be discussed.